Board of Governors of the Federal Reserve System

Abstract:

This paper revisits the conditions under which search models generate balanced growth paths (BGPs)—equilibria where unemployment, vacancies, and job flows remain steady as search frictions decline. Martellini and Menzio (2020) claim that such paths exist only when matches are “inspection goods” and match quality follows a Pareto distribution. We show that these conditions are sufficient but not necessary. Their implementation assumes a strong form of stationarity—requiring the endogenous distribution of match qualities to remain invariant under proportional scaling. This restriction forces the reservation quality to grow at a constant, strictly positive rate, mechanically tying declining frictions to long-term growth and yielding counterfactual implications of eliminating search frictions—persistent unemployment and infinite welfare gains. Relaxing this restriction, balanced growth can arise under alternative forms of scaling, such as additive transformations that restore stationarity without Pareto tails or inspection. We further show that biased technological progress, when vacancies and unemployed workers are complementary inputs, also generates well-behaved BGPs with finite welfare gains and vanishing unemployment as search frictions disappear.

Keywords: search frictions; balanced growth; inspection models; Pareto tails; biased technological change

DOI: https://doi.org/10.17016/FEDS.2025.098

Abstract:

The question of whether affluent households save at a higher rate than other parts of the distribution has been asked by economists on numerous occasions since the 1950s. It is standard in this research to define affluent, or “rich,” households as those with high lifetime earnings or income to better ground the empirical question in relevant theory. However, results in the literature are mixed regarding whether rich households in fact save more than others, with some studies suggesting a generally flat saving-rate profile across the distribution and others supporting the notion that the rich do indeed save more. Many empirical papers do not include direct measures of lifetime earnings, relying instead on proxies. Additionally, few include the full range of assets that low- and middle-income households depend on to finance their retirement, and even fewer use data that include sufficient samples of households that are in the extreme upper tails of the wealth or income distribution. The primary contribution of this paper is to combine all three in an examination of U.S. households. We use the 2022 Survey of Consumer Finances (SCF), which oversamples high-net-worth households, in combination with direct estimation of lifetime earnings, to explore wealth-to-lifetime-earnings ratios—the cumulative impact of saving over time—across the lifetime earnings distribution. In addition, we use an expanded measure of wealth that includes the asset value of defined benefit pensions and Social Security, the public pension program. We find a steep gradient of saving when defining rich households by their lifetime earnings, which crucially includes business income in household earnings. The steepness, though, does not manifest until the top deciles of lifetime earnings. Recent research draws attention to the outsized contribution of capital gains in driving wealth accumulation of the rich; when we remove unrealized capital gains from our metrics, however, the gradient of the wealth–lifetime-earnings ratio is reduced but not removed.

Keywords: distribution, lifetime earnings, pension, savings, social security, wealth

DOI: https://doi.org/10.17016/FEDS.2025.097

Abstract:

We use consumer panel scanner data to examine households' payment choices, a new application of such data. In particular, we study the long-term shift towards payment cards, as well as the role of transaction size in determining choices. We find that idiosyncratic household preferences are a key driver of payment choice. Our estimates suggest that transaction size, while important, may have a smaller effect on payment choice than previously thought, and that the effect varies substantially across households. Our results further suggest that idiosyncratic household preferences evolve slowly over time, explaining only a third of the increase in card use over the seven-year period in our data. Taken together, our findings have potential policy implications not just for the adoption of new methods such as instant payments, but also around potential costs to households from sun-setting older payment methods such as checks.

Keywords: Credit Cards, Heterogeneity, Households, Panel Data, Payment Choice

DOI: https://doi.org/10.17016/FEDS.2025.096

Abstract:

We use the longitudinal component of the Current Population Survey to compare transition rates into and out of disability and employment prior to and after the onset of the pandemic. We find that one-third of the increased employment rate among disabled people is due to the excess incidence of disability seen following the pandemic, while the other two-thirds is attributable to higher participation among people whose disabilities were unrelated to the pandemic. Further, we find evidence that these increases are concentrated in occupations with higher rates of telework.

DOI: https://doi.org/10.17016/FEDS.2025.095

Abstract:

Housing is the largest asset in U.S. household portfolios, and first-time homebuyers increasingly rely on parental transfers. This paper quantifies the contribution of parental transfers to the homeownership rate of young households. I build and estimate a life-cycle overlapping generations model with housing, where adult children and parents interact without commitment. I find that parental transfers account for 13 percentage points (27%) of young households' homeownership. Transfers from wealthy parents not only help households overcome borrowing constraints, but also help sustain homeownership, mitigating the drawbacks of illiquidity. Surprisingly, policies lowering entry barriers to homeownership generally increase the reliance on parental wealth, whereas increased liquidity reduces it. Finally, I show that children of wealthy parents strategically use the illiquidity of housing as a commitment device to encourage transfers, resulting in a preference for illiquidity.

Keywords: Homeownership, Parental transfers, Altruism, Life-cycle models

DOI: https://doi.org/10.17016/FEDS.2025.094

Abstract:

This paper analyzes the risks posed by future-state quantum computers, specifically the “harvest now decrypt later” (HNDL) risk. We review foundational concepts of quantum computing to address the present and ongoing threat of HNDL to currently protected data. We use the Bitcoin network as an illustrative example to study the implications of HNDL for distributed ledger cryptocurrency networks that rely upon traditional cryptography. We posit that while cryptocurrency distributed ledger network maintainers could successfully deploy post-quantum cryptography (PQC) mitigations to protect the network’s security and data integrity against a future-state quantum computer, data privacy of the network’s previously recorded transactions remains vulnerable against a future-state quantum computer due to HNDL. The difficulty in protecting data privacy lies in the risk that a bad actor can obtain a distributed ledger replica, harvest the data, and in the fullness of time reveal previously obfuscated and confidential data using a sufficiently powerful quantum computer. The authors highlight this gap in data privacy protection and note the shortage of mitigations for the data privacy risks associated with the HNDL threat within distributed ledger networks.

Keywords: payment networks, distributed ledger, technological innovation, quantum, peer-to-peer payments, data privacy, Bitcoin

DOI: https://doi.org/10.17016/FEDS.2025.093

Abstract:

We examined the performance of four families of large language models (LLMs) and a variety of common fuzzy matching algorithms in assessing the similarity of names and addresses in a sanctions screening context. On average, across a range of realistic matching thresholds, the LLMs in our study reduced sanctions screening false positives by 92 percent and increased detection rates by 11 percent relative to the best-performing fuzzy matching baseline. Smaller, less computationally intensive models from the same language model families performed comparably, which may support scaling. In terms of computing performance, the LLMs were, on average, over four orders of magnitude slower than the fuzzy methods. To help address this, we propose a model cascade that escalates higher uncertainty screening cases to LLMs, while relying on fuzzy and exact matching for easier cases. The cascade is nearly twice as fast and just as accurate as the pure LLM system. We show even stronger runtime gains and comparable screening accuracy by relying on the fastest language models within the cascade. In the near term, the economic cost of running LLMs, inference latency, and other frictions, including API limits, will likely necessitate using these types of tiered approaches for sanctions screening in high-velocity and high-throughput financial activities, such as payments. Sanctions screening in slower-moving processes, such as customer due diligence for account opening and lending, may be able to rely on LLMs more extensively.

Keywords: Large Language Models, Sanctions Screening, Model Cascading

DOI: https://doi.org/10.17016/FEDS.2025.092

Abstract:

This paper introduces parallel trends forest, a novel approach to constructing optimal control samples when using difference-in-differences (DiD) in a relatively long panel data with little randomization in treatment assignment. Our method uses machine learning techniques to construct an optimal control sample that best meet the parallel trends assumption. We demonstrate that our approach outperforms existing methods, particularly with noisy, granular data. Applying the parallel trends forest to analyze the impact of post-trade transparency in corporate bond markets, we find that it produces more robust estimates compared to traditional two-way fixed effects models. Our results suggest that the effect of transparency on bond turnover is small and not statistically significant when allowing for constrained deviations from parallel trends. This method offers researchers a powerful tool for conducting more reliable DiD analyses in complex, real-world settings.

Keywords: causal inference, difference-in-differences, parallel trends assumption, random forest

DOI: https://doi.org/10.17016/FEDS.2025.091

Abstract:

This paper investigates the impact of the adoption of generative AI on financial stability. We conduct laboratory-style experiments using large language models to replicate classic studies on herd behavior in investment decisions. Our results show that AI agents make more rational decisions than humans, relying predominantly on private information over market trends. Increased reliance on AI-powered investment advice could therefore potentially lead to fewer asset price bubbles arising from animal spirits that trade by following the herd. However, exploring variations in the experimental settings reveals that AI agents can be induced to herd optimally when explicitly guided to make profit-maximizing decisions. While optimal herding improves market discipline, this behavior still carries potential implications for financial stability. In other experimental variations, we show that AI agents are not purely algorithmic, but have inherited some elements of human conditioning and bias.

Keywords: Herd behavior, large language models, AI-powered traders, financial markets, financial stability

DOI: https://doi.org/10.17016/FEDS.2025.090

Abstract:

This paper investigates whether the “virtue of complexity” (VoC), documented in equity return prediction, extends to exchange rate forecasting. Using nonlinear Ridge regressions with Random Fourier Features (Ridge–RFF), we compare the predictive performance of complex models against linear regression and the robust random walk benchmark. Forecasts are constructed across three sets of economic fundamentals—traditional monetary, expanded monetary and non-monetary, and Taylor-rule predictors—with nominal complexity varied through rolling training windows of 12, 60, and 120 months. Our results offer a cautionary perspective. Complexity delivers only modest, localized gains: in very small samples with rich predictor sets, Ridge–RFF can outperform linear regression. Yet these improvements never translate into systematic gains over the random walk. As training windows expand, Ridge–RFF quickly loses ground, while linear regression increasingly dominates, at times even surpassing the random walk under expanded fundamentals. Market-timing analyses reinforce these findings: complexity-based strategies yield occasional short-sample gains but are unstable and prone to sharp drawdowns, whereas simpler linear and random walk strategies provide more robust and consistent economic value. By incorporating formal forecast evaluation tests—including Clark–West and Diebold–Mariano—we show that apparent gains from complexity are fragile and rarely statistically significant. Overall, our evidence points to a limited virtue of complexity in FX forecasting: complexity may help under narrowly defined conditions, but parsimony and the random walk benchmark remain more reliable across samples, predictor sets, and economic evaluations.

Keywords: Foreign exchange rate, Exchange rate disconnect puzzle, predictability, complexity, machine learning, Ridge, RFF

DOI: https://doi.org/10.17016/FEDS.2025.089

Abstract:

In the United States, credit card companies frequently use machine learning algorithms to proactively raise credit limits for borrowers. In contrast, an increasing number of countries have begun to prohibit credit limit increases initiated by banks rather than consumers. In this paper, we exploit detailed regulatory micro data to examine the extent to which bank-initiated credit limit increases are directed towards individuals with revolving debt. We then develop a model that captures the costs and benefits of regulating proactive credit limit increases, which we use to quantify their importance and evaluate the implications for household well-being.

Keywords: Algorithmic lending, Behavioral Finance, Consumer protection, Credit Cards, Credit limit increases, Financial regulation

DOI: https://doi.org/10.17016/FEDS.2025.088

Abstract:

A challenge for conducting monetary policy in a currency union is the diverse economic conditions among member states. Such disparities can drive natural interest rates apart, thereby undermining the stabilizing role of a unified monetary policy. To assess the stance of monetary policy across Eurozone-19 countries, we estimate their natural rates of interest (r∗) and inflation trends (π∗) to construct a measure of the country-level neutral nominal interest rates (r∗ + π∗) over 1999-2025, using a semistructural model that jointly characterizes the trend and cyclical components of key macroeconomic variables such as output, unemployment, inflation, 10-year government bond yields, and the common policy interest rate. Our setup improves upon those in the existing literature by allowing both a short-run interest rate gap—driven by the (shadow) policy rate—and a long-run interest rate gap—driven by the country-specific 10-year government bond yields—to affect and reflect economic conditions. We also impose cointegration between the dynamics of the country-specific latent variables and common counterparts to incorporate co-movements across the euro area economies. Our results show that the stance of monetary policy is homogeneous across the countries in our sample, but that a relatively highly degree of heterogeneity emerges at key historical turning points.

Keywords: Common monetary policy challenges, Euro area economies, Interest rate gap, Neutral interest rate, Sovereign debt risk.

DOI: https://doi.org/10.17016/FEDS.2025.087

Abstract:

This study examines changes in household and employment access to bank branches in the United States from 2014 to 2024, calculating distances with highly granular census block-level data. We develop a continuous measure of bank branch access that accounts for population and employment density, implicitly accounting for varying travel times within different urban and rural areas. Our findings indicate that despite a 19-percent decline in bank branches over the decade, average distances to the nearest branch increased only modestly—by 0.02 to 0.28 miles depending on area density. We find some disparities in branch access across racial and income groups, but these gaps did not widen substantially over the 2014-2024 period. Overall, our results suggest that while some localized reductions in branch access occurred, the significant reduction in the number of branches did not result in significant decreases in access to local bank branches for households or businesses.

Keywords: Banking, Branch Networks, Geospatial Analysis, Banking Deserts

DOI: https://doi.org/10.17016/FEDS.2025.086

Abstract:

This paper shows that adverse macroeconomic shocks systematically increase inflation for low-income households relative to high-income households. I document two key facts: (i) during every U.S. recession since 1959, aggregate spending shifts toward products disproportionately purchased by low-income households (necessities); and (ii) relative prices of necessities rise during recessions. These patterns can be explained by a model with non-homothetic demand and a concave production possibility frontier: shocks that reduce expenditure induce households to reallocate spending from luxuries to necessities, raising their relative prices. I empirically show that this mechanism operates for both demand and supply shocks, using monetary policy and oil price news shocks. Incorporating this mechanism into a quantitative model reproduces most of the variation in necessity prices and shares from 1961 to 2024. The model shows that the fall in expenditure due to a recessionary shock similar to the Great Recession leads inflation to increase by more than 1.5 percentage points for low-income households relative to high-income households. The results suggest that low-income households are hit twice by adverse shocks: once by the shock itself and again as their price index increases relative to that of other households.

Keywords: inflation, non-homotheticity, real income inequality, business cycle

DOI: https://doi.org/10.17016/FEDS.2025.085

Abstract:

When do households listen to the Fed? We show the answer lies in a simple but powerful force: household attention to macroeconomic conditions. We develop a model where attention acts as a crucial gatekeeper for the pass-through of policy news to beliefs, and confirm its predictions using household survey data. We find that belief revisions to monetary policy surprises are concentrated among attentive individuals—particularly those with high financial stakes—and this effect strengthens dramatically during uncertain times. This implies the expectations channel is most potent when it matters most, suggesting policymakers should account for the time-varying and heterogeneous nature of public attention.

Keywords: Inflation expectations,Monetary policy, Rational inattention, Behavioral macroeconomics

DOI: https://doi.org/10.17016/FEDS.2025.084

Abstract:

This paper shows that U.S. commercial banks' funding betas rise predictably with the length, magnitude, and direction of each monetary policy cycle: longer cycles and those with larger changes in the policy rate yield stronger pass-through in both tightening and loosening cycles, with modest asymmetry favoring slightly greater transmission during loosening cycles. Nondeposit liabilities consistently adjust more than deposits. Crucially, at the aggregate banking-system level and across banks grouped by size, this cycle-dependent relationship has remained remarkably stable over three decades, highlighting the durability and predictability of interest-rate transmission to banks' funding costs.

Keywords: Bank funding betas; Deposit vs. nondeposit funding costs; Monetary policy cycles; Interest-rate transmission

DOI: https://doi.org/10.17016/FEDS.2025.083

Abstract:

Financial stress can impact trading behavior in the U.S. commodity futures markets. To clarify the impact, we study absolute changes and relative exposure dynamics in traders' positions during two recent crises: the 2008 Global Financial Crisis (GFC) and the COVID-19 pandemic. The nature of these two crises are very distinct, and we find that traders behaved quite differently. The commodity market collapse during the 2008 GFC followed the classic pattern of a speculative bubble; speculators, including financial institutions and money managers, rushed to close their long positions in commodity futures while commodity producers or hedgers actively facilitated these trades. Consequently, the risk in commodity futures markets flowed from speculators back to producers. In sharp contrast, no evidence is found to support this type of risk flow during the COVID-19 crisis. Stress in the financial system was relatively mild compared with the 2008 GFC, and the commodity market experienced a strong rally early in the crisis. Both speculators and hedgers traded in an orderly fashion. In terms of traders' relative exposures, we find that the impact from financial stress was immaterial. We also find that speculators generally reacted to changing financial conditions more strongly than hedgers, during the period.

Keywords: Commodities, Futures, Financial Stability, Market Volatility, COVID-19, 2008 GFC

DOI: https://doi.org/10.17016/FEDS.2025.082r1

Abstract:

This paper uses a large-scale redrawing of flood zone maps for the City of New Orleans in 2016 to identify how banks respond to changes in perceived flood risk in residential mortgage origination. Using geo-coding, we separate loan-level data on mortgage originations into treatment versus control groups based on how individual properties were affected by the map changes. We find banks charged interest rates that were roughly 6 basis points higher for mortgages on treated properties that were removed from the special floods zones as a result of the map changes. In addition, lower loan-to-value ratios for mortgages on these properties suggest that banks also required higher downpayments. Both effects are temporary, lasting under two years. Further analysis using flood insurance claims data following a major flooding event in 2017 suggests the temporary nature of these effects may reflect learning by banks about the true extent of flood risk and insurance take-up following the map changes.

Keywords: FEMA Maps, Flood insurance, Mortgage lending

DOI: https://doi.org/10.17016/FEDS.2025.081

Abstract:

We study how corporate debt influences the competitive outcomes of horizontal and conglomerate mergers. In contrast to standard models where debt does not affect pricing, our framework shows that mergers can spread fixed debt obligations across a broader product portfolio, creating an "insurance effect" against adverse demand shocks. This effect interacts with the traditional recapture effect from reduced competition. Using numerical simulations and a case study of a major casino merger, we find that debt can either dampen or amplify post-merger price increases, depending on the merger's structure and the market environment.

Keywords: financial structure; merger simulation; horizontal markets

DOI: https://doi.org/10.17016/FEDS.2025.080

Abstract:

Asymmetries play an important role in many macroeconomic models. We show that assumptions on household and firm expectations play a key role in determining the effects of these asymmetries on macroeconomic outcomes. If households and firms have perfect foresight and hence do not account for the possibility of future shocks, then the implied longer-run averages and distributions for unemployment and inflation can differ significantly from their rational expectations counterparts. We first derive this result analytically under either an asymmetric monetary policy rule or a nonlinear Phillips curve before numerically examining some of the key nonlinearities featured in the recent literature.

Keywords: Macroeconomic Asymmetries, Business Cycles, Expectations

DOI: https://doi.org/10.17016/FEDS.2025.079

Abstract:

Did bank equity prices reflect growing sector imbalances before the 2023 failure of Silicon Valley Bank? We find that banks with higher reliance on uninsured deposits, or with higher marked-to-market leverage, had lower equity returns prior to SVB's collapse. Although markets priced uninsured deposits and high leverage individually, their interaction was not reflected in market prices prior to SVB’s failure. Post-SVB, banks with less ability to meet outflows without severely depleting capital, and banks with too little useable liquidity relative to runnable funding, experienced larger stock price declines, beyond what other fundamentals and business model risks explain. In addition, we highlight evidence of feedback between equity prices and balance sheet management: banks with lower returns in 2023:Q1 were more likely to rely heavily on reciprocal deposits by 2023:Q2.

Keywords: Financial Institutions, Bank Capital, Interest Rate Risk, Liquidity

DOI: https://doi.org/10.17016/FEDS.2025.078

Abstract:

This paper proposes an approach to enhance the Federal Reserve's readiness to undertake market-functioning asset purchases during Treasury market disruptions. It notes that by tilting the SOMA Treasury portfolio toward bills rather than maintaining a maturity structure proportionate to that of outstanding Treasury debt—often viewed as the most neutral portfolio—the Fed can create a larger volume of reinvestments each month that can serve as a “war chest” for undertaking market-functioning asset purchases. This structure of the SOMA Treasury portfolio enables market-functioning asset purchases to be made without expanding the balance sheet or increasing reserves. This avoids the need for close monitoring of reserves when asset purchases are eventually unwound, while also allowing for a clearer differentiation between asset purchases undertaken to support market functioning and asset purchases undertaken to ease financial conditions. Under reasonable assumptions, bills portfolio shares ranging up to around 40 percent—that is, twice that of the 20 percent proportionate portfolio— could be desirable. We also consider approaches for restoring the SOMA Treasury portfolio and, thereby war chest, back to its pre-stress composition. We find that, if the full monthly war chest is depleted to undertake market functioning purchases, restoring it to its pre-stress composition by allowing purchased coupon securities to mature and reinvesting these proceeds into bills, would take 2-1/2 to 5-1/2 years. These lengthy timeframes would limit for many years the Federal Reserve’s ability to respond to Treasury market disruptions without expanding its balance sheet.

DOI: https://doi.org/10.17016/FEDS.2025.077

Abstract:

We propose a novel estimation framework for option pricing models that incorporates local, state-dependent information to improve out-of-sample forecasting performance. Rather than modifying the underlying option pricing model, such as the Heston-Nandi GARCH or the Heston stochastic volatility framework, we introduce a local M-estimation approach that conditions on key state variables including VIX, realized volatility, and time. Our method reweights historical observations based on their relevance to current market conditions, using kernel functions with bandwidths selected via a validation procedure. This adaptive estimation improves the model’s responsiveness to evolving dynamics while maintaining tractability. Empirically, we show that local estimators substantially outperform traditional non-local approaches in forecasting near-term option implied volatilities. The improvements are particularly pronounced in low-volatility environments and across the cross-section of options. The local estimators also outperform the non-local estimators in explaining future option returns. Our findings suggest that local information, when properly incorporated into the estimation process, can enhance the accuracy and robustness of option pricing models.

Keywords: implied volatility forecasting, local maximum likelihood, model misspecification, option pricing

DOI: https://doi.org/10.17016/FEDS.2025.076

Abstract:

In heavy-tailed cases, variance targeting the Student's-t estimator proposed in Bollerslev (1987) for the linear GARCH model is shown to be robust to density misspecification, just like the popular Quasi-Maximum Likelihood Estimator (QMLE). The resulting Variance-Targeted, Non-Gaussian, Quasi-Maximum Likelihood Estimator (VTNGQMLE) is shown to possess a stable limit, albeit one that is highly non-Gaussian, with an ill-defined variance. The rate of convergence to this non-standard limit is slow relative √n and dependent upon unknown parameters. Fortunately, the sub-sample bootstrap is applicable, given a carefully constructed normalization. Surprisingly, both Monte Carlo experiments and empirical applications reveal VTNGQMLE to sizably outperform QMLE and other performance-enhancing (relative to QMLE) alternatives. In an empirical application, VTNGQMLE is applied to VIX (option-implied volatility of the S&P 500 Index). The resulting GARCH variance estimates are then used to forecast option-implied volatility of volatility (VVIX), thus demonstrating a link between historical volatility of VIX and risk-neutral volatility-of-volatility.

Keywords: GARCH, VIX, VVIX, heavy tails, robust estimation, variance forecasting, volatility, volatility-of-volatility

DOI: https://doi.org/10.17016/FEDS.2025.075

Abstract:

We review the design and communication of monetary policy strategies that take into account risks and uncertainty. A key element in a robust monetary strategy is the concept of risk management, which is the weighing of key risks when setting policy. When risks to the outlook are balanced, the baseline outlook may be sufficient to guide policy decisions. However, risk-management considerations become important when risks are asymmetric. We discuss how robust simple interest rate rules and optimal control policy can incorporate risk-management considerations into the design of a monetary policy strategy. Alternative scenarios can illustrate salient risks and how monetary policy might respond if those risks were to materialize. However, using alternative scenarios in policy deliberations and communications requires important implementation choices.

Keywords: Uncertainty, risk management, robust monetary policy strategies, scenario analysis, monetary policy communication

DOI: https://doi.org/10.17016/FEDS.2025.074

Abstract:

This paper discusses the measurement, assessment, and communication of risks and uncertainty that are relevant for monetary policy. It provides a taxonomy of policy-relevant uncertainty related to the state and the structure of the economy, and the formation of expectations. A wide range of tools is available to assess and quantify uncertainty and the balance of risks. Qualitative assessments of uncertainty—in policy statements, minutes, and speeches—are the main tools to communicate uncertainty and the balance of risks across major central banks. However, the use of quantitative tools for such communications—including scenario analysis—is evolving, and so far no clear consensus has emerged for best practices.

Keywords: Macroeconomic uncertainty, monetary policy, central bank communication

DOI: https://doi.org/10.17016/FEDS.2025.073

Abstract:

This paper considers robust monetary policy strategies both in situations of low demand and low inflation and when economic developments pose a tradeoff between inflation and output stabilization. We proceed in two parts. First, our quantitative analysis suggests that asymmetric average inflation targeting can provide modest benefits over other inflation-targeting strategies when the risks associated with the effective lower bound remain significant. Second, motivated by the recent experience of persistent supply shocks and rapid increases in inflation, we describe the main qualitative features of optimal policy in circumstances when the objectives of stabilizing inflation and economic activity conflict. We find that monetary policy may allow inflation to depart from the target in response to certain supply shocks or in cases when sectoral dynamics are relevant, but that it should be ready to respond forcefully and expeditiously to large inflationary shocks or if inflation expectations are at risk of becoming unanchored.

Keywords: Alternative monetary policy strategies, monetary policy communication, effective lower bound, supply shocks, sectoral dynamics, inflation surges

DOI: https://doi.org/10.17016/FEDS.2025.072

Abstract:

This paper examines the drivers of the 2020–23 inflation surge, with an emphasis on the similarities and differences across countries, as well as the role that monetary policy frameworks might have played in shaping central banks’ responses. The inflation surge in the U.S. and abroad was set in motion by two global events: the COVID-19 pandemic and Russia’s invasion of Ukraine. Pandemic-related supply disruptions, a rotation of consumer spending toward goods, and commodity price increases exacerbated by Russia’s invasion of Ukraine resulted in unusually large relative price increases, which required time to be absorbed. A simple Phillips curve framework suggests that the inflation surge was mainly driven by “cost push” factors, such as supply shortages and relative price shifts. Tight labor markets contributed to the persistence of above-target inflation. Despite differences in mandates of the monetary policy frameworks, central banks around the world responded similarly to recent global events.

Keywords: International comparison, inflation, global shortages, aggregate demand, aggregate supply, commodity prices, Phillips curve, inflation expectations, monetary policy

DOI: https://doi.org/10.17016/FEDS.2025.071

Abstract:

This paper reviews the drivers of the post-pandemic U.S. inflation surge and subsequent decline, including the behavior and role of inflation expectations. The sharp rise in inflation reflected severe imbalances between supply and demand stemming from the shocks of the pandemic and the policy response. Measures of short-term inflation expectations increased alongside realized inflation, especially those of households and firms, which may have contributed to inflation’s persistence through price- and wage-setting behavior. However, measures of longer-term inflation expectations remained generally well anchored, which likely prevented a larger or more lasting increase in inflation. The stability of longer-term inflation expectations, together with easing supply and demand imbalances, allowed inflation to fall from its peak in mid-2022 without a large increase in unemployment. We conclude by reviewing some lessons learned from this episode as well as potential risks to inflation going forward.

Keywords: Inflation, inflation expectations, COVID-19, monetary policy

DOI: https://doi.org/10.17016/FEDS.2025.070

Abstract:

This paper examines the Board staff’s inflation forecast misses over the years following the COVID-19 outbreak, focusing on a timeline of what staff members knew when and lessons learned along the way. The staff significantly underestimated both the size and persistence of the inflationary surge that followed the reopening of the U.S. economy. As a result, staff members made various changes to their forecasting procedures, including using new types of data to inform their assessment of supply–demand imbalances in product and labor markets and to guide their judgmental forecast. Throughout, an important difficulty was the lack of similar historical episodes upon which to base a quantitative analysis. Over time, the innovations helped improve the staff’s ability to understand and forecast inflation during this period. However, considerable uncertainty remains about the quantitative contributions of the various drivers of the pandemic-period inflation as well as the applicability of the lessons from this episode for forecasting.

Keywords: Inflation forecasting, inflation dynamics, Phillips curve, COVID-19 pandemic

DOI: https://doi.org/10.17016/FEDS.2025.069

Abstract:

This paper reviews recent academic studies to assess the implications of adopting a shortfalls, rather than a deviations, approach to pursuing maximum employment. Model-based simulations from these studies suggest three main findings. First, shortfalls rules generate inflationary pressure relative to deviations rules, which offsets downward pressure on inflation stemming from the presence of the effective lower bound. Second, since monetary policy leans against these inflationary pressures, a shortfalls rule implies a limited effect on average outcomes in the labor market. Finally, studies suggest that monetary policy can offset higher-than-desired average inflation under a shortfalls rule by leaning more strongly against deviations of inflation from the 2 percent objective, thereby keeping longer-term inflation expectations well anchored.

Keywords: Asymmetric monetary policy strategies, maximum employment, effective lower bound

DOI: https://doi.org/10.17016/FEDS.2025.068

Abstract:

We suggest a core set of indicators for evaluating the position of the labor market relative to maximum employment. The unemployment rate remains the key indicator of the cyclical position of the labor market, as it is time-tested, is highly correlated with other indicators, and has practical measurement advantages. But other indicators can provide complementary evidence to get a fuller picture of the labor market. A joint analysis of job vacancies and unemployment in a Beveridge curve diagram is helpful when structural shocks affect the labor market and when the labor market is very tight, while the employment-to-population ratio is useful late in expansions, when increases in employment tend to arise from higher labor force participation. Additional indicators—including wage growth and worker flows—can complement the core indicators we discuss. We draw on lessons from the Global Financial Crisis and the COVID-19 pandemic to evaluate the effectiveness of various indicators.

Keywords: Maximum employment, unemployment, job vacancies, labor force participation, wages, business cycle

DOI: https://doi.org/10.17016/FEDS.2025.067

Abstract:

We examine the experience of conducting reviews of monetary policy frameworks in the major advanced foreign economies since the Federal Open Market Committee's 2019–20 review. We find that periodic reviews are becoming the norm and have often been motivated by similar developments and challenges as those facing the Federal Reserve. In some cases, reviews were opportunities to alter numerical inflation objectives or clarify how policymakers balance fostering price stability and supporting employment and activity. In addition, foreign reviews emphasized the need for policy flexibility in pursuit of mandates. They also affirmed the usefulness of central banks' existing policy toolkits, while noting some concerns and limitations. Some reviews offered recommendations to improve communications.

Keywords: Monetary policy review, monetary policy strategies, monetary policy tools, central bank communications, foreign monetary policy

DOI: https://doi.org/10.17016/FEDS.2025.066

Abstract:

In this paper, we describe the Federal Reserve’s 2019–20 review of its monetary policy framework. First, we discuss the historical background of and motivation for the review. We then summarize the structure of the 2019–20 review, which included Fed Listens events, a flagship research conference, a series of staff analyses, and related Federal Open Market Committee (FOMC) deliberations. Finally, we present the main outcomes of the review, with particular attention paid to changes to the FOMC’s Statement on Longer Run Goals and Monetary Policy Strategy.

Keywords: Federal Reserve, framework review, consensus statement, inflation targeting, effective lower bound

DOI: https://doi.org/10.17016/FEDS.2025.065

Abstract:

There was a banking panic in New Mexico in early 1924 when about one-fourth of the banks in the state closed temporarily or permanently amid widespread runs. The Federal Reserve used both high profile and behind the scenes operations to calm the panic. This paper provides a history of this episode and explores how conspicuous and inconspicuous aspects of the Federal Reserve's response interacted to bolster confidence in the banking system.

Keywords: Banking Panic, New Mexico, Federal Reserve, Lender of Last Resort

DOI: https://doi.org/10.17016/FEDS.2025.064

Abstract:

We find that an increase in policy rate uncertainty is associated with an increase in MMF portfolio allocations towards assets with shorter-dated maturities. We also find that the direction of uncertainty matters: MMF portfolio maturity is more sensitive to uncertainty when it relates to changes in expectations for a larger increase or a smaller decrease in the policy rate than when it relates to changes in expectations for a smaller increase or a larger decrease in the policy rate. Furthermore, for MMF that are eligible to participate at the Federal Reserve's Overnight Reverse Repurchase Agreement (ON RRP) facility, we find that when policy rate uncertainty increases, MMF adjust their portfolio composition by increasing their take-up at the facility. This suggests that the ON RRP facility helps smooth fluctuations in short-term funding markets.

Keywords: money market funds, portfolio allocations, monetary policy expectations, uncertainty, Federal Reserve, ON RRP

DOI: https://doi.org/10.17016/FEDS.2025.063

Abstract:

This paper empirically investigates sources of hysteresis, focusing on downward nominal wage rigidity and the gender gap in the labor market, using U.S. state-level payroll employment data. Employing a Bayesian Markov-switching model of business cycles, we identify U-shaped and L-shaped recessions, which correspond to quick recoveries and hysteresis, respectively. Both U-shaped and L-shaped recessions are driven by supply and demand shocks; however, U-shaped recessions are associated with recessionary shocks that raise labor productivity, whereas L-shaped recessions are also driven by shocks that reduce labor productivity. Following L-shaped recessions, recoveries in employment, output, and labor productivity are sluggish and accompanied by declining inflation. In contrast, U-shaped recoveries feature stronger rebounds without significant changes in inflation. Greater downward nominal wage rigidity and a larger gender employment gap both increase the likelihood of L-shaped recessions and hysteresis. Downward nominal wage rigidity enhances the effectiveness of both expansionary monetary and tax policies. While expansionary monetary policy becomes more effective with a larger gender gap, the effectiveness of tax cuts remains unaffected.

Keywords: Hysteresis, Regional business cycles; L-shaped recession; U-shaped recession; Wage rigidity; Gender employment gap; Monetary policy; Fiscal policy

DOI: https://doi.org/10.17016/FEDS.2025.062

Abstract:

This paper fills an important gap in the volatility forecasting literature by comparing a broad suite of machine learning (ML) methods with both linear and nonlinear econometric models using high-frequency realized volatility (RV) data for the S&P 500. We evaluate ARFIMA, HAR, regime-switching HAR models (THAR, STHAR, MSHAR), and ML methods including Extreme Gradient Boosting, deep feed-forward neural networks, and recurrent networks (BRNN, LSTM, LSTM-A, GRU). Using rolling forecasts from 2006 onward, we find that regime-switching models—particularly THAR and STHAR—consistently outperform ML and linear models, especially when predictors are limited. These models also deliver more accurate risk forecasts and higher realized utility. While ML models capture some nonlinear patterns, they offer no consistent advantage over simpler, interpretable alternatives. Our findings highlight the importance of modeling regime changes through transparent econometric tools, especially in real-world applications where predictor availability is sparse and model interpretability is critical for risk management and portfolio allocation.

Keywords: Realized volatility, machine learning, regime-switching, nonlinearity, VaR, forecasting.

DOI: https://doi.org/10.17016/FEDS.2025.061

Abstract:

We provide evidence that mega firms have played an increasingly important role in shaping new technological trajectories in recent years. While the share of novel patents—defined as patents introducing new combinations of technological components— produced by mega firms declined until around 2000, it has rebounded sharply since then. Furthermore, we find that the technological impact and knowledge diffusion of novel patents by mega firms have grown relative to those by non-mega firms after 2001. We also explore potential drivers of this trend, presenting evidence that the rise in novel patenting by mega firms is tied to their disproportionate increase in cash holdings and the expansion of their technological scope. Our findings highlight an overlooked positive role of mega firms in the economywide innovation process.

Keywords: Mega Firms, Innovation, Novel Patents, Knowledge Diffusion

DOI: https://doi.org/10.17016/FEDS.2025.060

Abstract:

This paper examines how banks’ financing of nonbank lenders affects monetary policy transmission. Using supervisory bank loan-level data and deal-level private credit data, we document an intermediation chain: Banks lend to Business Development Companies (BDCs)—large private credit providers—which then lend to firms. As monetary tightening restricts bank lending, firms turn to BDCs for credit, prompting BDCs to borrow more from banks. This intermediation chain raises borrowing costs, as banks charge BDCs higher rates, which BDCs pass on to firms. Consistent with this pass-through, bank-reliant BDCs respond more strongly to monetary tightening, and BDC-dependent firms grow more but exhibit weaker interest coverage ratios. Overall, while bank lending to nonbanks mitigates credit contraction and supports investment during tightening, it amplifies monetary transmission by elevating borrowing costs and financial distress risk.

Keywords: Banks and nonbanks; Monetary policy transmission; Business development companies (BDCs); Private credit; Credit chain

DOI: https://doi.org/10.17016/FEDS.2025.059

Abstract:

This comment discusses Kolesár and Plagborg-Møller's (2025) finding that the standard linear local projection (LP) estimator recovers the average marginal effect (AME) even in nonlinear settings. We apply and discuss a subset their results using a simple nonlinear time series model, emphasizing the role of the weighting function and the impact of nonlinearities on small-sample properties.

Keywords: Local projections, average marginal effect, nonlinear time series , small-sample properties., weighting function

DOI: https://doi.org/10.17016/FEDS.2025.058

Abstract:

The official labor force statistics often exhibit discontinuities in January, when updated population estimates are incorporated into the Current Population Survey (CPS) for the current year but are not revised backward through history. We construct harmonized population estimates spanning five decades and produce new weights for the CPS microdata that are benchmarked to these estimates. Using these weights, we estimate harmonized labor force statistics that reflect the latest available information about the population and its characteristics. The harmonized labor force series are free from the discontinuities in the historical data and show a notably larger labor force shortfall in the post-pandemic period.

Keywords: population, labor force, employment, unemployment, immigration, CPS

DOI: https://doi.org/10.17016/FEDS.2025.057

Abstract:

We study investors’ perceptions of inflation through the lens of a high-frequency event study, documenting they have a stagflationary view of the world. In response to higher-than-expected inflation, investors expect firms’ nominal cash flows to remain stagnant while discount rates increase, resulting in lower stock prices. Both the equity risk premium and nominal risk-free yields rise, but longer-term real yields remain unchanged. Consistent with investors interpreting inflation as a cost shock, investors expect firms with low market power to suffer larger declines in cash flows. Cash flow expectations of equity investors are aligned with those of professional earnings analysts.

Keywords: Inflation, Stock Returns, Stagnant Cash Flows, Market Power

DOI: https://doi.org/10.17016/FEDS.2025.056

Abstract:

This paper uses a structural vector autoregressive (SVAR) model—identified with an external monetary policy instrument and sign restrictions—to derive a measure of bank credit conditions from changes in bank lending standards. The model incorporates data on interest rates, bank credit, and survey-based measures of bank lending standards to identify monetary policy, credit demand, and credit supply shocks. Using these identified shocks, we construct a novel measure of bank credit conditions that corresponds to the component of credit growth that would occur if credit demand remained unchanged, reflecting solely the impacts of monetary policy and credit supply shocks. Using this measure, we find that credit supply–driven changes in bank credit conditions have a stronger impact on real outcomes in the euro area, whereas monetary policy–driven changes play a larger role in the U.S. economy.

Keywords: Bank Credit; Bank Lending Surveys; Monetary Policy; External Instruments; Sign Restrictions; SVAR

DOI: https://doi.org/10.17016/FEDS.2025.055

Abstract:

A large literature at the intersection of economics and finance offers prescriptions for regulating banks to increase financial stability. This literature abstracts from the discretion that accounting standards give banks over financial reporting, creating a gap between the information assumed to be available to regulators in models of optimal regulation and the information available to regulators in reality. We bridge insights from the economics, finance, and accounting literatures to synthesize knowledge about the design and implementation of bank regulation and identify areas where more work is needed. We present a simple framework for organizing the relevant ideas, namely the externalities that motivate bank regulation, the rationales for allowing accounting discretion, and the use of discretion to circumvent regulation. Our takeaway from reviewing work in these areas is that academic studies of bank regulation and accounting discretion require a more unified approach to design optimal policy for the real world.

Keywords: Bank regulation, Discretion, Financial stability

DOI: https://doi.org/10.17016/FEDS.2025.054

Abstract:

With the advent of generative AI (genAI), the potential scope of artificial intelligence has increased dramatically, but the future effect of genAI on productivity remains uncertain. The effect of the technology on the innovation process is a crucial open question. Some inventions, such as the light bulb, temporarily raise productivity growth as adoption spreads, but the effect fades when the market is saturated; that is, the level of output per hour is permanently higher but the growth rate is not. In contrast, two types of technologies stand out as having longer-lived effects on productivity growth. First, there are technologies known as general-purpose technologies (GPTs). GPTs (1) are widely adopted, (2) spur abundant knock-on innovations (new goods and services, process efficiencies, and business reorganization), and (3) show continual improvement, refreshing this innovation cycle; the electric dynamo is an example. Second, there are inventions of methods of invention (IMIs). IMIs increase the efficiency of the research and development process via improvements to observation, analysis, communication, or organization; the compound microscope is an example. We show that GenAI has the characteristics of both a GPT and an IMI—an encouraging sign that genAI will raise the level of productivity. Even so, genAI’s contribution to productivity growth will depend on the speed with which that level is attained and, historically, the process for integrating revolutionary technologies into the economy is a protracted one.

Keywords: Artificial Intelligence, Machine Learning, Productivity, Technological Growth

DOI: https://doi.org/10.17016/FEDS.2025.053

Abstract:

We examine how primary dealers utilized repo operations conducted by the Federal Reserve from September 2019 until May 2020 and how usage affected dealer borrowing and lending. Using daily dealer-level supervisory data, we find that during normal market conditions, dealers primarily used Fed repo to expand their total repo borrowing and on-lent much of this funding to a broad variety of counterparties. However, during market stress in March 2020, dealers used Fed repo as a substitute for funding from other counterparties and focused their on-lending to affiliated counterparties. Moreover, dealers with more headroom under the Supplementary Leverage Ratio requirement used more of their Fed repo borrowing to provide intermediation in funding markets. Our results underscore the critical role that the Fed's repo operations played, especially in March 2020, by reducing dealer funding stress and enabling dealers to pass on liquidity.

Keywords: Federal Reserve, dealer intermediation, funding markets, repo operation, Standing Repo Facility, leverage ratio

DOI: https://doi.org/10.17016/FEDS.2025.052

Abstract:

We propose a simple algorithm for defining merger-specific geographic antitrust markets based on merging firm proximity. Applying it to over a thousand hypothetical bank mergers, we compare concentration measures in our markets to those defined by the Federal Reserve, which are not merger-specific, finding broad agreement but also offering potential improvements upon current definitions.

Keywords: market definition; bank mergers; computational methods

DOI: https://doi.org/10.17016/FEDS.2025.051

Abstract:

Using zip-code median income as a proxy for household income is common in economics but can mask heterogeneity and yield misleading conclusions. Using zip-code median income and self-reported household incomes from a representative panel of 150,000 U.S. households, we decompose average retail spending for 2018-2024. When using self-reported incomes, we observe substantial divergence in spending between low- and high-income households starting in mid-2021. When using zip-code aggregates as a proxy, this divergence disappears. Our findings indicate a 35 to 75 percent discrepancy between zip-code aggregates and self-reported incomes, highlighting the limitation of zip-code aggregates as a proxy for household incomes.

Keywords: Spending, Income, Heterogeneity, Zip-code Average Income

DOI: https://doi.org/10.17016/FEDS.2025.050

Abstract:

This paper estimates trading costs in the off-the-run Treasury market using comprehensive transactions data and machine learning techniques. The analysis reveals several key findings that enhance the understanding of the off-the-run Treasury market liquidity. First, the indicative bid-ask spread is shown to be a biased measure of liquidity, even when not considering transaction volume. Specifically, bid-ask spreads systematically overstate trading costs of more seasoned Treasuries, and the liquidity of benchmark, on-the-run securities affects how off-the-run bid-ask spreads map to trading costs. Second, the paper demonstrates that trading costs may scale non-monotonically with transaction volume, which suggests selective, opportunistic liquidity-taking. Additionally, transaction size has greater effect on off-the-run securities’ trading costs when benchmark, on-the-run liquidity is lower. Finally, indicative bid-ask spreads may notably overstate trading costs for larger orders of relatively less liquid securities. These findings contribute to our understanding of actual liquidity in the off-the-run Treasury market, while highlighting the limitations of a traditional liquidity measure. By providing a more nuanced view of trading costs, this study contributes valuable insights for supporting financial stability and optimal asset allocation.

Keywords: liquidity, Treasury market, off-the-run, effective bid-ask spread

DOI: https://doi.org/10.17016/FEDS.2025.049

Abstract:

We apply natural language processing tools to news articles in the financial press to construct a sentiment index—an index of the perceived semantic orientation of monetary policy communications around scheduled Federal Open Market Committee (FOMC) meetings. To that end, we develop several dictionaries that capture various monetary policy tools: conventional monetary policy, asset purchases, and forward guidance. The surprises in the sentiment index around FOMC meetings announcements explain variation in major asset prices classes between May 1999 and November 2022. Sentiment index surprises are important for explaining the variation in asset prices beyond monetary policy surprises.

Keywords: Textual analysis, semantic orientation, sentiment index, Federal Reserve, FOMC, hawkish, dovish, asset prices, policy expectations, conventional monetary policy, asset purchases, forward guidance, zero-lower-bound, COVID

DOI: https://doi.org/10.17016/FEDS.2025.048

Abstract:

Amid ongoing trade policy shifts and geopolitical uncertainty, concerns about stagflation have reemerged as a key macroeconomic risk. This paper develops a probabilistic framework to estimate the likelihood of stagflation versus soft landing scenarios over a four-quarter horizon. Building on Bekaert, Engstrom, and Ermolov (2025), the model integrates survey forecasts, structural shock decomposition, and a non-Gaussian BEGE-GARCH approach to capture time-varying volatility and skewness. Results suggest that the probability of stagflation was elevated at around 30 percent in late 2022, while the chance of a soft landing was below 5 percent. As inflation moderated and growth remained strong through 2024, these probabilities reversed. However, by mid-2025, renewed tariff concerns drove stagflation risk back up and the probability of a soft landing lower. These shifts highlight the potential value of distributional forecasting for policymakers and market participants navigating uncertain macroeconomic conditions.

Keywords: GARCH, Inflation, Recession, Soft landing, Stagflation, Time-varying uncertainty

DOI: https://doi.org/10.17016/FEDS.2025.047

Abstract:

This paper documents a new dollar channel that transmits monetary policy across borders. Exploiting unique features of the syndicated loan market for identification, we show that changes in the euro-dollar exchange rate around ECB monetary policy announcements that are orthogonal to simultaneous changes in euro-area interest rates and stock prices affect U.S. leveraged loan spreads. Specifically, in response to dollar appreciation, investors require higher compensation for risk, and borrowing costs for U.S. firms increase. These findings imply a causal link between the U.S. dollar and investors’ risk appetite.

Keywords: loan pricing, monetary policy spillovers, dollar, institutional investors, risk taking

DOI: https://doi.org/10.17016/FEDS.2025.046

Abstract:

Home-related spending in categories such as furnishings, renovations, and repairs is tied to housing market activity, with significant implications for aggregate expenditure dynamics. We refer to this relationship as the home purchase channel of expenditure. Using household-level panel data we estimate that home purchases lead to sizable increases in home-related spending, but not to increases in goods and services unrelated to home purchase. These findings are robust to the use of close-control groups and placebo tests. We then build a heterogeneous household model with housing, home renovations, and home-related durables that is calibrated to match our household-level evidence. Model simulations of housing market shocks generate large fluctuations in home-related and total expenditure. We show that the home purchase channel amplifies aggregate expenditure dynamics, with home-related spending accounting for around half of total spending fluctuations over the housing cycle.

Keywords: Home purchase, Household spending, Housing, Housing cycle

DOI: https://doi.org/10.17016/FEDS.2025.045

Abstract:

We evaluate the ability of large language models (LLMs) to estimate historical macroeconomic variables and data release dates. We find that LLMs have precise knowledge of some recent statistics, but performance degrades as we go farther back in history. We highlight two particularly important kinds of recall errors: mixing together first print data with subsequent revisions (i.e., smoothing across vintages) and mixing data for past and future reference periods (i.e., smoothing within vintages). We also find that LLMs can often recall individual data release dates accurately, but aggregating across series shows that on any given day the LLM is likely to believe it has data in hand which has not been released. Our results indicate that while LLMs have impressively accurate recall, their errors point to some limitations when used for historical analysis or to mimic real time forecasters.

Keywords: Artificial intelligence, Forecasting, Large language models, Real-time data

DOI: https://doi.org/10.17016/FEDS.2025.044

Abstract:

This article refines the concept of black swans, typically described as highly unlikely and catastrophic events, by clearly distinguishing between knowable and unknowable events. By emphasizing that black swans are “unknown unknowns,” the article highlights that the realization of new black swans cannot be prevented and motivates a need for policies that build the financial system's resilience to unforeseeable crises. The article introduces a "resilience principle" that calls for policies that are adaptable, universal, and systemic. Examples are provided of policies with these features, none of which relies on the official sector being better positioned than the private sector to anticipate the unknown.

Keywords: Black Swans, Systemic Risk, Uncertainty, Financial Stability

DOI: https://doi.org/10.17016/FEDS.2025.043

Abstract:

We argue that inflationary shocks affect allocative efficiency by changing the rate and the characteristics of workers’ job-to-job transitions. First, using monetary policy shocks and survey data on search effort, we empirically show that a one percentage point rise in inflation increases job-to-job transitions by up to 4.5%, and workers with higher inflation expectations are more likely to search and do so more effectively. Second, we build a general equilibrium model of directed on-the-job search to quantify the aggregate implications of labor market reactions. Higher-than-expected inflation reduces real wages, prompting workers to search more actively and aim lower. This increases job-to-job transitions but lowers the efficiency gains per transition. Therefore, the effect on output is ambiguous. Last, we calibrate the model to the U.S. economy. Inflationary shocks increase reallocation rates, yet allocative efficiency and output decline. Small deflationary shocks (e.g., 2%) increase output in the short run, while others decrease it.

Keywords: Inflation, Job-to-job Flows,Worker Reallocation

DOI: https://doi.org/10.17016/FEDS.2025.042

Abstract:

We analyze evolution of inflation expectations in the euro area (EA) using a novel measure of inflation expectations implied by the French nominal and inflation-indexed bonds. Overall, we find that EA inflation expectations have been relatively well anchored in the 2004–2019 sample but have been somewhat sensitive to the incoming macroeconomic news and monetary policy shocks in the sample that includes the COVID-19 pandemic. Our results are robust with respect to the use of different inflation-indexed securities data, such as the EA inflation-linked swaps.

Keywords: Obligations Assimilables du Trésor, OAT, French inflation-indexed bonds, nominal-indexed bond spreads, inflation swaps, inflation expectations, macroeconomic news, monetary policy shocks, euro area, inflation anchoring, stability.

DOI: https://doi.org/10.17016/FEDS.2025.041

Abstract:

This paper presents an overview of how various labor market indicators differ across geography. While many indicators are often discussed in terms of national aggregates, such discussions obscure the large degree of variation that exists across localities. We primarily use counties as a geographic unit, and document both structural differences that persist over time as well as differences in the past two business cycles. The racial composition of communities plays a large role in explaining geographic differences in labor market indicators, in some cases even more so than income. We specifically focus on the importance of labor market tightness in the general economic development of counties and in the recovery from the pandemic recession. We find substantial heterogeneity in the degree of labor market tightness across counties, as measured by the vacancy rate using job postings from Lightcast, and moreover find a close connection between this rate and county income growth. Finally, we show how the distribution of labor market tightness evolved over the course of the pandemic.

Keywords: Geographic Inequality, Job Postings, Job Vacancies, Unemployment Rates

DOI: https://doi.org/10.17016/FEDS.2025.040

Abstract:

This paper examines the costs and benefits of using a straightforward county-level income definition in the classification and study of lower-income communities. A definition based on population-weighted distribution of county-level median household incomes does a good job of identifying the most economically disadvantaged communities across a wide range of indicators. We show robustness to the use of different thresholds, levels of geography, and cost-of-living adjustments.

Keywords: Geographic Inequality, Household Financial Well-being

DOI: https://doi.org/10.17016/FEDS.2025.039

Abstract:

We study the behavior of liquidity providers and liquidity consumers in the 10-year U.S. Treasury futures market during the height of the COVID-19 shock in March 2020, a period of market turmoil when demand for liquidity was high. In March 2020, PTFs reduced their volume of liquidity providing trades as a share of total trading volume. However, they still accounted for the lion share of total liquidity provision and their liquidity provision improved market liquidity. In contrast, dealers (banks and non-banks) increased their volume of liquidity providing trades as a share of total trading volume, but their activity did not have a large effect on overall liquidity. Among the traders that place liquidity consuming trades, asset managers had the largest impact on liquidity by increasing transaction costs. Despite a significant attention to the role of basis traders in the Treasury market disruption of March 2020, we do not find evidence for basis traders being important drivers of disruption in Treasury futures market.

Keywords: PTFs; Basis Traders; Treasury Futures

DOI: https://doi.org/10.17016/FEDS.2025.038

Abstract:

This paper examines the place that a "look-through" approach to price shocks has acquired in inflation-targeting frameworks. The "look-through" approach reflects the fact that, in the event of a shock that is likely (on impact) to put a sizable share of consumer prices under upward pressure, one option available to the central bank is to accommodate the initial price rise. In doing so, it can also attempt to ensure that future inflation rates, and inflation expectations, are insulated from the shock. Although the policy of "looking through" has achieved considerable acceptance, its origins are not widely understood. The analysis provided here indicates that key aspects of the "look-through" approach were aired in U.S. public discourse in 1973−1974, when the appropriate response to the first oil shock was being considered. The approach was subsequently refined in the course of several countries' experiences of price shocks from the mid-1970s to the early 1990s, with the specific "look through" terminology emerging at the end of this period. The connection between the "look-through" approach and the notion of inflation expectations being anchored by the central bank is also considered.

Keywords: Monetary policy strategy, inflation targeting, look-through approach.

DOI: https://doi.org/10.17016/FEDS.2025.037

Abstract:

We introduce methodology to bridge scenario analysis and model-based risk forecasting, leveraging their respective strengths in policy settings. Our Bayesian framework addresses the fundamental challenge of reconciling judgmental narrative approaches with statistical forecasting. Analysis evaluates explicit measures of concordance of scenarios with a reference forecasting model, delivers Bayesian predictive synthesis of the scenarios to best match that reference, and addresses scenario set incompleteness. This underlies systematic evaluation and integration of risks from different scenarios, and quantifies relative support for scenarios modulo the defined reference forecasts. The framework offers advances in forecasting in policy institutions that supports clear and rigorous communication of evolving risks. We also discuss broader questions of integrating judgmental information with statistical model-based forecasts in the face of unexpected circumstances.

Keywords: Macroeconomic Forecasting, Mixtures of Scenarios, Misclassification Rates, Entropic Tilting, Bayesian Predictive Synthesis, Judgmental Forecasting, Forecast Risk Assessment

DOI: https://doi.org/10.17016/FEDS.2025.036

Abstract:

The isolated effects of collateral reuse on financial stability are ambiguous and understudied. While greater collateral reuse can guarantee more payments with fewer assets, it can also increase the exposure to potential drops in collateral price. To analyze these tradeoffs, we develop a financial network model with endogenous asset pricing, multiple equilibria, and equilibrium selection. We find that more collateral reuse decreases the likelihood of the worst equilibrium (crisis), with varying effects depending on the network structure. Therefore, collateral reuse can unambiguously improve financial stability for a fixed degree of risk-taking behavior. However, with endogenous risk-taking, we show that a higher degree of collateral reuse can worsen financial stability through greater risk-taking. As a result, while crises may occur less frequently, their severity would increase, leading to a lower social surplus during crises.

Keywords: Collateral, Collateral reuse, Financial network, Fire sale, Multiple equilibria, Equilibrium selection, Systemic risk

DOI: https://doi.org/10.17016/FEDS.2025.035

Abstract:

Self-imposed risk limits effectively limit dealers' appetite for risks and their capacity to intermediate in Treasury markets in times of market stress. Using granular and high frequency regulatory data on US dealers' Treasury securities trading desk positions and desk-level Value-at-Risk limits, we show that dealers are more inclined to reduce their positions as they get closer to their internal risk limit, consistent with such limit being meaningful and costly for traders to breach. Dealers actively manage their inventories away from their limits by selling longer-term securities and requiring higher compensation to take on additional risks. During the height of the Covid-crisis in 2020, dealer desks that were closer to their VaR limits sold more Treasury securities to the Fed and accepted lower prices in the emergency open market operations. Our findings complement studies that link post-GFC bank regulations to market liquidity by showing that self-imposed risk limits can explain the risk-averse behavior by dealers, and provide a micro-foundation for the link between market volatility and market liquidity in dealer-intermediated OTC markets. In times of crisis, policy prescriptions such as deregulation alone may not be sufficient to induce risk-taking by dealer intermediaries. Moreover, to address market functioning issues, policy actions that address the funding costs of intermediaries would not be as effective as policies that remove risks from intermediary balance sheets directly.

Keywords: Dealer Intermediation Capacity, Treasury Market, Risk Limits, Regulation, Market Liquidity

DOI: https://doi.org/10.17016/FEDS.2025.034

Abstract:

We propose a new way to classify individuals without a bank account, accounting for their actual interest in being banked. Analogous to how unemployment statistics are defined and estimated, we differentiate the individuals that do not have a bank account and would like to have one (the “unbanked”) from individuals that do not have a bank account and are not interested in having one (the “out of banking population”). Using FDIC data, we show the evolution over time of these new measures and show that the two groups differ in policy-relevant ways. While the unbanked mostly cite financial and past credit or banking history problems as reasons for not having a bank account, the out of banking population cites a growing mistrust toward the traditional banking system. Policymakers should consider these factors when designing policies aimed at increasing financial inclusion.

Keywords: unbanked, FDIC, banking, checking, fintech, financial inclusion

DOI: https://doi.org/10.17016/FEDS.2025.033

Abstract:

As financial intermediaries, banks have a key role in producing information and managing the risks on diverse loan portfolios. An important input into this process is ongoing collection of financial performance from borrowers. Using supervisory data on commercial real estate loans (CRE), this paper studies relationships between the content and timeliness of borrower-reported performance, internal bank risk ratings, and subsequent loan performance. Banks heavily rely on borrower reporting when setting risk ratings, despite the fact that borrowers with stale financials are more likely to default. Although banks can generally be slow to update their ratings as information becomes more stale on average, we find causal evidence that they do monitor more intensively in response to loan, location and portfolio risks.

Keywords: bank monitoring, risk management, commercial real estate mortgages, financial performance reporting

DOI: https://doi.org/10.17016/FEDS.2025.032

Abstract:

Using data on U.S. manufacturing plants, I estimate a production function model that includes agglomeration intensity as a component of total factor productivity and allows agglomeration benefits to vary across establishments, which can lead to sorting. I find that agglomeration benefits decline with unobserved establishment-level raw productivity.

Keywords: Agglomeration, Sorting, Census of Manufactures.

DOI: https://doi.org/10.17016/FEDS.2025.031

Abstract:

We show that the effectiveness of unconventional monetary policy is limited by how banks adjust credit supply and manage liquidity risk in response to fragile non-bank funding. For identification, we use granular U.S. administrative data on deposit accounts and loan-level commitments, matched with bank-firm supervisory balance sheets. Quantitative easing increases bank fragility by triggering a large inflow of uninsured deposits from non-bank financial institutions. In response, banks that are more exposed to this fragility actively manage their liquidity risk by offering better rates to insured deposits, while cutting uninsured rates. Doing so, they shift away from uninsured to insured deposits. Importantly, on the asset side, these banks also reduce the supply of contingent credit lines to corporate clients. This tightening of liquidity provision has real effects, as firms reliant on more exposed banks experience a reduction in liquidity insurance stemming from credit lines, leading to lower investment. Our analysis reveals that the fragility of deposit funding can disrupt the complementarity between deposit-taking and the provision of credit lines.

Keywords: Bank fragility, Liquidity risk, Liquidity Insurance, Deposits, Credit lines, Quantitative Easing, Quantitative Tightening, Non-banks

DOI: https://doi.org/10.17016/FEDS.2025.030

Abstract:

This study assesses whether the introduction of the GSIB surcharge requirement resulted in GSIBs reducing the systemic risk posed by their activities. We find limited evidence of GSIBs managing their activities to avoid increases in their surcharges. For a sample of international banks, proximity to surcharge thresholds is associated to a decrease in the growth of intra-financial system liabilities, underwriting activities, and holdings of trading and available-for-sale securities. In the case of US GSIBs and the method 2 GSIB surcharge, we find some association between proximity to surcharge thresholds and a decrease in the growth of trading and available-for-sale securities and short-term wholesale funding.

Keywords: bank capital requirements, banking regulation, GSIB surcharge, systemic risk

DOI: https://doi.org/10.17016/FEDS.2025.029

Abstract:

We show that the secular decline in real interest rates in the United States, which began in the early 1980s and persisted for nearly four decades, reduced the labor’s share of output and the unemployment rate, and increased earnings inequality. We establish this link using a model of frictional labor markets, estimated from household-level data, in which unemployment risk is only partially insurable. Rising debt resulting from lower interest rates reduces the value of unemployment, leading to lower equilibrium wages relative to productivity and a lower unemployment rate. Wage dispersion also rises. The model is consistent with panel-data reduced-form evidence linking unemployment duration, assets, debt, and post-unemployment wages. In the model, a decline in the real interest rate of the magnitude observed in the data generates a decline in the labor’s share of 6 percentage points and in the unemployment rate of 0.3 percentage points. The variance of log earnings rises from 0.66 to 0.75.

Keywords: Labor Share, Household Indebtedness, Reservation Wage

DOI: https://doi.org/10.17016/FEDS.2025.028

Abstract:

Since the 1990s, monetary policy research has highlighted the properties of policy rules that stabilize inflation and economic activity, the role of inflation targeting in anchoring expectations, and the constraints posed by the effective lower bound (ELB). This paper combines these themes by examining whether explicitly responding to long-run inflation expectations improves policy effectiveness. Using both a small model for intuition and a large-scale policy model for quantitative evaluation, the analysis shows that the proposed approach reinforces inflation anchoring, reduces volatility from slow-moving inflationary forces, and mitigates ELB risks. The findings suggest that policy rules incorporating long-run inflation expectations enhance stability and complement makeup strategies by addressing ELB risks through different channels. Given that central banks already emphasize inflation expectations in their communications, this strategy aligns naturally with existing policy discussions.

Keywords: Monetary policy; inflation targeting; anchored inflation expectations; effective lower bound

DOI: https://doi.org/10.17016/FEDS.2025.027

Abstract:

Using a broad definition of energy consumption that includes both residential energy use and gasoline for transport, we identify 20% of households in the PSID as energy burdened (EB) based on a twice-the-median, income-based threshold. Logit analysis shows that being nonwhite, being single with dependents, receiving public assistance, having no post-secondary education, and being unemployed increase the probability of being EB. We document four key empirical facts: (1) EB/non-EB status is persistent; (2) EB households have significantly higher marginal propensities to consume and marginal propensities to consume energy compared to non-EB households; (3) EB households experience lower expected energy consumption growth despite having higher expected income growth relative to non-EB households; and (4) EB households face more volatile energy consumption and income than non-EB households. Lastly, we show that both consumption inequality and energy consumption inequality have risen more moderately than income inequality over the 1999 to 2021 period. Inequality in residential energy consumption increased until 2009, then declined, whereas inequality in gasoline consumption for transport has risen steadily, reaching a level 50% higher in 2021 than in 1999.

DOI: https://doi.org/10.17016/FEDS.2025.026

Abstract:

Since the initial launch of inflation targeting in the early 1990s in New Zealand and a few other countries, inflation targeting has become the predominant monetary policy strategy in large advanced and emerging market economies. Inflation targeting has been remarkably successful in anchoring inflation, likely owing to core elements of the framework across central banks. Its reaction process, which adjusts the monetary policy stance to ensure the return of inflation to target, allows it to flexibly incorporate a wide range of factors while limiting the discretionary biases that can contribute to excessive inflation. The emphasis on communications about the inflation outlook promotes transparency and accountability. As a result, inflation targeting central banks have, on balance, managed well the large shocks associated with the Global Financial Crisis and COVID. Even so, there are numerous challenges discussed in this paper that are associated with calibration and communications of forward guidance, quantitative easing/tightening, and financial stability.

Keywords: Inflation targeting, monetary policy, central banking, financial stability

DOI: https://doi.org/10.17016/FEDS.2025.025

Abstract:

Using the micro data underlying the U.S. CPI, we document several findings about firm price-setting behavior during and following the Covid-19 pandemic, a period with the highest levels of inflation seen in around forty years. 1) The frequency of price change increased substantially as inflation took off, and has declined markedly as inflation has receded. 2) The average size of price changes also increased as price increases became more common, while the absolute value changed little. 3) The dispersion of price changes did not fall, contrary to the prediction of state-dependent models. 4) A menu cost model fitted on pre-pandemic pricing data has difficulty matching the increase in the frequency of price changes post-pandemic, which was not the case for the high inflation period of the 1980s. A re-calibrated menu cost model with smaller menu costs and larger idiosyncratic shocks can match the elevated frequency seen in the post-pandemic period, but not the movements in the dispersion of price changes. Such a model also implies a faster pass-through of shocks to inflation than the model fitted to pre-pandemic data.

Keywords: Inflation, Microdata, Sticky prices

DOI: https://doi.org/10.17016/FEDS.2025.024

Abstract:

This paper examines the revival in the analysis of monetary policy rules that took place during the 1990s. The focus is on the role that John Taylor played in this revival. It is argued that Taylor’s role—most notably through his advancing the Taylor rule, developed in 1992−1993 and increasingly permeating discussions in research and policy circles over the subsequent several years—is usefully viewed as one of building bridges. In particular, Taylor created links between a monetary policy rules tradition closely associated with Milton Friedman and an interest-rate setting tradition long associated with central banks. The rules tradition had looked unfavorably on interest-rate setting, while the central bank tradition was unfavorably disposed toward monetary policy rules. The Taylor rule provided a compromise between the traditions, while also advancing an interest-rate reaction function that helped create a revival during the 1990s of economic research on monetary policy rules.

Keywords: Taylor rule, interest rate rules.

DOI: https://doi.org/10.17016/FEDS.2025.023r1

Abstract:

I provide evidence that investors' attention allocation plays a critical role in how financial markets incorporate macroeconomic news. Using intraday data, I document a sharp increase in the market reaction to Consumer Price Index (CPI) releases during the 2021-2023 inflation surge. Bond yields, market-implied inflation expectations, and other asset prices exhibit significantly stronger responses to CPI surprises, while reactions to other macroeconomic announcements remain largely unchanged. The joint reactions of these asset prices point to an attention-based explanation–an interpretation I corroborate throughout the rest of the paper. Specifically, I construct a measure of CPI investor attention and find that: (1) attention was exceptionally elevated around CPI announcements during the inflation surge, and (2) higher pre-announcement attention robustly leads to stronger market reactions. Studying investor attention in the context of Employment Report releases and Federal Reserve announcements, I document a similar importance of attention allocation for market reactions. Lastly, I find that markets tend to overreact to announcements that attract high levels of attention.

Keywords: Macroeconomic News Announcements, Investor Attention, Financial Markets, Inflation, Federal Reserve, High-frequency event study

DOI: https://doi.org/10.17016/FEDS.2025.022

Abstract:

I combine microdata on the intertemporal marginal propensity to consume with 10 structural macro shocks to identify the role of intertemporal substitution in consumption behavior. Although some of the structural shocks that I examine lead to large and persistent changes in real interest rates—which in many models would induce a large intertemporal substitution effect—I find no evidence that households shift the timing of their consumption in response to these interest rate changes. Indeed, changes to the expected path of income explain almost all the aggregate consumption response, leaving no role for intertemporal substitution.

Keywords: Intertemporal Substitution, HANK, Monetary Policy, Consumption

DOI: https://doi.org/10.17016/FEDS.2025.021

Abstract:

We develop a model of Ponzi schemes with asymmetric information to study Ponzi frauds. A long-lived agent offers to save on behalf of short-lived agents at a higher rate than they can earn themselves. The long-lived agent may genuinely have a superior savings technology, but may be an imposter trying to steal from short-lived agents. The model identifies when a Ponzi fraud can occur and what interventions can prevent it. A key feature of Ponzi frauds is that the long-lived agent builds trust over time and improves their reputation by keeping the scheme going.

Keywords: Ponzi scheme, asymmetric information, reputation, fraud

DOI: https://doi.org/10.17016/FEDS.2025.020

Abstract:

This paper was written for the academic conference held in celebration of the 100th anniversary of the Division of Research and Statistics (R&S) of the Federal Reserve Board. The work of the Federal Reserve turns strongly on empirical efforts to understand the structure and state of the economy, and R&S can be thought of as operating a large factory for discovering and developing data and analytical methods to provide evidence relevant to the mission of the Board. This paper, as signaled by its title, illustrates how the measurement research component of the R&S factory often looks far beyond current conventions to meet the needs of the Board—and has done so since its earliest days. It would take a far longer paper to provide a complete history and evolution of measurement activities in R&S; here, we provide an indicative review focusing on selected areas from which, we believe, it is easy to conclude that R&S has been—and likely will continue to be—an important innovator in economic measurement.

Keywords: Data collection methods and estimation strategies; Business cycles, productivity, and price measurement; Financial accounts and financial data; the Survey of Consumer Finances; Blended data.

DOI: https://doi.org/10.17016/FEDS.2025.019

Abstract:

This study examines the demographic characteristics of U.S. state border counties, comparing them with those of nonborder counties. The demographic representativeness of border counties is essential for the interpretation of the results in state border-county difference-in-difference analyses, used in state policy evaluations. Our findings reveal that border counties generally have higher proportions of White, older, and disabled populations. We also see occasional instances of wide demographic differences across state boundaries. These differences potentially undermine the external validity and identification of policy evaluations. We illustrate the implications of these finding through a case study, highlighting the need for robustness checks and demographic considerations in border-county policy research.

Keywords: Demographics, Difference-in-Difference Estimates, Event Studies, Natural Experiments, Policy Experiments, US state border counties

DOI: https://doi.org/10.17016/FEDS.2025.018

Abstract:

Payment fraud has been high in recent years, and as criminals gain access to capability-enhancing generative AI tools, there is a growing need for innovative fraud detection research. However, the pace, diversity, and reproducibility of such research are inhibited by the dearth of publicly available payment transaction data. A few payment simulation methodologies have been developed to help narrow the payment transaction data gap without compromising important data privacy and security expectations. While these simulation approaches have enabled research advancements, more work is needed to generate datasets that reflect diverse and evolving fraud tactics. This paper introduces CardSim, a flexible, scalable payment card transaction simulation methodology that extends the small but emerging body of simulators available for payment fraud modeling research. CardSim is novel in the extent to which it is calibrated to publicly available data and in its Bayesian approach to associating payment transaction features with fraud. The simulator’s modular structure, which is operationalized in a corresponding software package, makes it easy to update based on new evidence about payment trends or fraud patterns. After laying out the simulation methodology, I show how outputs can be used to test and evaluate machine learning workflows, modeling approaches, and interpretability frameworks that are relevant for payment card fraud detection.

Keywords: Payment cards, Fraud detection, Bayesian analysis, Simulation, Machine learning

DOI: https://doi.org/10.17016/FEDS.2025.017

Abstract:

Filtered historical simulation (FHS)—a simple method of calculating Value-at-Risk that reacts quickly to changes in market volatility—is a popular method for calculating margin at central counterparties. However, FHS does not address how correlation can vary through time. Typically, in margin systems, each risk factor is filtered individually so that the computational burden increases linearly as the number of risk factors grows. We propose an alternative method that filters historical returns using latent risk factors derived from principal component analysis. We compare this method's performance with "traditional" FHS for different simulated and constructed portfolios. The proposed method performs much better when there are large changes in correlation. It also performs well when that is not the case, although some care needs to be taken with certain concentrated portfolios. At the same time, the computational requirements can be reduced significantly. Backtesting comparisons are performed using data from 2020 when markets were stressed by the COVID-19 crisis.

Keywords: Portfolio risk, Value-at-Risk, Margin, CCPs, Principal component analysis (PCA), historical simulation, FHS

DOI: https://doi.org/10.17016/FEDS.2025.016

Abstract:

The Federal Reserve’s discount window is a tool that can provide reserves to banks at a rate set by the Federal Reserve, the discount rate. During the past several years, there have been large fluctuations in the level of reserves in the banking system and in the level discount rate relative to other interest rates. In this paper, we explore how banks’ holdings of reserves, especially relative to the amount of reserves that banks prefer to hold, and the interest rate available at the discount window influence borrowing at the window. We find that banks borrow more when their reserves are low and when the discount rate is relatively attractive, although the size of these effects depends on a bank’s size, FHLB membership status, and financial condition.

DOI: https://doi.org/10.17016/FEDS.2025.015

Abstract:

Analysis of market liquidity often focuses on measures of the current cost of trading. However, investors and policy-makers also care about what would happen to liquidity in the event of an adverse shock. If liquidity were to deteriorate rapidly at times when investors were seeking to rebalance portfolios, this could amplify the effects of shocks to the financial system even if liquidity is high most of the time. We examine the potential for such fragility of liquidity in the Treasury market. We show that a reduction in the availability of resting orders to trade ("market depth") increases liquidity fragility, likely because lower depth increases the dependence of low trading costs on prompt replenishment of resting orders. Our results apply to all major benchmark Treasury securities individually, which enables us to establish analogous conclusions for market-wide liquidity fragility.

Keywords: liquidity, fragility, Treasury market, price impact, volatility, market depth, hidden Markov model

DOI: https://doi.org/10.17016/FEDS.2025.014

Abstract:

We develop a model of the repo market with strategic interactions among dealers who compete for funding in a decentralized over-the-counter market and have access to a centrally cleared interdealer market. We show that such “wiring” of the repo market combined with imperfect competition in dealer funding results in market inefficiencies and instability. The model allows us to disentangle supply and demand factors, and we use these factors to estimate supply and demand elasticities. Our estimates suggest that the instability of the market in September 2019 was driven by a large supply shock facing inelastic dealer funding demand, amplified by strategic interactions among dealers. We evaluate different interventions for market functioning and efficiency, including the Standing Repo Facility.

Keywords: market efficiency, over-the-counter markets, Standing Repo Facility, centrally cleared markets, networked markets, repo market

DOI: https://doi.org/10.17016/FEDS.2025.013

Abstract:

Modern payments systems are critical infrastructure for the US and global economy, and they all utilize computing systems to facilitate transactions. These computing systems can be vulnerable to failures and an outage of a payment system could cause a serious ripple effect throughout the economy it supports.

Commonly used designs in existing distributed computer systems often lack a built-in defense against certain types of failures (e.g., malicious attacks and silent data corruption) and rely on preventing these failures from happening in the first place via techniques external to the system itself. These computer system failures can cause downtime in the systems (e.g., modern payments systems) that rely on them. Byzantine Fault Tolerant (BFT) systems have the potential of improved resiliency and security. BFT systems can tolerate a larger range of failure modes than contemporary designs but suffer from performance challenges. Our work sought to design and evaluate a scalable BFT architecture and compare its properties to other database architectures used in payments infrastructure. This analysis is intended to better understand technical tradeoffs and is agnostic to broader policy or operational considerations.

In this paper, we present Heraclius, a parallelizable leader-based, BFT key-value store that could be extended for use in payment systems. Heraclius executes transactions in parallel to achieve high transaction volumes. We analyze the scalability of the protocol, bottlenecks and potential solutions to the bottlenecks. We ran the prototype implementation with up to 256 nodes and achieved a transactional volume of 110 thousand operations per second with a transaction latency 0.2 seconds.

Keywords: BFT systems, Payment systems

DOI: https://doi.org/10.17016/FEDS.2025.012

Abstract:

This paper introduces a novel measure of firm-level Artificial Intelligence (AI) Research & Development—the AIR Index—derived from the semantic similarity between earnings conference call transcripts and leading AI research papers. The AIR Index varies widely across industries, with sustained strength in computer and electronic manufacturing, and accelerating growth in computing infrastructure and educational services seen after the introduction of ChatGPT in November 2022. I find that the AIR Index is associated with an immediate increase in Tobin's Q and can help explain the cross-section of cumulative absolute returns following the conference call, suggestive of investors valuing substantive AI discussions in the near-term. A sharp rise in the AIR Index leads to persistent increases in year-over-year capex growth, lasting about a year before tapering off, indicative of the life cycle of AI-induced capital deepening. However, I find no significant effects of AI R&D on productivity or employment. Using industry level survey data from Census, I find that recent growth in the AIR Index correlates with broader AI adoption trends. The positive association of the AIR Index with capex and valuation holds across previous time periods, suggesting that Generative AI may be the latest form of an ongoing technical innovation process, albeit at an accelerated pace.

Keywords: artificial intelligence, capital expenditure, corporate finance, natural language processing, productivity

DOI: https://doi.org/10.17016/FEDS.2025.011

Abstract:

The annual Survey of Household Economics and Decisionmaking (SHED) receives substantial research attention for topics related to household finances and economic well-being. To assess the reliability of data from the SHED, we compare aggregate statistics from the SHED with prominent, nationally representative surveys that use different survey designs, sample methodologies, and interview modes. Specifically, we compare recent statistics from the SHED with similar questions in U.S. Census Bureau surveys, including the Current Population Survey (CPS) and the American Community Survey (ACS). Overall, aggregate responses to the SHED benchmark well against nationally representative surveys, particularly for questions with nearly identical wording. However, we also note that subtle differences in wording of survey questions for broadly similar indicators can prompt moderate variations across data sources.

Keywords: SHED, Survey methodology, Census Bureau: CPS, ACS, Demographic, Employment, Homeownership, Health insurance, Food insufficiency

DOI: https://doi.org/10.17016/FEDS.2025.010

Abstract:

Regulating bank risk-taking is challenging since banks know more than regulators about the risks of their portfolios and can make adjustments to game regulations. To address this problem, I build a tractable model that incorporates this information asymmetry. The model is flexible enough to encompass many regulatory tools, although I focus on taxes. These taxes could also be interpreted as reflecting the shadow costs of other regulations, such as capital requirements. I show that linear risk-sensitive taxes should not generally be set more conservatively to address asymmetric information. I further show the efficacy of three regulatory tools: (1) not disclosing taxes to banks until after portfolio selection, (2) nonlinear taxes that respond to information contained in banks' portfolio choice, and (3) taxes on banks' realized pro ts that incentivize banks to reduce risk.

DOI: https://doi.org/10.17016/FEDS.2025.009

Abstract:

The Medicaid continuous enrollment provision, which ensured uninterrupted coverage for beneficiaries during the COVID-19 pandemic, was ended in March 2023. This unwinding process has led to large-scale Medicaid disenrollments, as states resumed their standard renewal process to evaluate enrolled individuals' eligibility status. Our analysis investigates whether resumption of states' renewal process has led to an increase in the risk of becoming uninsured for adults aged under 65 and affected their household economic well-being. Using state-month variation in the timing of the first round of disenrollments, we first document a 6-12 percent decline in total Medicaid enrollments after states resumed their renewal process. Next, based on nationally representative samples of adults younger than age 65, we do not find statistically relevant effects on the probability of being without any health coverage. However, looking at different demographic groups, we see a one percentage point increase in the likelihood of becoming uninsured for adults who have a college education but do not have a bachelor's or higher degree.

Keywords: Continuous enrollment provision; COVID-19 pandemic; Medicaid; health insurance; policy analysis

DOI: https://doi.org/10.17016/FEDS.2025.008

Abstract:

The equity market’s reaction to macroeconomic news is consistent with the propagation of news into the real economy. We embody all the macro news in an activity news index and a price news index that together explain 34% of the quarterly stock price returns variation. When those indexes capture a stream of favorable macroeconomic surprises, publicly traded firms experience increases in revenues, profitability, financing, and investment activities. The firm-level results lead up to an expansion of the real side of the whole U.S. economy. Our findings, taken together, show that stock prices’ reactions to macro news have a strong association with firm-level and economy-wide growth.

Keywords: Macroeconomic News, Equity Markets, Real Activity

DOI: https://doi.org/10.17016/FEDS.2025.007

Abstract:

The correlation of the spatial distribution of banking exposures with changes in spatial patterns of economic activity (e.g., internal migration, changes in agglomeration patterns, climate change, etc.) may have financial stability implications. We therefore study the spatial distribution of large U.S. banks' commercial and industrial (C&I) lending portfolios. We construct a novel dataset that augments FR Y-14Q regulatory data with borrower microdata for a more granular understanding of where banks' exposures are located by looking beyond headquarters to the location of facilities. We find that banks are exposed to almost all U.S. counties, with clustered exposure in certain geographies. We then use our dataset for a climate-related application by analyzing what fraction of C&I loans have been extended to firms that operate in areas vulnerable to physical risks, identifying, for example, counties where both (i) banks are highly exposed via their lending portfolios, and (ii) physical risks have historically resulted in large losses. Results of this kind can help inform risk management and be used to improve resilience to future stresses.

Keywords: bank lending to firms, climate risks, mapping of firm facilities, spatial lending patterns

DOI: https://doi.org/10.17016/FEDS.2025.006

Abstract:

Using a novel data collection, we examine the impact of the Volcker Rule on trading revenue of the 21 largest U.S. trading firms during the 100 day stress period centered on the COVID-19 financial crisis. We find that despite the market volatility, trading profits were consistent with volume-driven fees, commissions, and widening of the bid-ask spread. This work adds to the growing body of evidence that a consequence of the Volcker Rule on firm revenue associated with trading is increased financial stability and decreased risk exposure to market shocks.

Keywords: Bank Trading, Supervision and regulation of financial markets and institutions, Systemic Risk, Volcker Rule

DOI: https://doi.org/10.17016/FEDS.2025.005

Abstract:

This paper studies the estimation and inference of time-varying impulse response functions in structural vector autoregressions (SVARs) identified with external instruments. Building on kernel estimators that allow for nonparametric time variation, we derive the asymptotic distributions of the relevant quantities. Our estimators are simple and computationally trivial and allow for potentially weak instruments. Simulations suggest satisfactory empirical coverage even in relatively small samples as long as the underlying parameter instabilities are sufficiently smooth. We illustrate the methods by studying the time-varying effects of global oil supply news shocks on US industrial production.

Keywords: Time-varying parameters, Nonparametric estimation, Structural VAR, External instruments, Weak instruments, Oil supply news shocks, Impulse response analysis

DOI: https://doi.org/10.17016/FEDS.2025.004

Abstract:

In this paper, we assemble the most comprehensive dataset to date on the characteristics of colleges and universities, including dates of operation, institutional setting, student body, staff, and finance data from 2002 to 2023. We provide an extensive description of what is known and unknown about closed colleges compared with institutions that did not close. Using this data, we first develop a series of predictive models of financial distress, utilizing factors like operational revenue/expense patterns, sources of revenue, metrics of liquidity and leverage, enrollment/staff patterns, and prior signs of significant financial strain. We benchmark these models against existing federal government screening mechanisms such as financial responsibility scores and heightened cash monitoring. We document a high degree of missing data among colleges that eventually close and show that this is a key impediment to identifying at risk institutions. We then show that modern machine learning techniques, combined with richer data, are far more effective at predicting college closures than linear probability models, and considerably more effective than existing accountability metrics. Our preferred model, which combines an off-the-shelf machine learning algorithm with the richest set of explanatory variables, can significantly improve predictive accuracy even for institutions with complete data, but is particularly helpful for predicting instances of financial distress for institutions with spotty data. Finally, we conduct simulations using our estimates to contemplate likely increases in future closures, showing that enrollment challenges resulting from an impending demographic cliff are likely to significantly increase annual college closures for reasonable scenarios.

Keywords: higher education, college, university, enrollment, tuition, revenue, budget, closure, fiscal challenge, demographic cliff

DOI: https://doi.org/10.17016/FEDS.2025.003

Abstract:

Using inflation swap prices, we study how changes in expected inflation affect firm-level credit spreads and equity returns, and uncover evidence of a time-varying inflation sensitivity. In times of "good inflation," when inflation news is perceived by investors to be more positively correlated with real economic growth, movements in expected inflation substantially reduce corporate credit spreads and raise equity valuations. Meanwhile in times of "bad inflation," these effects are attenuated and the opposite can take place. These dynamics naturally arise in an equilibrium asset pricing model with a time-varying inflation-growth relationship and persistent macroeconomic expectations.

Keywords: Inflation Sensitivity, Time Variation, Asset Prices, Stock-Bond Correlation

DOI: https://doi.org/10.17016/FEDS.2025.002

Abstract:

Replacing faulty measurements with missing values can suppress outlier-induced distortions in state-space inference. We therefore put forward two complementary methods for enhanced outlier-robust filtering and forecasting: supervised missing data substitution (MD) upon exceeding a Huber threshold, and unsupervised missing data substitution via exogenous randomization (RMDX).

Our supervised method, MD, is designed to improve performance of existing Huber-based linear filters known to lose optimality when outliers of the same sign are clustered in time rather than arriving independently. The unsupervised method, RMDX, further aims to suppress smaller outliers whose size may fall below the Huber detection threshold. To this end, RMDX averages filtered or forecasted targets based on measurement series with randomly induced subsets of missing data at an exogenously set randomization rate. This gives rise to regularization and bias-variance trade-off as a function of the missing data randomization rate, which can be set optimally using standard cross-validation techniques.

We validate through Monte Carlo simulations that both methods for missing data substitution can significantly improve robust filtering, especially when combined together. As further empirical validation, we document consistently attractive performance in linear models for forecasting inflation trends prone to clustering of measurement outliers.

Keywords: Kalman filter, outliers, Huberization, missing data, randomization

DOI: https://doi.org/10.17016/FEDS.2025.001