Finance and Economics Discussion Series (FEDS)
Staff working papers in the Finance and Economics Discussion Series (FEDS) investigate a broad range of issues in economics and finance, with a focus on the U.S. economy and domestic financial markets.
Shedding Light on Survey Accuracy—A Comparison between SHED and Census Bureau Survey Results
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
Regulating Bank Portfolio Choice Under Asymmetric Information
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
The effect of ending the pandemic-related mandate of continuous Medicaid coverage on health insurance coverage
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
Decoding Equity Market Reactions to Macroeconomic News
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
Spatially Mapping Banks' Commercial & Industrial Loan Exposures: Including an Application to Climate-Related Risks
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
Impact of the Volcker Rule on the Trading Revenue of Largest U.S. Trading Firms During the COVID-19 Crisis Period
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
Nonparametric Time Varying IV-SVARs: Estimation and Inference
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
Predicting College Closures and Financial Distress
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
"Good" Inflation, "Bad" Inflation: Implications for Risky Asset Prices
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
Missing Data Substitution for Enhanced Robust Filtering and Forecasting in Linear State-Space Models
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
Disclaimer: The economic research that is linked from this page represents the views of the authors and does not indicate concurrence either by other members of the Board's staff or by the Board of Governors. The economic research and their conclusions are often preliminary and are circulated to stimulate discussion and critical comment.
The Board values having a staff that conducts research on a wide range of economic topics and that explores a diverse array of perspectives on those topics. The resulting conversations in academia, the economic policy community, and the broader public are important to sharpening our collective thinking.
ISSN 2767-3898 (Online)
ISSN 1936-2854 (Print)