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Working Paper
Sample Selection Models Without Exclusion Restrictions: Parameter Heterogeneity and Partial Identification
Honore, Bo E.; Hu, Luojia (2021-07)
This paper studies semiparametric versions of the classical sample selection model (Heckman (1976, 1979)) without exclusion restrictions. We extend the analysis in Honoré and Hu (2020) by allowing for parameter heterogeneity and derive implications of this model. We also consider models that allow for heteroskedasticity and briefly discuss other extensions. The key ideas are illustrated in a simple wage regression for females. We find that the derived implications of a semiparametric version of Heckman's classical sample selection model are consistent with the data for women with no college ...
Working Paper Series , Paper WP 2022-33

Working Paper
Inflation at Risk
López-Salido, J. David; Loria, Francesca (2020-02-13)
We investigate how macroeconomic drivers affect the predictive inflation distribution as well as the probability that inflation will run above or below certain thresholds over the near term. This is what we refer to as Inflation-at-Risk–a measure of the tail risks to the inflation outlook. We find that the recent muted response of the conditional mean of inflation to economic conditions does not convey an adequate representation of the overall pattern of inflation dynamics. Analyzing data from the 1970s reveals ample variability in the conditional predictive distribution of inflation that ...
Finance and Economics Discussion Series , Paper 2020-013

Report
Nonlinear Binscatter Methods
Cattaneo, Matias D.; Crump, Richard K.; Feng, Yingjie; Farrell, Max H. (2024-08-01)
Binscatters are a powerful tool for empirical work in the social, behavioral, and biomedical sciences. Available tools rely on least squares estimation of the conditional mean. We introduce novel binscatter methods based on nonlinear, possibly nonsmooth M-estimation, covering generalized linear, robust, and quantile regression models. We provide theoretical results and practical tools, including optimal bin selection, confidence bands, and statistical tests regarding functional form or shape restrictions. We demonstrate our methods by studying the relationship of income and (lack of) health ...
Staff Reports , Paper 1110

Working Paper
Composition-Adjusted Wage Growth: A Robust Measure from Microdata
Honore, Bo E.; Hu, Luojia (2025-07)
Wage growth is a key indicator of labor market conditions, but common measures often conflate individual wage changes with shifts in workforce composition. This paper develops a composition-adjusted measure of wage growth using nonparametric decomposition and program evaluation methods. The adjusted measure tracks unadjusted growth in stable periods but diverges during disruptions: during the Covid-19 pandemic, wage growth falls from 12% to 6% after adjustment. The method accommodates rich covariates, is robust to data quality issues such as rounding, heaping and top-coding, and enables ...
Working Paper Series , Paper WP 2025-14

Report
The Affordable Care Act and the COVID-19 Pandemic: A Regression Discontinuity Analysis
Pinkovskiy, Maxim L.; Chakrabarti, Rajashri; Nober, William; Meyerson, Lindsay; Avtar, Ruchi (2020-11-01)
Did Medicaid expansion under the Affordable Care Act affect the course of the COVID-19 pandemic? We answer this question using a regression discontinuity design for counties near the borders of states that expanded Medicaid with states that did not. Relevant covariates change continuously across the Medicaid expansion frontier. We find that (1) health insurance changes discontinuously at the frontier, (2) COVID-19 testing is discontinuously larger in Medicaid-expanding states, and (3) the fraction of beds occupied in ICUs is discontinuously smaller in Medicaid-expanding states. We also find ...
Staff Reports , Paper 948

Working Paper
Parallel Trends Forest: Data-Driven Control Sample Selection in Difference-in-Differences
Huh, Yesol; Kling, Matthew (2025-09-29)
This paper introduces parallel trends forest, a novel approach to selecting 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 find control units 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 ...
Finance and Economics Discussion Series , Paper 2025-091

Working Paper
Challenging Demographic Representativeness at State Borders: Implications for Policy Research
Kay, Benjamin S.; Khatiwoda, Albina (2025-03-07)
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 ...
Finance and Economics Discussion Series , Paper 2025-018

Working Paper
Spatial Dependence and Data-Driven Networks of International Banks
Craig, Ben R.; Saldias Zambrana, Martin (2016-12-02)
This paper computes data-driven correlation networks based on the stock returns of international banks and conducts a comprehensive analysis of their topological properties. We first apply spatial-dependence methods to filter the effects of strong common factors and a thresholding procedure to select the significant bilateral correlations. The analysis of topological characteristics of the resulting correlation networks shows many common features that have been documented in the recent literature but were obtained with private information on banks? exposures. Our analysis validates these ...
Working Papers (Old Series) , Paper 1627

Working Paper
Impact of Allowing Sunday Alcohol Sales in Georgia on Employment and Hours
Hotchkiss, Julie L.; Qi, Yanling (2015-11-01)
This paper uses differential timing across counties of the removal of restrictions on Sunday alcohol sales in the state of Georgia to determine whether the change had an impact on employment and hours in the beer, wine, and liquor retail sales industry. A triple-difference (DDD) analysis finds significant relative increases in average weekly hours in the treated industry. There is no significant relative employment increase. The DDD hours result is stronger when we limit the counties removing restrictions to those that border states with significantly higher alcohol excise taxes.
FRB Atlanta Working Paper , Paper 2015-10

Working Paper
A Market Interpretation of Treatment Effects
Minton, Robert; Mulligan, Casey B. (2024-12-20)
Markets, likened to an invisible hand, often appear to contradict econometric assumptions that rule out spillovers of one person’s treatment on another’s outcomes. This paper provides a simple statistical framework highlighting that controls are indirectly affected by the treatment through the market. Further, the effect of the treatment on the treated reveals only part of the consequence for the treated of treating the entire market. When combined with economic theory, our framework leads to a new application of Marshall’s Laws of Derived Demand that relates econometric estimates of ...
Finance and Economics Discussion Series , Paper 2024-096

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