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Working Paper
Important Factors Determining Fintech Loan Default: Evidence from the LendingClub Consumer Platform
Jagtiani, Julapa; Croux, Christophe; Korivi, Tarunsai; Vulanovic, Milos (2020-04-16)
This study examines key default determinants of fintech loans, using loan-level data from the LendingClub consumer platform during 2007–2018. We identify a robust set of contractual loan characteristics, borrower characteristics, and macroeconomic variables that are important in determining default. We find an important role of alternative data in determining loan default, even after controlling for the obvious risk characteristics and the local economic factors. The results are robust to different empirical approaches. We also find that homeownership and occupation are important factors in ...
Working Papers , Paper 20-15

Report
Latent Heterogeneity in the Marginal Propensity to Consume
Melcangi, Davide; Lewis, Daniel J.; Pilossoph, Laura (2019-11-01)
We estimate the unconditional distribution of the marginal propensity to consume (MPC) using clustering regression and the 2008 stimulus payments. Since we do not measure heterogeneity as the variation of MPCs with observables, we can recover the full distribution of MPCs. Households spent at least one quarter of the rebate, and individual households used rebates for different goods. While many observables are individually correlated with our estimated MPCs, these relationships disappear when tested jointly, except for nonsalary income and the average propensity to consume. Household ...
Staff Reports , Paper 902

Working Paper
Theory Meets Textual Analysis: Measuring Firm-Level Labor Cost Pressures and Inflation Pass-Through
Kalyani, Aakash; Ozkan, Serdar (2026-01-30)
We develop a novel measure of firm-level marginal labor cost and investigate its inflation pass-through. We apply textual analysis to earnings calls to identify labordiscussions. Leveraging cost-minimization theory that firms equate marginal revenue products across variable inputs, we regress intermediate input revenue shares on labor discussion intensity to recover marginal labor cost shocks. This theory-based approach aggregates multidimensional qualitative information into a single measure. Our aggregate index outperforms conventional slack variables in forecasting inflation. Industry ...
Working Papers , Paper 2025-021

Working Paper
Sellin' in the Rain: Weather, Climate, and Retail Sales
Roth Tran, Brigitte (2022-01-21)
I apply a novel machine-learning based “weather index” method to daily store- level sales data for a national apparel and sporting goods brand to examine short-run responses to weather and long-run adaptation to climate. I find that even when considering potentially offsetting shifts of sales between outdoor and indoor stores, to the firm's website, or over time, weather has significant persistent effects on sales. This suggests that weather may increase sales volatility as more severe weather shocks be- come more frequent under climate change. Consistent with adaptation to climate, I ...
Working Paper Series , Paper 2022-02

Working Paper
Skill and Efficiency in the U.S. Mutual Fund Industry
Oh, Dong Hwan; Patton, Andrew J. (2026-03-25)
We propose a new measure of mutual fund manager ability: "efficiency" is the ability to accrue the risk premium associated with a risk factor. The familiar abnormal return, or alpha, is shown to be the sum of two distinct measures of ability: "aggregate efficiency" which is the beta-weighted sum of the fund's (in)efficiencies across risk factors, and "skill," the component that is unrelated to factor exposures. Using a panel of U.S. equity mutual fund returns from 1999-2023, we document significant heterogeneity in mutual fund manager skill and efficiency. We employ regression trees and their ...
Finance and Economics Discussion Series , Paper 2026-032

Working Paper
Finding Needles in Haystacks: Multiple-Imputation Record Linkage Using Machine Learning
Abowd, John M.; Abramowitz, Joelle Hillary; Levenstein, Margaret Catherine; McCue, Kristin; Patki, Dhiren; Raghunathan, Trivellore; Rodgers, Ann Michelle; Shapiro, Matthew D.; Wasi, Nada; Zinsser, Dawn (2021-10-01)
This paper considers the problem of record linkage between a household-level survey and an establishment-level frame in the absence of unique identifiers. Linkage between frames in this setting is challenging because the distribution of employment across establishments is highly skewed. To address these difficulties, this paper develops a probabilistic record linkage methodology that combines machine learning (ML) with multiple imputation (MI). This ML-MI methodology is applied to link survey respondents in the Health and Retirement Study to their workplaces in the Census Business Register. ...
Working Papers , Paper 22-11

Working Paper
The Anatomy of Out-of-Sample Forecasting Accuracy
Rapach, David E.; Borup, Daniel; Goulet Coulombe, Philippe; Montes Schütte, Erik Christian; Schwenk-Nebbe, Sander (2022-11-07)
We develop metrics based on Shapley values for interpreting time-series forecasting models, including“black-box” models from machine learning. Our metrics are model agnostic, so that they are applicable to any model (linear or nonlinear, parametric or nonparametric). Two of the metrics, iShapley-VI and oShapley-VI, measure the importance of individual predictors in fitted models for explaining the in-sample and out-of-sample predicted target values, respectively. The third metric is the performance-based Shapley value (PBSV), our main methodological contribution. PBSV measures the ...
FRB Atlanta Working Paper , Paper 2022-16

Working Paper
The Anatomy of Out-of-Sample Forecasting Accuracy
Borup, Daniel; Goulet Coulombe, Philippe; Rapach, David E.; Montes Schütte, Erik Christian; Schwenk-Nebbe, Sander (2024-02-21)
We introduce the performance-based Shapley value (PBSV) to measure the contributions of individual predictors to the out-of-sample loss for time-series forecasting models. Our new metric allows a researcher to anatomize out-of-sample forecasting accuracy, thereby providing valuable information for interpreting time-series forecasting models. The PBSV is model agnostic—so it can be applied to any forecasting model, including "black box" models in machine learning, and it can be used for any loss function. We also develop the TS-Shapley-VI, a version of the conventional Shapley value that ...
FRB Atlanta Working Paper , Paper 2022-16b

Working Paper
A New Tool for Robust Estimation and Identification of Unusual Data Points
Verbrugge, Randal; Garciga, Christian (2020-03-05)
Most consistent estimators are what Müller (2007) terms “highly fragile”: prone to total breakdown in the presence of a handful of unusual data points. This compromises inference. Robust estimation is a (seldom-used) solution, but commonly used methods have drawbacks. In this paper, building on methods that are relatively unknown in economics, we provide a new tool for robust estimates of mean and covariance, useful both for robust estimation and for detection of unusual data points. It is relatively fast and useful for large data sets. Our performance testing indicates that our baseline ...
Working Papers , Paper 20-08

Working Paper
Alternative Methods for Studying Consumer Payment Choice
Shy, Oz (2020-06-23)
Using machine learning techniques applied to consumer diary survey data, the author of this working paper examines methods for studying consumer payment choice. These techniques, especially when paired with regression analyses, provide useful information for understanding and predicting the payment choices consumers make.
FRB Atlanta Working Paper , Paper 2020-8

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