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Dynamic Heterogeneous Distribution Regression Panel Models, with an Application to Labor Income Processes
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Dynamic Heterogeneous Distribution Regression Panel Models, with an Application to Labor Income Processes

Ivan Fernandez-Val, Wayne Yuan Gao, Yuan Liao and Francis Vella
ArXiv.org
Cornell University
07/29/2025
DOI: 10.48550/arxiv.2202.04154
url
https://doi.org/10.48550/arxiv.2202.04154View
Preprint (Author's original)This preprint has not been evaluated by subject experts through peer review. Preprints may undergo extensive changes and/or become peer-reviewed journal articles. Open Access

Abstract

We introduce a dynamic distribution regression panel data model with heterogeneous coefficients across units. The objects of primary interest are functionals of these coefficients, including predicted one-step-ahead and stationary cross-sectional distributions of the outcome variable. Coefficients and their functionals are estimated via fixed effect methods. We investigate how these functionals vary in response to counterfactual changes in initial conditions or covariate values. We also identify a uniformity problem related to the robustness of inference to the unknown degree of coefficient heterogeneity, and propose a cross-sectional bootstrap method for uniformly valid inference on function-valued objects. We showcase the utility of our approach through an empirical application to individual income dynamics. Employing the annual Panel Study of Income Dynamics data, we establish the presence of substantial coefficient heterogeneity. We then highlight some important empirical questions that our methodology can address. First, we quantify the impact of a negative labor income shock on the distribution of future labor income.

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