Ivan Fernandez-Val, Wayne Yuan Gao, Yuan Liao, Francis Vella
arXiv 8 Feb 2022 · Econometrics · 4 citations (OpenAlex)
arXiv:2202.04154 · PDF · DOI · OpenAlex · Extracted main text
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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The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.
| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | Arellano, M., Blundell, R. and Bonhomme, S (2017) Earnings and consumption dynamics: a nonlinear panel data framework | 0.874 | 6 | 2 | 100% |
| 2 | Dhaene, G. and Jochmans, K (2015) Split-panel jackknife estimation of fixed-effect models | 0.737 | 3 | 3 | 67% |
| 3 | Fernández-Val, I. and Weidner, M (2016) Individual and time effects in nonlinear panel models with large n, t | 0.737 | 3 | 2 | 100% |
| 4 | Hu, Y., Moffitt, R. and Sasaki, Y (2019) Semiparametric estimation of the canonical permanent-transitory model of earnings dynamics | 0.737 | 3 | 2 | 100% |
| 5 | Okui, R. and Yanagi, T (2019) Panel data analysis with heterogeneous dynamics | 0.644 | 3 | 2 | 67% |
| 6 | Lee, W (2025) Identification and estimation of dynamic random coefficient models. https://arxiv.org/abs/2505.01600 | 0.644 | 2 | 2 | 100% |
| 7 | Lillard, L. A. and Willis, R. J (1978) Dynamic aspects of earning mobility | 0.644 | 2 | 2 | 100% |
| 8 | Browning, M. and Carro, J (2007) Heterogeneity and microeconometrics modeling | 0.585 | 3 | 1 | 100% |
| 9 | Liao, Y. and Yang, X (2018) Uniform inference for characteristic effects of large continuous-time linear models self | 0.585 | 3 | 1 | 100% |
| 10 | Arellano, M., Blundell, R. and Bonhomme, S (2018) Nonlinear persistence and partial insurance: income and consumption dynamics in the psid | 0.511 | 2 | 1 | 100% |
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