arXiv 24 Feb 2022 · Econometrics · publishedEconometric Theory (2024) · 2 citations (OpenAlex)
arXiv:2202.12062 · PDF · DOI · OpenAlex · Extracted main text
We propose a new approach to the semiparametric analysis of panel data binary choice models with fixed effects and dynamics (lagged dependent variables). The model we consider has the same random utility framework as in Honore and Kyriazidou (2000). We demonstrate that, with additional serial dependence conditions on the process of deterministic utility and tail restrictions on the error distribution, the (point) identification of the model can proceed in two steps, and only requires matching the value of an index function of explanatory variables over time, as opposed to that of each explanatory variable. Our identification approach motivates an easily implementable, two-step maximum score (2SMS) procedure -- producing estimators whose rates of convergence, in contrast to Honore and Kyriazidou's (2000) methods, are independent of the model dimension. We then derive the asymptotic properties of the 2SMS procedure and propose bootstrap-based distributional approximations for inference. Monte Carlo evidence indicates that our procedure performs adequately in finite samples.
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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 | Honoré, B. E. and E. Kyriazidou (2000) Panel data discrete choice models with lagged dependent variables | 1.000 | 7 | 3 | 100% |
| 2 | Manski, C. F (1987) Semiparametric analysis of random effects linear models from binary panel data | 0.928 | 4 | 3 | 100% |
| 3 | Manski, C. F (1985) Semiparametric analysis of discrete response | 0.843 | 3 | 3 | 100% |
| 4 | Honoré, B. E. and A. Lewbel (2002) Semiparametric binary choice panel data models without strictly exogeneous regressors | 0.811 | 4 | 2 | 100% |
| 5 | Honoré, B. E. and E. Tamer (2006) Bounds on parameters in panel dynamic discrete choice models | 0.811 | 4 | 2 | 100% |
| 6 | Hong, H. and J. Li (2020) The numerical bootstrap | 0.794 | 10 | 3 | 50% |
| 7 | Kim, J. and D. Pollard (1990) Cube root asymptotics | 0.794 | 8 | 5 | 50% |
| 8 | Chen, S., S. Khan, and X. Tang (2019) Exclusion Restrictions in Dynamic Binary Choice Panel Data Models: Comment on “Semiparametric Binary Choice Panel Data Models Wi… | 0.737 | 3 | 2 | 100% |
| 9 | Williams, B (2019) Nonparametric identification of discrete choice models with lagged dependent variables | 0.737 | 3 | 2 | 100% |
| 10 | Altonji, J. G. and R. L. Matzkin (2005) Cross section and panel data estimators for nonseparable models with endogenous regressors | 0.644 | 2 | 2 | 100% |
Showing the top 10 of 61 scored citations.
arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.
| Citing paper | Intensity | Mentions | Sections | |
|---|---|---|---|---|
| 1 | Revisiting Panel Data Discrete Choice Models with Lagged Dependent Variables | 0.965 | 10 | 5 |
| 2 | Semiparametric Dynamic Logit Model with Endogenous Networks | 0.644 | 2 | 2 |