Victor Aguirregabiria, Jesus M. Carro
arXiv 12 Jul 2021 · Econometrics · publishedThe Review of Economics and Statistics (2024) · 9 citations (OpenAlex)
arXiv:2107.06141 · PDF · DOI · OpenAlex · Extracted main text
In nonlinear panel data models, fixed effects methods are often criticized because they cannot identify average marginal effects (AMEs) in short panels. The common argument is that identifying AMEs requires knowledge of the distribution of unobserved heterogeneity, but this distribution is not identified in a fixed effects model with a short panel. In this paper, we derive identification results that contradict this argument. In a panel data dynamic logit model, and for $T$ as small as three, we prove the point identification of different AMEs, including causal effects of changes in the lagged dependent variable or the last choice's duration. Our proofs are constructive and provide simple closed-form expressions for the AMEs in terms of probabilities of choice histories. We illustrate our results using Monte Carlo experiments and with an empirical application of a dynamic structural model of consumer brand choice with state dependence.
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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, Muris, and Weidner (2021) Dynamic Ordered Panel Logit Models | 1.000 | 6 | 3 | 100% |
| 2 | Honoré and Kyriazidou (2000) Panel data discrete choice models with lagged dependent variables | 0.874 | 14 | 2 | 100% |
| 3 | Dobronyi, Gu, and Kim (2021) Identification of Dynamic Panel Logit Models with Fixed Effects | 0.874 | 8 | 2 | 100% |
| 4 | Bonhomme (2011) Panel Data, Inverse Problems, and the Estimation of Policy Parameters | 0.874 | 6 | 2 | 100% |
| 5 | Chamberlain (1985) Heterogeneity, omitted variable bias, and duration dependence | 0.874 | 6 | 2 | 100% |
| 6 | Aguirregabiria, Gu, and Luo (2021) Sufficient statistics for unobserved heterogeneity in dynamic structural logit models | 0.843 | 10 | 4 | 60% |
| 7 | Heckman (1981) The incidental parameters problem and the problem of initial conditions in estimating a discrete time - discrete data stochastic… | 0.811 | 4 | 2 | 100% |
| 8 | Honoré and Weidner (2020) Dynamic Panel Logit Models with Fixed Effects | 0.811 | 4 | 2 | 100% |
| 9 | Magnac (2000) Subsidised training and youth employment: distinguishing unobserved heterogeneity from state dependence in labour market histories | 0.811 | 4 | 2 | 100% |
| 10 | Erdem, Imai, and Keane (2003) Brand and quantity choice dynamics under price uncertainty | 0.693 | 8 | 1 | 100% |
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