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Identification of Average Marginal Effects in Fixed Effects Dynamic Discrete Choice Models

Victor Aguirregabiria () and Jesus Carro ()

Working Papers from University of Toronto, Department of Economics

Abstract: 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 the identification of 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 logic model, and for T as small as four, we prove the point identification of different AMEs, including causal effects of changes in the lagged dependent variable or in the duration in last choice. 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.

Keywords: Identification; Average marginal effects; Fixed effects models; Panel data; Dynamic discrete choice; State dependence; Dynamic demand of differentiated products (search for similar items in EconPapers)
JEL-codes: C23 C25 C51 (search for similar items in EconPapers)
Pages: Unknown pages
Date: 2021-07-08
New Economics Papers: this item is included in nep-dcm, nep-ecm and nep-ore
References: View references in EconPapers View complete reference list from CitEc
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Working Paper: Identification of Average Marginal Effects in Fixed Effects Dynamic Discrete Choice Models (2021) Downloads
Working Paper: Identification of Average Marginal Effects in Fixed Effects Dynamic Discrete Choice Models (2021) Downloads
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