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Sufficient Statistics for Unobserved Heterogeneity in Structural Dynamic Logit Models

Victor Aguirregabiria (), Jiaying Gu and Yao Luo

No 12930, CEPR Discussion Papers from C.E.P.R. Discussion Papers

Abstract: We study the identification and estimation of structural parameters in dynamic panel data logit models where decisions are forward-looking and the joint distribution of unobserved heterogeneity and observable state variables is nonparametric, i.e., fixed-effects model. We consider models with two endogenous state variables: the lagged decision variable, and the time duration in the last choice. This class of models includes as particular cases important economic applications such as models of market entry-exit, occupational choice, machine replacement, inventory and investment decisions, or dynamic demand of differentiated products. The identification of structural parameters requires a sufficient statistic that controls for unobserved heterogeneity not only in current utility but also in the continuation value of the forward-looking decision problem. We obtain the minimal sufficient statistic and prove identification of some structural parameters using a conditional likelihood approach. We apply this estimator to a machine replacement model.

Keywords: dynamic structural models; fixed effects; identification; Panel data discrete choice models; Structural state dependence; Sufficient statistic; Unobserved heterogeneity (search for similar items in EconPapers)
JEL-codes: C23 C25 C41 C51 C61 (search for similar items in EconPapers)
New Economics Papers: this item is included in nep-dcm, nep-ore and nep-upt
Date: 2018-05
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Working Paper: Sufficient Statistics for Unobserved Heterogeneity in Structural Dynamic Logit Models (2018) Downloads
Working Paper: Sufficient Statistics for Unobserved Heterogeneity in Structural Dynamic Logit Models (2018) Downloads
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