Sufficient Statistics for Unobserved Heterogeneity in Structural Dynamic Logit Models

Victor Aguirregabiria (), Jiaying Gu and Yao Luo

Papers from arXiv.org

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.

Date: 2018-05
New Economics Papers: this item is included in nep-com, nep-dcm and nep-upt
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Citations: View citations in EconPapers (9)

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Related works:
Journal Article: Sufficient statistics for unobserved heterogeneity in structural dynamic logit models (2021)
Working Paper: Sufficient Statistics for Unobserved Heterogeneity in Structural Dynamic Logit Models (2018)
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Persistent link: https://EconPapers.repec.org/RePEc:arx:papers:1805.04048

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