Working Paper: CEPR ID: DP12930
Authors: Victor Aguirregabiria; Jiaying Gu; Yao Luo
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: Panel Data; Discrete Choice Models; Dynamic Structural Models; Fixed Effects; Unobserved Heterogeneity; Structural State Dependence; Identification; Sufficient Statistic
JEL Codes: C23; C25; C41; C51; C61
Edges that are evidenced by causal inference methods are in orange, and the rest are in light blue.
| Cause | Effect |
|---|---|
| sufficient statistic derived (C29) | controls for unobserved heterogeneity in both current utility and continuation value (C34) |
| sufficient statistic controls for unobserved heterogeneity (C20) | identification of structural parameters (C51) |
| distribution of time-varying unobservables follows a logistic distribution (C46) | identification of structural parameters (C51) |
| unobserved heterogeneity interacts with current utility and continuation value (D80) | identification of structural parameters in models with endogenous state variables (C51) |
| minimal sufficient statistic (C20) | ensures conditional likelihood function remains dependent on structural parameters (C51) |
| methodology allows for identification of parameters (C50) | captures true state dependence (C32) |
| identification claims (F20) | extend to various economic applications (A12) |