Linear IV Regression Estimators for Structural Dynamic Discrete Choice Models

NBER Working Paper No. 25134

Issued in October 2018, Revised in February 2020

NBER Program(s):Economics of Education, Health Economics, Industrial Organization, Public Economics



In structural dynamic discrete choice models, unobserved and mis-measured state variables may lead to biased parameter estimates and misleading inference. In this paper, we show that instrumental variables can address such measurement problems when they relate to state variables that evolve exogenously from the perspective of individual agents (i.e., market-level states). We define a class of linear instrumental variables estimators that rely on Euler equations expressed in terms of conditional choice probabilities (ECCP estimators). These estimators do not require observing or modeling the agent's entire information set, nor solving or simulating a dynamic program. As such, they are simple to implement and computation- ally light. We provide constructive arguments for the identification of model primitives, and establish the estimator's consistency and asymptotic normality. Four applied examples serve to illustrate the ECCP approach's implementation, advantages, and limitations: dynamic demand for durable goods, agricultural land use change, technology adoption, and dynamic labor supply. We illustrate the estimator's good finite-sample performance in a Monte Carlo study, and we estimate a labor supply model empirically for taxi drivers in New York City.

Acknowledgments

Machine-readable bibliographic record - MARC, RIS, BibTeX

Document Object Identifier (DOI): 10.3386/w25134

Published: Myrto Kalouptsidi & Paul T. Scott & Eduardo Souza-Rodrigues, 2020. "Linear IV regression estimators for structural dynamic discrete choice models," Journal of Econometrics, .