Linear IV Regression Estimators for Structural Dynamic Discrete Choice Models

Working Paper: NBER ID: w25134

Authors: Myrto Kalouptsidi; Paul T. Scott; Eduardo Souza-Rodrigues

Abstract: 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.

Keywords: Instrumental Variables; Dynamic Discrete Choice; Structural Models; Econometrics

JEL Codes: C13; C35; C36; C51; C61

Causal Claims

Cause	Effect
Measurement errors (C20)	underestimating labor supply responses (J20)
Endogeneity issues (C20)	underestimating labor supply responses (J20)
Neglecting endogeneity (C20)	underestimating labor supply responses (J20)
Instrumental variables (C36)	mitigate biases (D91)
ECCP estimators (C51)	consistent parameter estimates (C51)
ECCP estimators (C51)	asymptotically normal parameter estimates (C51)