DP13240 Linear IV Regression Estimators for Structural Dynamic Discrete Choice Models
In structural dynamic discrete choice models, the presence of serially correlated unob-
served states and state variables that are measured with error may lead to biased parameter
estimates and misleading inference. In this paper, we show that instrumental variables can
address these issues, as long as measurement problems involve 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 computationally light. We provide
constructive identification arguments to identify the model primitives, and establish the con-
sistency and asymptotic normality of the estimator. A Monte Carlo study demonstrates the
good finite-sample performance of the ECCP estimator in the context of a dynamic demand
model for durable goods.
