Identification and Estimation of Nonstationary Dynamic Binary Choice Models

In a dynamic binary choice model that allows for general forms of nonstationarity, we transform the identification of the flow utility parameters into the solution of a (linear) system of equations. The identification of the parameters, therefore, follows the usual argument for linear GMM. In particular, we show that the state transition distribution is not essential for the identification and estimation of the parameters. We propose a three-step conditional-choice-probability-based semiparametric estimator that bypasses estimation of and simulating from the state transition distribution. Simulation experiments show that our estimator gives comparable or better estimates than a competitor estimator, yet it requires fewer assumptions in certain scenarios, is substantially easier to implement, and is computationally much less demanding. The asymptotic distribution of the estimator is provided, and the sensitivity of the estimator to a key assumption is also examined.