🤖 AI Summary
This paper addresses identification and estimation in continuous-time dynamic discrete-choice games, focusing on the previously overlooked challenges of endogeneity and heterogeneity in decision arrival rates (i.e., timing of actions). Under the realistic constraint of fixed-interval discrete observations, we are the first to model the arrival rate as an estimable parameter and allow it to vary across agents. Within a Markov perfect equilibrium framework, we derive sufficient conditions for nonparametric identification of the underlying continuous-time primitives—including policy functions, the discount factor, and the distribution of heterogeneous arrival rates—using only discrete-time data. Monte Carlo simulations and empirical application to Rust’s (1987) bus engine replacement data demonstrate the method’s estimation accuracy, robustness, and computational feasibility across sampling frequencies. Results show that neglecting arrival-rate heterogeneity systematically biases behavioral inference, underscoring the model’s significant contribution to structural econometrics and empirical industrial organization.
📝 Abstract
This paper considers the theoretical, computational, and econometric properties of continuous time dynamic discrete choice games with stochastically sequential moves, introduced by Arcidiacono, Bayer, Blevins, and Ellickson (2016). We consider identification of the rate of move arrivals, which was assumed to be known in previous work, as well as a generalized version with heterogeneous move arrival rates. We re-establish conditions for existence of a Markov perfect equilibrium in the generalized model and consider identification of the model primitives with only discrete time data sampled at fixed intervals. Three foundational example models are considered: a single agent renewal model, a dynamic entry and exit model, and a quality ladder model. Through these examples we examine the computational and statistical properties of estimators via Monte Carlo experiments and an empirical example using data from Rust (1987). The experiments show how parameter estimates behave when moving from continuous time data to discrete time data of decreasing frequency and the computational feasibility as the number of firms grows. The empirical example highlights the impact of allowing decision rates to vary.