Identification and Estimation of Continuous-Time Job Search Models with Preference Shocks

This paper applies some of the key insights of dynamic discrete choice models to continuous-time job search models. We propose a novel framework that incorporates preference shocks into search models, resulting in a tight connection between value functions and conditional choice probabilities. Including preference shocks allows us to establish constructive identification of all the model parameters. Our method also makes it possible to estimate rich nonstationary job search models in a simple and tractable way, without having to solve any differential equations. We apply our framework to rich longitudinal data from Hungarian administrative records, allowing for nonstationarities in offer arrival rates, wage offers, and in the flow payoff of unemployment. Longer unemployment durations are associated with substantially worse wage offers and lower offer arrival rates, which results in accepted wages falling over time.