Testing for State Dependence with Time-Variant Transition Probabilities

Abstract

We consider the identification of state dependence in a dynamic Logit model with timevariant transition probabilities and an arbitrary distribution of the unobserved heterogeneity. We derive a simple result that allows us to test for the presence of state dependence in this model. Monte Carlo evidence suggests that this test has desirable properties even when there are some violations of the model’s assumptions. We also consider alternative tests for state dependence that will have desirable properties only when the transition probabilities do not depend on time and provide evidence that there is an "acceptable" range in which ignoring time-dependence does not matter too much. We conclude with an application to the Barker Hypothesis.Dynamic Panel Data Models, State Dependence, Health