Publication: Dynamic binary outcome models with maximal heterogeneity
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2014-02
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Elsevier
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Abstract
Most econometric schemes to allow for heterogeneity in micro behavior have two drawbacks: they do not fit the data and they rule out interesting economic models. In this paper we consider the time homogeneous first order Markov (HFOM) model that allows for maximal heterogeneity. That is, the modeling of the heterogeneity does not impose anything on the data (except the HFOM assumption for each agent) and it allows for any theory model (that gives a HFOM process for an individual observable variable). 'Maximal' means that the joint distribution of initial values and the transition probabilities is unrestricted. We establish necessary and sufficient conditions for generic local point identification of our heterogeneity structure that are very easy to check, and we show how it depends on the length of the panel. We apply our techniques to a long panel of Danish workers who are very homogeneous in terms of observables. We show that individual unemployment dynamics are very heterogeneous, even for such a homogeneous group. We also show that the impact of cyclical variables on individual unemployment probabilities differs widely across workers. Some workers have unemployment dynamics that are independent of the cycle whereas others are highly-sensitive to macro shocks. (C) 2013 Elsevier B.V. All rights reserved.
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Discrete choice, Markov processes, Nonparametric identification, Unemployment dynamics, Discrete-choice models, Finite mixture-models, Identification, Likelihood, Participation, Dependence
Bibliographic citation
Carro, J. ; Browning, Martin. “Dynamic binary outcome models with maximal heterogeneity”, Journal of Econometrics v. 178, n. 2, pp. 805-823, 2014