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Dynamic binary outcome models with maximal heterogeneity

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  • Martin Browning

    ()

  • Jesus M. Carro

    ()

Abstract

Most econometric schemes to allow for heterogeneity in micro behaviour 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 modelling 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 the point identification of our heterogeneity structure and show how it depends on the length of the panel. A feasible ML estimation procedure is developed. Tests for a variety of subsidiary hypotheses such as the assumption that marginal dynamic effects are homogeneous are developed. 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.

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Bibliographic Info

Paper provided by Universidad Carlos III, Departamento de Economía in its series Economics Working Papers with number we091710.

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Date of creation: Feb 2009
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Handle: RePEc:cte:werepe:we091710

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Keywords: Discrete choice; Markov processes; Nonparametric identification; Unemployment dynamics;

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Cited by:
  1. Martin Browning & Jesus M. Carro, 2013. "The Identification of a Mixture of First-Order Binary Markov Chains," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 75(3), pages 455-459, 06.
  2. Stéphane Bonhomme & Elena Manresa, 2012. "Grouped Patterns Of Heterogeneity In Panel Data," Working Papers wp2012_1208, CEMFI.

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