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Dynamic binary outcome models with maximal heterogeneity

  • Martin Browning
  • Jesus M. Carro

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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File URL: http://www.economics.ox.ac.uk/materials/working_papers/paper426.pdf
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Paper provided by University of Oxford, Department of Economics in its series Economics Series Working Papers with number 426.

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Date of creation: 01 Apr 2009
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Handle: RePEc:oxf:wpaper:426
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