Dynamic binary outcome models with maximal heterogeneity

Martin Browning and Jesus Carro

Journal of Econometrics, 2014, vol. 178, issue 2, 805-823

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.

Keywords: Discrete choice; Markov processes; Nonparametric identification; Unemployment dynamics (search for similar items in EconPapers)
JEL-codes: C23 C24 J64 (search for similar items in EconPapers)
Date: 2014
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Citations: View citations in EconPapers (24)

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Related works:
Working Paper: Dynamic binary outcome models with maximal heterogeneity (2009)
Working Paper: Dynamic Binary Outcome Models with Maximal Heterogeneity (2009)
Working Paper: Dynamic binary outcome models with maximal heterogeneity (2009)
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Persistent link: https://EconPapers.repec.org/RePEc:eee:econom:v:178:y:2014:i:2:p:805-823

DOI: 10.1016/j.jeconom.2013.11.005

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