Publication: Dynamic binary outcome models with maximal heterogeneity
Loading...
Identifiers
Publication date
2009-02
Defense date
Authors
Advisors
Tutors
Journal Title
Journal ISSN
Volume Title
Publisher
Impact
Export
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.
Description
Keywords
Discrete choice, Markov processes, Nonparametric identification, Unemployment dynamics