Testing for state dependence in binary panel data with individual covariates

Francesco Bartolucci, Valentina Nigro () and Claudia Pigini

MPRA Paper from University Library of Munich, Germany

Abstract: We propose a test for state dependence in binary panel data under the dynamic logit model with individual covariates. For this aim, we rely on a quadratic exponential model in which the association between the response variables is accounted for in a different way with respect to more standard formulations. The level of association is measured by a single parameter that may be estimated by a conditional maximum likelihood approach. Under the dynamic logit model, the conditional estimator of this parameter converges to zero when the hypothesis of absence of state dependence is true. This allows us to implement a Wald test for this hypothesis which may be very simply performed and attains the nominal significance level under any structure of the individual covariates. Through an extensive simulation study, we find that our test has good finite sample properties and it is more robust to the presence of (autocorrelated) covariates in the model specification in comparison with other existing testing procedures for state dependence. The test is illustrated by an application based on data coming from the Panel Study of Income Dynamics.

Keywords: conditional inference; dynamic logit model; quadratic exponential model; Wald test (search for similar items in EconPapers)
JEL-codes: C12 C23 (search for similar items in EconPapers)
Date: 2013-07-11
New Economics Papers: this item is included in nep-dcm and nep-ecm
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (5)

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