Pseudo conditional maximum likelihood estimation of the dynamic logit model for binary panel data

Francesco Bartolucci and Valentina Nigro (valentina.nigro@bancaditalia.it)

Journal of Econometrics, 2012, vol. 170, issue 1, 102-116

Abstract: We show how the dynamic logit model for binary panel data may be approximated by a quadratic exponential model. Under the approximating model, simple sufficient statistics exist for the subject-specific parameters introduced to capture the unobserved heterogeneity between subjects. The latter must be distinguished from the state dependence which is accounted for by including the lagged response variable among the regressors. By conditioning on the sufficient statistics, we derive a pseudo conditional likelihood estimator of the structural parameters of the dynamic logit model, which is simple to compute. Asymptotic properties of this estimator are studied in detail. Simulation results show that the estimator is competitive in terms of efficiency with estimators recently proposed in the econometric literature.

Keywords: Log-linear models; Longitudinal data; Pseudo likelihood inference; Quadratic exponential distribution (search for similar items in EconPapers)
JEL-codes: C13 C23 C25 (search for similar items in EconPapers)
Date: 2012
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (29)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:econom:v:170:y:2012:i:1:p:102-116

DOI: 10.1016/j.jeconom.2012.03.004

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