This paper proposes a pairwise likelihood estimator based on an analytic approximation method for the random effects probit model. It is widely known that the standard approach for the random effects probit model relies on numerical integration and that its likelihood function does not have a closed form. When the number of time periods or the serial correlation across periods is large, the resulting estimator is likely to become biased. This study derives an analytic approximation for the likelihood function of one pair of time periods without relying on typical numerical-integral procedures. We then apply this formula in a pairwise likelihood estimation procedure to derive our estimator, which is obtained as the product of the analytic approximation of the likelihood function for all possible pairs of time periods. A simulation study is conducted for the comparison of our proposed estimator with the estimators for the pooled probit model and Gaussian quadrature procedure. The evidence shows that our proposed estimator enjoys desirable asymptotic properties. In addition, compared to the estimator based on the Gaussian quadrature procedure, our proposed estimator exhibits comparable performances in all the configurations considered in the simulation study and shows superiority for the cases of a large number of time periods and high serial correlation across periods. We apply our proposed estimator to British Household Panel Survey data so as to characterize the trend of working probabilities.
Discrete choice Panel probit model Error function
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Heckman JJ, Willis RJ (1976) Estimation of a stochastic model of reproduction: an econometric approach. In: Terleckyj NE (ed) Household production and consumption. National Bureau of Economic Research, New YorkGoogle Scholar
Kuk AYC, Nott DJ (2000) A pairwise likelihood approach to analyzing correlated binary data. Stat Probab Lett 47:329–335CrossRefzbMATHGoogle Scholar
Stroud A, Secrest D (1966) Gaussian quadrature formulas. Prentice-Hall, Englewood CliffszbMATHGoogle Scholar
Tsay WJ, Huang CJ, Fu TT, Ho IL (2013) A simple closed-form approximation for the cumulative distribution function of the composite error of stochastic frontier models. J Product Anal 39:259–269CrossRefGoogle Scholar