This paper presents new moment conditions for dynamic panel data logit models with fixed effects. After introducing the new moment conditions we explore their implications for identification and estimation of the model parameters that are common to all individuals, and we find that those common model parameters are estimable at root-n rate for many more dynamic panel logit models than was previously known in the literature. A GMM estimator that is based on the new moment conditions is shown to perform well in Monte Carlo simulations and in an empirical illustration to labor force participation.
This paper builds on Bonhomme (2012) to develop a method to systematically construct moment conditions for dynamic panel data logit models with fixed effects. After introducing the moment conditions obtained in this way, we explore their implications for identification and estimation of the model parameters that are common to all individuals, and we find that those common model parameters are estimable at root-n rate for many more dynamic panel logit models than has been appreciated by the existing literature. In the case where the model contains one lagged variable, the moment conditions in Kitazawa (2013, 2016) are transformations of a subset of ours. A GMM estimator that is based on the moment conditions is shown to perform well in Monte Carlo simulations and in an empirical illustration to labor force participation.
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presents new
builds on Bonhomme (2012) to develop a method to systematically construct
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new moment conditions
moment conditions obtained in this way,
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was previously known in the literature.
has been appreciated by the existing literature. In the case where the model contains one lagged variable, the moment conditions in Kitazawa (2013, 2016) are transformations of a subset of ours.
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new