Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/241913
Year of Publication: 
2020
Series/Report no.: 
cemmap working paper No. CWP38/20
Publisher: 
Centre for Microdata Methods and Practice (cemmap), London
Abstract: 
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.
Persistent Identifier of the first edition: 
Document Type: 
Working Paper

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