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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.
Authors
Research Associate University College London and University of Oxford
Martin is an IFS Research Associate, a Fellow of the Nuffield College and a Professor in the Department of Economics at the University of Oxford.
Bo E. Honoré
Working Paper details
- DOI
- 10.1920/wp.cem.2020.3820
- Publisher
- The IFS
Suggested citation
Honoré, B and Weidner, M. (2020). Moment Conditions for Dynamic Panel Logit Models with Fixed Effects. London: The IFS. Available at: https://ifs.org.uk/publications/moment-conditions-dynamic-panel-logit-models-fixed-effects (accessed: 8 May 2024).
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