Sebastian Kripfganz

Lecturer (assistant professor) in Economics,
University of Exeter Business School, Department of Economics

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© Sebastian Kripfganz
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Title and Abstract
Estimation of linear dynamic panel data models with time-invariant regressors
We present a sequential approach to estimating a dynamic Hausman-Taylor model. We first estimate the coefficients of the time-varying regressors and subsequently regress the first-stage residuals on the time-invariant regressors. In comparison to estimating all coefficients simultaneously, this two-stage procedure is more robust against model misspecification, allows for a flexible choice of the first-stage estimator, and enables simple testing of the overidentifying restrictions. For correct inference, we derive analytical standard error adjustments. We evaluate the finite-sample properties with Monte Carlo simulations and apply the approach to a dynamic gravity equation for U.S. outward foreign direct investment.
Suggested Citation
Kripfganz, S., and C. Schwarz (2019). Estimation of linear dynamic panel data models with time-invariant regressors. Journal of Applied Econometrics 34 (4), 526-546.
Related Work
Breitung, J., S. Kripfganz, and K. Hayakawa (2021). Bias-corrected method of moments estimators for dynamic panel data models. Econometrics and Statistics, forthcoming.
Kripfganz, S. (2019). Generalized method of moments estimation of linear dynamic panel data models. Proceedings of the 2019 London Stata Conference.
Kripfganz, S. (2016). Quasi-maximum likelihood estimation of linear dynamic short-T panel-data models. Stata Journal 16 (4), 1013-1038.
Sebastian Kripfganz
University of Exeter
Claudia Schwarz
European Central Bank
Journal Article
Journal of Applied Econometrics 34 (4), 526-546
Article DOI
Online appendix
Data set and Stata replication file
(JAE Data Archive)
Stata Program