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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Stata Programs
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 (forthcoming). Estimation of linear dynamic panel data models with time-invariant regressors. Journal of Applied Econometrics.
Related Work
Kripfganz, S. (2016). Quasi-maximum likelihood estimation of linear dynamic short-T panel-data models. Stata Journal 16 (4), 1013-1038.
Authors
Sebastian Kripfganz
University of Exeter
Claudia Schwarz
European Central Bank
Journal Article
Journal of Applied Econometrics
Article DOI
10.1002/jae.2681
Supplements
Online appendix
Data set and Stata replication file
(JAE Data Archive)
Stata Program
xtseqreg

www.kripfganz.de