Sebastian Kripfganz

Lecturer (assistant professor) in Economics,
University of Exeter Business School, Department of Economics
 
© Sebastian Kripfganz
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Title and Abstract
Estimation of linear dynamic panel data models with time-invariant regressors
We propose a two-stage procedure to estimate the effects of time-invariant regressors in 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, providing analytical standard error adjustments. The two-stage approach benefits from increased robustness against model misspecification, allows exploiting advantages of estimators relying on transformations to eliminate the unit-specific heterogeneity, and can yield more efficient estimates for the coefficients of time-invariant regressors. The approach is illustrated with Monte Carlo simulations and the estimation of a dynamic gravity equation for U.S. outward foreign direct investment.
Suggested Citation
Kripfganz, S. and C. Schwarz (2015). Estimation of linear dynamic panel data models with time-invariant regressors. ECB Working Paper 1838, European Central Bank.
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
Working Paper
ECB Working Paper 1838,
European Central Bank
Supplements
Stata replication file
Stata Program
xtseqreg

www.kripfganz.de