Estimation of Linear Dynamic Panel Data Models with Time-Invariant Regressors

53 Pages Posted: 26 Aug 2015

See all articles by Sebastian Kripfganz

Sebastian Kripfganz

University of Exeter Business School - Department of Economics

Claudia Schwarz

European Central Bank (ECB)

Multiple version iconThere are 2 versions of this paper

Date Written: August 25, 2015

Abstract

We propose a two-stage estimation procedure to identify the effects of time-invariant regressors in a dynamic version of the 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 for the second-stage coefficients. The two-stage approach is more robust against misspecification than GMM estimators that obtain all parameter estimates simultaneously. In addition, it allows exploiting advantages of estimators relying on transformations to eliminate the unit-specific heterogeneity. We analytically demonstrate under which conditions the one-stage and two-stage GMM estimators are equivalent. Monte Carlo results highlight the advantages of the two-stage approach infinite samples. Finally, the approach is illustrated with the estimation of a dynamic gravity equation for U.S. outward foreign direct investment.

Keywords: Dynamic panel data; Time-invariant variables; Two-stage estimation; System GMM; Dynamic gravity equation

JEL Classification: C13; C23; F23

Suggested Citation

Kripfganz, Sebastian and Schwarz, Claudia, Estimation of Linear Dynamic Panel Data Models with Time-Invariant Regressors (August 25, 2015). ECB Working Paper No. 1838, Available at SSRN: https://ssrn.com/abstract=2650425

Sebastian Kripfganz

University of Exeter Business School - Department of Economics ( email )

Streatham Court
Exeter, EX4 4RJ
United Kingdom

Claudia Schwarz (Contact Author)

European Central Bank (ECB) ( email )

Sonnemannstrasse 22
Frankfurt am Main, 60314
Germany

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