A parametric approach to the estimation of cointegration vectors in panel data

A parametric approach to the estimation
of cointegration vectors in panel data

Jorg Breitung*

Humboldt University Berlin

Institute of Statistics and Econometrics
Spandauer Strasse 1

10178 Berlin, Germany

January 14, 2002

Abstract

In this paper a parametric framework for estimation and inference
in cointegrated panel data models is considered that is based on a
cointegrated VAR(p) model. A convenient two-step estimator is sug-
gested where in the first step all individual specific parameters are
estimated, whereas in the second step the long-run parameters are
estimated from a pooled least-squares regression. The two-step esti-
mator and related test procedures can easily be modified to account
for contemporaneously correlated errors, a feature that is often en-
countered in multi-country studies. Monte Carlo simulations suggest
that the two-step estimator and related test procedures outperform
semiparametric alternatives such as the FM-OLS approach, especially
if the number of time periods is small.

* The research for this paper was carried out within the SFB 373 at the Humboldt
University Berlin and the METEOR research project “Macroeconomic Consequences of
Financial Crises” at the University of Maastricht. I wish to thank Ralf BrUggemann, Gerd
Hansen and Uwe Hassler for helpful comments and suggestions.

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