A Parametric approach to the Estimation of Cointegration Vectors in Panel Data
AbstractIn this article, 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 suggested where, in the first step, all individual specific parameters are estimated, and in the second step, the long-run parameters are estimated from a pooled least-squares regression. The two-step estimator and related test procedures can easily be modified to account for contemporaneously correlated errors, a feature that is often encountered in multi-country studies. Monte Carlo simulations suggest that the two-step estimator and related test procedures outperform semiparametric alternatives such as the fully modified OLS approach, especially if the number of time periods is small.
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Bibliographic InfoArticle provided by Taylor & Francis Journals in its journal Econometric Reviews.
Volume (Year): 24 (2005)
Issue (Month): 2 ()
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- Breitung, Jörg, 2002. "A parametric approach to the estimation of cointegration vectors in panel data," SFB 373 Discussion Papers 2002,3, Humboldt University of Berlin, Interdisciplinary Research Project 373: Quantification and Simulation of Economic Processes.
- Jörg Breitung, 2002. "A parametric approach to the estimation of cointegration vectors in panel data," 10th International Conference on Panel Data, Berlin, July 5-6, 2002 B5-4, International Conferences on Panel Data.
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