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Testing for Panel Cointegration Using Common Correlated Effects Estimators
Author(s) -
Banerjee Anindya,
CarrioniSilvestre Josep Lluís
Publication year - 2017
Publication title -
journal of time series analysis
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.576
H-Index - 54
eISSN - 1467-9892
pISSN - 0143-9782
DOI - 10.1111/jtsa.12234
Subject(s) - cointegration , estimator , spurious relationship , mathematics , econometrics , cross section (physics) , statistics , series (stratigraphy) , statistic , section (typography) , test statistic , panel data , statistical hypothesis testing , computer science , physics , quantum mechanics , paleontology , biology , operating system
Spurious regression analysis in panel data when the time series are cross section dependent is analysed in the article. We show that consistent estimation of the long‐run average parameter is possible once we control for cross section dependence using cross section averages in the spirit of the common correlated effects approach proposed by Pesaran. This result is used to design a panel cointegration test statistic accounting for cross section dependence. The performance of the proposal is investigated in comparison with factor‐based methods to control for cross section dependence when strong, semi‐weak and weak cross section dependence may be present.