Panels with non-stationary multifactor error structures

The presence of cross-sectionally correlated error terms invalidates much inferential theory of panel data models. Recently, work by Pesaran (2006) has suggested a method which makes use of cross-sectional averages to provide valid inference in the case of stationary panel regressions with a multifa...

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Vydané v:Journal of econometrics Ročník 160; číslo 2; s. 326 - 348
Hlavní autori: Kapetanios, G., Pesaran, M. Hashem, Yamagata, T.
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Amsterdam Elsevier B.V 01.02.2011
Elsevier
Elsevier Sequoia S.A
Edícia:Journal of Econometrics
Predmet:
Applications
Asymptotic methods
Bias
Common correlated effects
Correlation analysis
Cross-section dependence
Cross-section dependence Large panels Unit roots Principal components Common correlated effects
Data analysis
Distribution
Distribution theory
Econometrics
Estimating techniques
Estimation
Exact sciences and technology
Inference
Insurance, economics, finance
Large panels
Linear inference, regression
Logic and foundations
Mathematical logic, foundations, set theory
Mathematics
Model theory
Monte Carlo method
Monte Carlo simulation
Panel data
Principal components
Probability and statistics
Sciences and techniques of general use
Statistics
Stochastic processes
Studies
Unit root
Unit roots
C33
C13
Cross-section dependence
C12
Unit roots
Common correlated effects
Large panels
Principal components
Statistical distribution
Error estimation
Non parametric estimation
Multivariate analysis
Stochastic method
Unit root
Correspondence analysis
Panel data
Cross sectional study
Approximation theory
Monte Carlo method
Data analysis
Factor analysis
Asymptotic behavior
Average
Statistical estimation
Mean estimation
Statistical method
Statistical regression
Numerical analysis
Correlation analysis
Small sample
Econometrics
Principal component analysis
Biased estimation
ISSN:0304-4076, 1872-6895
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Shrnutí:The presence of cross-sectionally correlated error terms invalidates much inferential theory of panel data models. Recently, work by Pesaran (2006) has suggested a method which makes use of cross-sectional averages to provide valid inference in the case of stationary panel regressions with a multifactor error structure. This paper extends this work and examines the important case where the unobservable common factors follow unit root processes. The extension to I ( 1 ) processes is remarkable on two counts. First, it is of great interest to note that while intermediate results needed for deriving the asymptotic distribution of the panel estimators differ between the I ( 1 ) and I ( 0 ) cases, the final results are surprisingly similar. This is in direct contrast to the standard distributional results for I ( 1 ) processes that radically differ from those for I ( 0 ) processes. Second, it is worth noting the significant extra technical demands required to prove the new results. The theoretical findings are further supported for small samples via an extensive Monte Carlo study. In particular, the results of the Monte Carlo study suggest that the cross-sectional-average-based method is robust to a wide variety of data generation processes and has lower biases than the alternative estimation methods considered in the paper.
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ISSN:0304-4076
1872-6895
DOI:10.1016/j.jeconom.2010.10.001