Testing Cross-sectional Correlation in Large Panel Data Models with Serial Correlation
Badi Baltagi (),
Chihwa Kao () and
Bin Peng ()
No 2016-32, Working papers from University of Connecticut, Department of Economics
This paper considers the problem of testing cross-sectional correlation in large panel data models with serially correlated errors. It finds that existing tests for cross-sectional correlation encounter size distortions with serial correlation in the errors. To control the size, this paper proposes a modification of Pesaran’s CD test to account for serial correlation of an unknown form in the error term. We derive the limiting distribution of this test as (N, T) → ∞. The test is distribution free and allows for unknown forms of serial correlation in the errors. Monte Carlo simulations show that the test has good size and power for large panels when serial correlation in the errors is present. JEL Classification: C13; C33 Key words: Cross-sectional Correlation Test; Serial Correlation; Large Panel Data Model
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Journal Article: Testing Cross-Sectional Correlation in Large Panel Data Models with Serial Correlation (2016)
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Persistent link: https://EconPapers.repec.org/RePEc:uct:uconnp:2016-32
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