Testing Cross-sectional Correlation in Large Panel Data Models with Serial Correlation

Badi Baltagi, Chihwa Kao and Peng Bin ()

No 2016-32, Working papers from University of Connecticut, Department of Economics

Abstract: 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

Pages: 24 pages
Date: 2016-10
New Economics Papers: this item is included in nep-ecm and nep-ets
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Citations: View citations in EconPapers (21)

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Related works:
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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