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Econometrics 2016, 4(4), 44;

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

Department of Economics & Center for Policy Research, 426 Eggers Hall, Syracuse University, Syracuse, NY 13244-1020, USA
Department of Economics, 365 Fairfield Way, U-1063, University of Connecticut, Storrs, CT 06269-1063, USA
Department of Finance, 523 School of Economics, Huazhong University of Science and Technology, Wuhan 430074, China
Author to whom correspondence should be addressed.
Academic Editors: In Choi and Ryo Okui
Received: 23 July 2016 / Revised: 12 October 2016 / Accepted: 19 October 2016 / Published: 4 November 2016
(This article belongs to the Special Issue Recent Developments in Panel Data Methods)
Full-Text   |   PDF [325 KB, uploaded 4 November 2016]


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 Cross-sectional Dependence (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. View Full-Text
Keywords: cross-sectional correlation test; serial correlation; large panel data model cross-sectional correlation test; serial correlation; large panel data model
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).

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Baltagi, B.H.; Kao, C.; Peng, B. Testing Cross-Sectional Correlation in Large Panel Data Models with Serial Correlation. Econometrics 2016, 4, 44.

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