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The asymptotic distribution of the F-test statistic for individual effects

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  • Chris D. Orme
  • Takashi Yamagata
Abstract
(the number of cross-sections) and T is fixed (the number of time periods). Three theoretical results emerge: (i) the standard F-test procedure will still deliver asymptotically valid inferences; (ii) under (pure) local random effects, the F-test and random effects test procedures have identical asymptotic power; (iii) under local fixed, or random effects which are correlated with the regressors, the F-test will have higher asymptotic power than the random effects test. Copyright Royal Economic Society 2006

Suggested Citation

  • Chris D. Orme & Takashi Yamagata, 2006. "The asymptotic distribution of the F-test statistic for individual effects," Econometrics Journal, Royal Economic Society, vol. 9(3), pages 404-422, November.
    • Handle: RePEc:ect:emjrnl:v:9:y:2006:i:3:p:404-422
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    References listed on IDEAS

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    1. Yuzo Honda, 1985. "Testing the Error Components Model with Non-Normal Disturbances," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 52(4), pages 681-690.
    2. Ali, Mukhtar M. & Sharma, Subhash C., 1996. "Robustness to nonnormality of regression F-tests," Journal of Econometrics, Elsevier, vol. 71(1-2), pages 175-205.
    3. T. S. Breusch & A. R. Pagan, 1980. "The Lagrange Multiplier Test and its Applications to Model Specification in Econometrics," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 47(1), pages 239-253.
    4. Moulton, Brent R & Randolph, William C, 1989. "Alternative Tests of the Error Components Model," Econometrica, Econometric Society, vol. 57(3), pages 685-693, May.
    5. Leslie G. Godfrey & Chris D. Orme, 2000. "Controlling the significance levels of prediction error tests for linear regression models," Econometrics Journal, Royal Economic Society, vol. 3(1), pages 66-83.
    6. Baltagi, Badi H. & Chang, Young-Jae & Li, Qi, 1992. "Monte Carlo results on several new and existing tests for the error component model," Journal of Econometrics, Elsevier, vol. 54(1-3), pages 95-120.
    7. Chesher, Andrew D, 1984. "Testing for Neglected Heterogeneity," Econometrica, Econometric Society, vol. 52(4), pages 865-872, July.
    8. Nerlove, Marc, 1971. "Further Evidence on the Estimation of Dynamic Economic Relations from a Time Series of Cross Sections," Econometrica, Econometric Society, vol. 39(2), pages 359-382, March.
    9. Qin, Huaizhen & Wan, Alan T.K., 2004. "ON THE PROPERTIES OF THE t- AND F-RATIOS IN LINEAR REGRESSIONS WITH NONNORMAL ERRORS," Econometric Theory, Cambridge University Press, vol. 20(4), pages 690-700, August.
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    Cited by:

    1. Jochmans, K., 2019. "Testing Correlation in Error-Component Models," Cambridge Working Papers in Economics 1993, Faculty of Economics, University of Cambridge.
    2. Badi Baltagi & Chihwa Kao & Sanggon Na, 2013. "Testing for cross-sectional dependence in a panel factor model using the wild bootstrap $$F$$ test," Statistical Papers, Springer, vol. 54(4), pages 1067-1094, November.
    3. Khusrav Gaibulloev & Todd Sandler, 2009. "The Impact Of Terrorism And Conflicts On Growth In Asia," Economics and Politics, Wiley Blackwell, vol. 21(3), pages 359-383, November.
    4. Seung C. Ahn & Young H. Lee, 2014. "Major League Baseball Attendance," Journal of Sports Economics, , vol. 15(5), pages 451-477, October.
    5. Kelvin K. F. Law & Lillian F. Mills, 2017. "Military experience and corporate tax avoidance," Review of Accounting Studies, Springer, vol. 22(1), pages 141-184, March.
    6. Chris D. Orme & Takashi Yamagata, 2014. "A Heteroskedasticity-Robust F -Test Statistic for Individual Effects," Econometric Reviews, Taylor & Francis Journals, vol. 33(5-6), pages 431-471, August.
    7. Bennala, Nezar & Hallin, Marc & Paindaveine, Davy, 2012. "Pseudo-Gaussian and rank-based optimal tests for random individual effects in large n small T panels," Journal of Econometrics, Elsevier, vol. 170(1), pages 50-67.
    8. Calhoun, Gray, 2011. "Hypothesis testing in linear regression when k/n is large," Journal of Econometrics, Elsevier, vol. 165(2), pages 163-174.

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