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On the size and power of testing for no autocorrelation under weak assumptions

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  • Jen-Je Su

Abstract

Recently, Lobato (Journal of the American Statistical Association, 96, 1066-76, 2001) proposed a robust test of no autocorrelation on a time series when the series is possibly dependent. While the Lobato test is shown to be accurate in size, its power performance is unsatisfactory. This paper seeks to improve the power of the Lobato test without comprising its good size property. Based on the recent works of Jansson (2004) and Phillips et al. (2003), two classes of modified Lobato tests are suggested. It is found that the Lobato test and its Phillips-Sun-Jin modification exhibit very similar control over size while the Jansson modification tends to be more vulnerable to size distortion. It is also found that both modified tests dominate the Lobato test in terms of local asymptotic power and in terms of finite sample power and the Phillips-Sun-Jin modification seems to outperform the Jansson modification. Autocorrelations in monthly financial asset (stock/bond) returns are investigated.

Suggested Citation

  • Jen-Je Su, 2005. "On the size and power of testing for no autocorrelation under weak assumptions," Applied Financial Economics, Taylor & Francis Journals, vol. 15(4), pages 247-257.
    • Handle: RePEc:taf:apfiec:v:15:y:2005:i:4:p:247-257
    DOI: 10.1080/0960310042000319237
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    References listed on IDEAS

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    1. Andrews, Donald W K, 1991. "Heteroskedasticity and Autocorrelation Consistent Covariance Matrix Estimation," Econometrica, Econometric Society, vol. 59(3), pages 817-858, May.
    2. Nicholas M. Kiefer & Timothy J. Vogelsang & Helle Bunzel, 2000. "Simple Robust Testing of Regression Hypotheses," Econometrica, Econometric Society, vol. 68(3), pages 695-714, May.
    3. Peter C.B. Phillips & Yixiao Sun & Sainan Jin, 2003. "Consistent HAC Estimation and Robust Regression Testing Using Sharp Origin Kernels with No Truncation," Cowles Foundation Discussion Papers 1407, Cowles Foundation for Research in Economics, Yale University.
    4. Whitney K. Newey & Kenneth D. West, 1994. "Automatic Lag Selection in Covariance Matrix Estimation," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 61(4), pages 631-653.
    5. Lobato, I.N. & Nankervis, John C. & Savin, N.E., 2002. "Testing For Zero Autocorrelation In The Presence Of Statistical Dependence," Econometric Theory, Cambridge University Press, vol. 18(3), pages 730-743, June.
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    Cited by:

    1. Lee, Wei-Ming, 2007. "Robust M tests using kernel-based estimators with bandwidth equal to sample size," Economics Letters, Elsevier, vol. 96(3), pages 295-300, September.

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