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A Small-Sample Correction for Testing for gth-Order Serial Correlation with Artificial Regressions

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  • Belsley, David A

Abstract

Monte Carlo experiments establish that the usual 't-statistic' used for testing for first-order dial correlation with artificial regressions is far from being distributed as a Student's t in small samples. Rather, it is badly biased in both mean and variance and results in grossly misleading tests of hypotheses when treated as a Student's t. (Similar distortions plague the familiar Durbin-Watson statistic.) Simply computed corrections for the mean and variance are derived, however, which are shown to lead to a transformed statistic producing acceptable tests. The test procedure is detailed and exemplar code provided. Citation Copyright 1997 by Kluwer Academic Publishers.

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  • Belsley, David A, 1997. "A Small-Sample Correction for Testing for gth-Order Serial Correlation with Artificial Regressions," Computational Economics, Springer;Society for Computational Economics, vol. 10(3), pages 197-229, August.
    • Handle: RePEc:kap:compec:v:10:y:1997:i:3:p:197-229
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    References listed on IDEAS

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    1. Davidson, Russell & MacKinnon, James G., 1993. "Estimation and Inference in Econometrics," OUP Catalogue, Oxford University Press, number 9780195060119.
    2. MacKinnon, James G, 1992. "Model Specification Tests and Artificial Regressions," Journal of Economic Literature, American Economic Association, vol. 30(1), pages 102-146, March.
    3. Breusch, T S, 1978. "Testing for Autocorrelation in Dynamic Linear Models," Australian Economic Papers, Wiley Blackwell, vol. 17(31), pages 334-355, December.
    4. Godfrey, Leslie G, 1978. "Testing for Higher Order Serial Correlation in Regression Equations When the Regressors Include Lagged Dependent Variables," Econometrica, Econometric Society, vol. 46(6), pages 1303-1310, November.
    5. Durbin, J, 1970. "Testing for Serial Correlation in Least-Squares Regression When Some of the Regressors are Lagged Dependent Variables," Econometrica, Econometric Society, vol. 38(3), pages 410-421, May.
    6. Godfrey, Leslie G, 1978. "Testing against General Autoregressive and Moving Average Error Models When the Regressors Include Lagged Dependent Variables," Econometrica, Econometric Society, vol. 46(6), pages 1293-1301, November.
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    Cited by:

    1. Mohd. FAYAZ & Kaur Bhatia SANDEEP, 2016. "Trends , Patterns and Determinants of Indian Current Account Deficit," Applied Econometrics and International Development, Euro-American Association of Economic Development, vol. 16(1).
    2. Belsley, David A., 2002. "An investigation of an unbiased correction for heteroskedasticity and the effects of misspecifying the skedastic function," Journal of Economic Dynamics and Control, Elsevier, vol. 26(9-10), pages 1379-1396, August.
    3. David A. Belsley, 2000. "An Investigation Of An Unbiased Corection For Heteroskedasticity And The Effects Of Misspecifying The Skedastic Function," Computing in Economics and Finance 2000 154, Society for Computational Economics.
    4. Godfrey, L.G. & Tremayne, A.R., 2005. "The wild bootstrap and heteroskedasticity-robust tests for serial correlation in dynamic regression models," Computational Statistics & Data Analysis, Elsevier, vol. 49(2), pages 377-395, April.

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    More about this item

    JEL classification:

    • C12 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Hypothesis Testing: General
    • C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Statistical Simulation Methods: General
    • C19 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Other
    • C49 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Other

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