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The limiting power of autocorrelation tests in regression models with linear restrictions

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  • Wan, Alan
  • Zou, Guohua
  • Banerjee, Anurag

Abstract

It is well known that the Durbin-Watson and several other tests for first-order autocorrelation have limiting power of either zero or one in a linear regression model without an intercept, and tend to a constant lying strictly between these values when an intercept term is present. This paper considers the limiting power of these tests in models with restricted coefficients. Surprisingly, it is found that with linear restrictions on the coefficients, the limiting power can still drop to zero even with the inclusion of an intercept in the regression. It is also shown that for regressions with valid restrictions, these test statistics have algebraic forms equivalent to the corresponding statistics in the unrestricted model.

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File URL: http://eprints.soton.ac.uk/33806/1/0405.pdf
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Paper provided by Economics Division, School of Social Sciences, University of Southampton in its series Discussion Paper Series In Economics And Econometrics with number 0405.

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Date of creation: 01 Jan 2004
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Handle: RePEc:stn:sotoec:0405

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  1. Kramer, W., 1985. "The power of the Durbin-Watson test for regressions without an intercept," Journal of Econometrics, Elsevier, vol. 28(3), pages 363-370, June.
  2. White, Kenneth J, 1992. "The Durbin-Watson Test for Autocorrelation in Nonlinear Models," The Review of Economics and Statistics, MIT Press, vol. 74(2), pages 370-73, May.
  3. King, Maxwell L., 1985. "A point optimal test for autoregressive disturbances," Journal of Econometrics, Elsevier, vol. 27(1), pages 21-37, January.
  4. Kramer, Walter & Zeisel, Helmut, 1990. "Finite sample power of linear regression autocorrelation tests," Journal of Econometrics, Elsevier, vol. 43(3), pages 363-372, March.
  5. Bartels, Robert & Goodhew, John, 1981. "The Robustness of the Durbin-Watson Test," The Review of Economics and Statistics, MIT Press, vol. 63(1), pages 136-39, February.
  6. Blattberg, Robert C, 1973. "Evaluation of the Power of the Durbin-Watson Statistic for Non-First Order Serial Correlation Alternatives," The Review of Economics and Statistics, MIT Press, vol. 55(4), pages 508-15, November.
  7. Dufour, J.-M., 1986. "Exact tests and confidence sets in linear regressions with autocorrelated errors," CORE Discussion Papers 1986037, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
  8. Banerjee, Anurag N. & Magnus, Jan R., 1999. "The sensitivity of OLS when the variance matrix is (partially) unknown," Journal of Econometrics, Elsevier, vol. 92(2), pages 295-323, October.
  9. Bernard, Andrew B & Jones, Charles I, 1996. "Comparing Apples to Oranges: Productivity Convergence and Measurement across Industries and Countries," American Economic Review, American Economic Association, vol. 86(5), pages 1216-38, December.
  10. Schmidt, Peter & Guilkey, David K, 1975. "Some Further Evidence on the Power of the Durbin-Watson and Geary Tests," The Review of Economics and Statistics, MIT Press, vol. 57(3), pages 379-82, August.
  11. Bartels, Robert, 1992. "On the power function of the Durbin-Watson test," Journal of Econometrics, Elsevier, vol. 51(1-2), pages 101-112.
  12. King, M. L., 1981. "The alternative Durbin-Watson test : An assessment of Durbin and Watson's choice of test statistic," Journal of Econometrics, Elsevier, vol. 17(1), pages 51-66, September.
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