Hostname: page-component-8448b6f56d-tj2md Total loading time: 0 Render date: 2024-04-23T13:50:31.356Z Has data issue: false hasContentIssue false
  • English
  • Français

Locally Optimal Properties of the Durbin-Watson Test

Published online by Cambridge University Press:  18 October 2010

Maxwell L. King  and
Merran A. Evans
Maxwell L. King
Affiliation:
Monash University, Australia
Merran A. Evans
Affiliation:
Monash University, Australia
Get access Rights & Permissions [Opens in a new window]

Abstract

Although originally designed to detect AR(1) disturbances in the linear-regression model, the Durbin-Watson test is known to have good power against other forms of disturbance behavior. In this paper, we identify disturbance processes involving any number of parameters against which the Durbin–Watson test is approximately locally best invariant uniformly in a range of directions from the null hypothesis. Examples include the sum of q independent ARMA(1,1) processes, certain spatial autocorrelation processes involving up to four parameters, and a stochastic cycle model.

Type
Brief Report
Information
Econometric Theory , Volume 4 , Issue 3 , December 1988 , pp. 509 - 516
Copyright
Copyright © Cambridge University Press 1988 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

REFERENCES

1. Blattberg, R.C. Evaluation of the power of the Durbin–Watson statistic for non-first-order serial correlation alternatives. Review of Economics and Statistics 55 (1973): 508–515.10.2307/1925676Google Scholar
2. Durbin, J. & Watson, G.S.. Testing for serial correlation in least-squares regression III. Biometrika 58 (1971): 1–19.Google Scholar
3. Giles, D.E.A. & Hampton, P.. An Engel curve analysis of household expenditure in New Zealand. Economic Record 61 (1985): 450–462.10.1111/j.1475-4932.1985.tb01997.xGoogle Scholar
4. Harvey, A.C. Trends and cycles in macroeconomic time series. Journal of Business and Economic Statistics 3 (1985): 216–227.Google Scholar
5. Isaacson, S.L. On the theory of unbiased tests of simple statistical hypotheses specifying the values of two or more parameters. Annals of Mathematical Statistics 22 (1951): 217–234.Google Scholar
6. King, M.L. The alternative Durbin-Watson test: an assessment of Durbin and Watson's choice of test statistic. Journal of Econometrics 17 (1981): 51–66.10.1016/0304-4076(81)90058-0Google Scholar
7. King, M.L. Testing for a serially correlated component in regression disturbances. International Economic Review 23 (1982): 577–582.10.2307/2526375Google Scholar
8. King, M.L. Testing for moving average regression disturbances. The Australian Journal of Statistics 25 (1983): 23–34.10.1111/j.1467-842X.1983.tb01193.xGoogle Scholar
9. King, M.L. & Evans, M.A.. The Durbin-Watson test and cross-sectional data. Economics Letters 18 (1985): 31–34.10.1016/0165-1765(85)90073-4Google Scholar
10. King, M.L. & Evans, M.A.. Testing for block effects in regression models based on survey data. Journal of the American Statistical Association 81 (1986): 677–679.10.1080/01621459.1986.10478320Google Scholar
11. King, M.L. & Hillier, G.H.. Locally best invariant tests of the error covariance matrix of the linear-regression model. Journal of the Royal Statistical Society, B47 (1985): 98–102.Google Scholar
12. Neyman, J. & Pearson, E.S.. Contributions to the theory of testing statistical hypotheses, Part II. Statistical Research Memoirs 2 (1938): 25–57.Google Scholar
13. Neyman, J. & Scott, E.L.. On the use of C(α) optimal tests of composite hypotheses. Bulletin of the International Statistical Institute 41 (1967): 477–497.Google Scholar
14. Sen Gupta, A. & Vermeire, L.. Locally optimal tests for multiparameter hypotheses. Journal of the American Statistical Association 81 (1986): 819–825.Google Scholar