Hostname: page-component-cd9895bd7-lnqnp Total loading time: 0 Render date: 2024-12-27T18:57:18.290Z Has data issue: false hasContentIssue false

A Point Optimal Test for Moving Average Regression Disturbances

Published online by Cambridge University Press:  18 October 2010

Maxwell L. King
Affiliation:
Monash University

Abstract

This paper reconsiders King's [12] locally optimal test procedure for first-order moving average disturbances in the linear regression model. It recommends two tests, one for problems involving positively correlated disturbances and one for negatively correlated disturbances. Both tests are most powerful invariant at a point in the alternative hypothesis parameter space that is determined by a function involving the sample size and the number of regressors. Selected bounds for the tests' significance points are tabulated and an empirical comparison of powers demonstrates the overall superiority of the new test for positively correlated moving average disturbances.

Type
Articles
Copyright
Copyright © Cambridge University Press 1985

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.Abrahamse, A.P.J. and Koerts, J.New estimators of disturbances in regression analysis. Journal of the American Statistical Association 66 (1971): 7174.CrossRefGoogle Scholar
2.Abrahamse, A.P.J. and Louter, A. S.On a new test for autocorrelation in least squares regression. Biometrika 58 (1971): 5360.CrossRefGoogle Scholar
3.Davies, R. B.Algorithm AS 155. The distribution of a linear combination of x 2 random variables. Applied Statistics 29 (1980): 323333.CrossRefGoogle Scholar
4.Dubbelman, C.A priori fixed covariance matrices of disturbance estimators. European Economic Review 3 (1972): 413436.CrossRefGoogle Scholar
5.Dubbelman, C. A. S., Louter and Abrahamse, A.P.J.On typical characteristics of economic time series and the relative qualities of five autocorrelation tests. Journal of Econometrics 8 (1978): 295306.CrossRefGoogle Scholar
6.Durbin, J. and Watson, G. S.Testing for serial correlation in least squares regression II. Biometrika 38 (1951): 159178.CrossRefGoogle ScholarPubMed
7.Durbin, J. and Watson, G. S.. Testing for serial correlation in least squares regression III. Biometrika 58 (1971): 119.Google Scholar
8.Evans, M. A. and King, M. L.A point optimal test for heteroscedastic disturbances. Journal of Econometrics, 27 (1985): 163178.CrossRefGoogle Scholar
9.Farebrother, R. W.Algorithm AS 153. Pan's procedure for the tail probabilities of the Durbin–Watson statistic. Applied Statistics 29 (1980): 224227; 30(1981): 189.CrossRefGoogle Scholar
10.Hannan, E. J. and Terrell, R. D.Testing for serial correlation after least square s regression. Econometrica 36 (1968): 133150.CrossRefGoogle Scholar
11.King, M. L.The alternative Durbin-Watson test: An assessment of Durbin and Watson's choice of test statistic. Journal of Econometrics 17 (1981): 5166.CrossRefGoogle Scholar
12.King, M. L.Testing for moving average regression disturbances. Australian Journal of Statistics 25 (1983): 2334.CrossRefGoogle Scholar
13.King, M. L.A new test for fourth order autoregressive disturbances. Journal of Econometrics 24 (1984): 269277.CrossRefGoogle Scholar
14.King, M. L.A point optimal test for autoregressive disturbances. Journal of Econometrics 27 (1985): 2137.CrossRefGoogle Scholar
15.King, M. L. and Evans, M. A.A joint test for serial correlation and heteroscedasticity. Economics Letters 16 (1984): 297302.CrossRefGoogle Scholar
16.Koerts, J. and Abrahamse, A.P.J.On the Theory and Application of the General Linear Model. Rotterdam University Press: Rotterdam, 1969.Google Scholar
17.Maddala, G. S.Econometrics. McGraw-Hill Kogakusha: Tokyo, 1977.Google Scholar
18.Sargan, J. D. and Bhargava, A.Testing residuals from least squares regression for being generated by the Gaussian random walk. Econometrica 51 (1983): 153174.CrossRefGoogle Scholar
19.Sowey, E. R.University teaching of econometrics: A personal view. Econometric Reviews 2 (1983): 255289.CrossRefGoogle Scholar
20.Theil, H. and Nagar, A. L.. Testing the independence of regression disturbances. Journal of the American Statistical Association 56 (1961): 793806.CrossRefGoogle Scholar