Hostname: page-component-76fb5796d-qxdb6 Total loading time: 0 Render date: 2024-04-26T15:29:02.262Z Has data issue: false hasContentIssue false

Classes of Similar Regions and Their Power Properties for Some Econometric Testing Problems

Published online by Cambridge University Press:  11 February 2009

Abstract

In an hypothesis testing problem involving nuisance parameters for which boundedly complete sufficient statistics exist under the null hypothesis, the class of all similar regions for the problem is characterized by the conditional distribution of the data given these sufficient statistics. If there exists a one-to-one transformation y → (t, u) of the data, y, to the sufficient statistic, t, and a second vector of statistics, u, that is independent of t under the null hypothesis, then the statistic u itself characterizes the class of similar regions. This paper applies this idea to five testing problems of interest in econometrics. In each case we obtain the density of the relevant statistic under the null hypothesis, when it is free of nuisance parameters, and under the alternative. Using the density under the alternative, we discuss the power properties of the class of similar tests for each problem. Other applications are also suggested.

Type
Research Article
Copyright
Copyright © Cambridge University Press 1987

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. Anderson, T. W. On the theory of testing serial correlation. Scandinavisk Aktuarietidskrift 31 (1948): 88116.Google Scholar
2. Anderson, T. W. The Statistical Analysis of Time Series, New York: Wiley, 1971.Google Scholar
3. Chernoff, H., and Scheffé, H.. A generalization of the Neyman-Pearson fundamental lemma. Annals of Mathematical Statistics 23 (1952): 213225.10.1214/aoms/1177729438CrossRefGoogle Scholar
4. Cliff, A. D., and Ord, J. K.. Testing for spatial autocorrelation among regression residuals. Geographical Analysis 4 (1972): 267284.10.1111/j.1538-4632.1972.tb00475.xCrossRefGoogle Scholar
5. Constantine, A. G., and Muirhead, R. J.. Asymptotic expansions for distributions of latent roots and multivariate analysis. Journal of Multivariate Analysis 6 (1976): 369391.10.1016/0047-259X(76)90046-4CrossRefGoogle Scholar
6. Cox, D. R., and Hinkley, D. V.. Theoretical Statistics, London: Chapman and Hall, 1974.10.1007/978-1-4899-2887-0CrossRefGoogle Scholar
7. Dantzig, G. B., and Wald, A.. On the fundamental lemma of Neyman and Pearson. Annals of Mathematical Statistics 22 (1951): 8793.10.1214/aoms/1177729695CrossRefGoogle Scholar
8. Davis, A. W. Invariant polynomials with two matrix arguments extending the zonal polyno mials: Applications to multivariate distribution theory. Annals of the Institute of Statistical Mathematics A31 (1979): 465485.10.1007/BF02480302CrossRefGoogle Scholar
9. Durbin, J., and Watson, G. S.. Testing for serial correlation in least squares regression, I. Biometrika 37 (1950): 409428.Google ScholarPubMed
10. Durbin, J., and Watson, G. S.. Testing for serial correlation in least squares regression, II. Biometrika 38 (1951): 159178.10.1093/biomet/38.1-2.159CrossRefGoogle ScholarPubMed
11. Durbin, J., and Watson, G. S.. Testing for serial correlation in least squares regression, III. Biometrika 58 (1971): 119.Google Scholar
12. Evans, G.B.A., and Savin, N. E.. Testing for unit roots: 2. Econometrics 52 (1984): 12411269.10.2307/1910998CrossRefGoogle Scholar
13. Farrell, R. H. Techniques of Multivariate Calculation, Springer-Verlag, New York, 1976.10.1007/BFb0079663CrossRefGoogle Scholar
14. Ferguson, T. S. Mathematical Statistics: A Decision Theoretic Approach, New York: Academic Press, 1967.Google Scholar
15. Franzini, L., and Harvey, A. C.. Testing for deterministic trend and seasonal components in time series models. Biometrika 70 (1983): 673682.10.1093/biomet/70.3.673CrossRefGoogle Scholar
16. Hausman, J. Specification tests in econometrics. Econometrica 46 (1978): 12511271.10.2307/1913827CrossRefGoogle Scholar
17. Henshaw, R. C. Testing single-equation least squares regression models for autocorrelated disturbances. Econometrica 34 (1966) 646660.10.2307/1909774CrossRefGoogle Scholar
18. Herz, C. S. Bessel functions of matrix argument. Annals of Mathematics 61 (1955): 474523.10.2307/1969810CrossRefGoogle Scholar
19. Hillier, G. H., Kinal, T. W., and Srivastava, V. K. On the moments of ordinary least squares and instrumental variables estimators in a general structural equation. Econometrica 52 (1984): 185202.10.2307/1911467CrossRefGoogle Scholar
20. Hillier, G. H. On the joint and marginal densities of instrumental variables estimators in a general structural equation. Econometric Theory 1 (1985): 5372.10.1017/S0266466600010999CrossRefGoogle Scholar
21. Hillier, G. H. Joint tests for zero restrictions on nonnegative regression coefficients. Biometrika 73 (1986): 657669.10.1093/biomet/73.3.657CrossRefGoogle Scholar
22. Hsu, P. L. Analysis of variance from the power function standpoint. Biometrika 32 (1941): 62.10.1093/biomet/32.1.62CrossRefGoogle Scholar
23. 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): 217234.10.1214/aoms/1177729642CrossRefGoogle Scholar
24. James, A. T. Normal multivariate analysis and the orthogonal group. Annals of Mathematical Statistics 25 (1954): 4075.10.1214/aoms/1177728846CrossRefGoogle Scholar
25. James, A. T. Distribution of matrix variates and latent roots derived from normal samples. Annals of Mathematical Statistics 35 (1964): 475501.10.1214/aoms/1177703550CrossRefGoogle Scholar
26. Judge, G. G., Griffiths, W. E., Hill, R. C., and Lee, T-C.. The Theory and Practice of Econometrics, New York: Wiley, 1980.Google Scholar
27. Kariya, T. Tests for independence between two seemingly unrelated regression equations. The Annals of Statistics 9 (1981): 381390.10.1214/aos/1176345403CrossRefGoogle Scholar
28. Kariya, T. The general MANOVA problem. Annals of Statistics 5 (1978): 200214.Google Scholar
29. Kariya, T. and Eaton, M. L.. Robust tests for spherical symmetry. Annals of Statistics 9 (1977): 210213.Google Scholar
30. Kendall, M. G. and Stuart, A.. The Advanced Theory of Statistics Vol. 2, London: Griffin, 1969.10.2307/2528806CrossRefGoogle Scholar
31. King, M. L. Some aspects of statistical inference in the linear regression model, unpublished Ph.D. Thesis, University of Canterbury, 1979.Google Scholar
32. King, M. L. The alternative Durbin–Watson test: an assessment of Durbin and Watson's choice of test statistic. Journal of Econometrics 17 (1981): 5166.10.1016/0304-4076(81)90058-0CrossRefGoogle Scholar
33. King, M. L. A small sample property of the Cliff–Ord test for spatial correlation. Journal of the Royal Statistical Society, B 43 (1981): 263264.Google Scholar
34. King, M. L. Testing for a serially correlated component in regression disturbances. Inter national Economic Review 23 (1982): 577582.10.2307/2526375CrossRefGoogle Scholar
35. King, M. L. and Hillier, G. H.. Locally best invariant tests of the error covariance matrix of the linear regression model. Journal of the Royal Statistical Society, B 47 (1985): 98102.Google Scholar
36. King, M. L. and Inder, B.. Testing the covariance matrix of the linear regression model: Some further results, mimeo, Monash University, (1986).Google Scholar
37. La Motte, L. R. and McWhorter, A.. An exact test for the presence of random walk coefficients in a linear regression model. Journal of the American Statistical Association 73 (1978): 816820.10.1080/01621459.1978.10480105CrossRefGoogle Scholar
38. Lehmann, E. L. On optimum tests of composite hypotheses with one constraint. Annals of Mathematical Statistics 18 (1947): 473494.10.1214/aoms/1177730340CrossRefGoogle Scholar
39. Lehmann, E. L. Testing Statistical Hypotheses New York: Wiley, 1959.Google Scholar
40. Marden, J. and Perlman, M. D.. Invariant tests for means with covariates. Annals of Statistics 8 (1980): 2563.10.1214/aos/1176344890CrossRefGoogle Scholar
41. Muirhead, R. J. Aspects of Multivariate Statistical Theory, New York: Wiley, 1982.10.1002/9780470316559CrossRefGoogle Scholar
42. Nakamura, A. and Nakamura, M.. A note on the relationship between several specification error tests presented by Durbin, Wu, and Hausman. Econometrica 49 (1982): 15831588.10.2307/1911420CrossRefGoogle Scholar
43. Revankar, N. S. and Hartley, M. J., An independence test and conditional unbiased predictions in the context of simultaneous equation systems. International Economic Review 14 (1973): 625631.10.2307/2525975CrossRefGoogle Scholar
44. Sargan, J. D. and Bhargava, A.. Testing residuals from least squares regression for being generated by the Gaussian random walk. Econometrica 51 (1983): 153174.10.2307/1912252CrossRefGoogle Scholar
45. Savin, N. E. Multiple Hypothesis Testing, Chapter 14 in Handbook of Econometrics Vol. 2, Amsterdam: North Holland, 1984.Google Scholar
46. Vinod, H. D. Generalization of the Durbin–Watson statistic for higher-order autoregressive processes. Communications in Statistics 2 (1973): 115144.10.1080/03610927308827060CrossRefGoogle Scholar
47. Wald, A. Tests of statistical hypotheses concerning several parameters when the number of observations is large. American Mathematical Society Transactions 54 (1943): 426.10.1090/S0002-9947-1943-0012401-3CrossRefGoogle Scholar
48. Wallis, K. F. Testing for fourth-order autocorrelation in quarterly regression equations. Econometrica 40 (1972): 617636.10.2307/1912957CrossRefGoogle Scholar
49. Wolfowitz, J. The power of the classical tests associated with the normal distribution. Annals of Mathematical Statistics 20 (1949): 540.10.1214/aoms/1177729946CrossRefGoogle Scholar
50. Wu, De-Min. Alternative tests of independence between stochastic regressors and disturbances. Econometrica 42 (1974): 529546.10.2307/1911789CrossRefGoogle Scholar