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On Point-Optimal Cox Tests

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  • Dastoor, Naorayex K.
  • Fisher, Gordon

Abstract

This paper is concerned with the general problem of testing one form of covariance structure against another in a normal linear regression. It is shown that all the point-optimal tests recently proposed by King and his associates can be interpreted as special cases of a Cox test for non-nested hypotheses. This provides a synthesis of a whole range of point-optimal tests as well as demonstrating that King and his associates have exposed a class of Cox tests which have an exact distribution.

Suggested Citation

  • Dastoor, Naorayex K. & Fisher, Gordon, 1988. "On Point-Optimal Cox Tests," Econometric Theory, Cambridge University Press, vol. 4(1), pages 97-107, April.
    • Handle: RePEc:cup:etheor:v:4:y:1988:i:01:p:97-107_01
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    References listed on IDEAS

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    1. Pesaran, M H & Deaton, Angus S, 1978. "Testing Non-Nested Nonlinear Regression Models," Econometrica, Econometric Society, vol. 46(3), pages 677-694, May.
    2. King, Maxwell L., 1985. "A Point Optimal Test for Moving Average Regression Disturbances," Econometric Theory, Cambridge University Press, vol. 1(2), pages 211-222, August.
    3. Davidson, Russell & MacKinnon, James G, 1981. "Several Tests for Model Specification in the Presence of Alternative Hypotheses," Econometrica, Econometric Society, vol. 49(3), pages 781-793, May.
    4. MacKinnon, James G. & White, Halbert & Davidson, Russell, 1983. "Tests for model specification in the presence of alternative hypotheses : Some further results," Journal of Econometrics, Elsevier, vol. 21(1), pages 53-70, January.
    5. M. H. Pesaran, 1974. "On the General Problem of Model Selection," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 41(2), pages 153-171.
    6. King, Maxwell L., 1983. "Testing for autoregressive against moving average errors in the linear regression model," Journal of Econometrics, Elsevier, vol. 21(1), pages 35-51, January.
    7. King, Maxwell L., 1985. "A point optimal test for autoregressive disturbances," Journal of Econometrics, Elsevier, vol. 27(1), pages 21-37, January.
    8. Fisher, Gordon R. & McAleer, Michael, 1981. "Alternative procedures and associated tests of significance for non-nested hypotheses," Journal of Econometrics, Elsevier, vol. 16(1), pages 103-119, May.
    9. Dastoor, Naorayex K., 1983. "Some aspects of testing non-nested hypotheses," Journal of Econometrics, Elsevier, vol. 21(2), pages 213-228, February.
    10. Evans, Merran A. & King, Maxwell L., 1985. "A point optimal test for heteroscedastic disturbances," Journal of Econometrics, Elsevier, vol. 27(2), pages 163-178, February.
    11. Gourieroux, Christian & Monfort, Alain & Trognon, Alain, 1983. "Testing nested or non-nested hypotheses," Journal of Econometrics, Elsevier, vol. 21(1), pages 83-115, January.
    12. Godfrey, Leslie G, 1983. "Testing Non-Nested Models after Estimation by Instrumental Variables or Least Squares," Econometrica, Econometric Society, vol. 51(2), pages 355-365, March.
    13. Fisher, Gordon R., 1983. "Tests for two separate regressions," Journal of Econometrics, Elsevier, vol. 21(1), pages 117-132, January.
    14. Kadiyala, Koteswara Rao, 1970. "Testing for the Independence of Regression Disturbances," Econometrica, Econometric Society, vol. 38(1), pages 97-117, January.
    15. King, Maxwell L., 1984. "A new test for fourth-order autoregressive disturbances," Journal of Econometrics, Elsevier, vol. 24(3), pages 269-277, March.
    16. King, Maxwell L. & Evans, Merran A., 1984. "A joint test for serial correlation and heteroscedasticity," Economics Letters, Elsevier, vol. 16(3-4), pages 297-302.
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    Cited by:

    1. MacKinnon, James G, 1992. "Model Specification Tests and Artificial Regressions," Journal of Economic Literature, American Economic Association, vol. 30(1), pages 102-146, March.
    2. Maxwell L. King & Sivagowry Sriananthakumar, 2015. "Point Optimal Testing: A Survey of the Post 1987 Literature," Monash Econometrics and Business Statistics Working Papers 5/15, Monash University, Department of Econometrics and Business Statistics.

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