IDEAS home Printed from https://ideas.repec.org/p/qed/wpaper/573.html
   My bibliography  Save this paper

Model Specification Tests Against Non-Nested Alternatives

Author

Listed:
  • James G. MacKinnon

Abstract

Non-nested hypothesis tests provide a way to test the specification of an econometric model against the evidence provided by one or more non-nested alternatives. This paper surveys the recent literature on non-nested hypothesis testing in the context of regression and related models. Much of the purely statistical literature which has evolved from the fundamental work of Cox is discussed briefly or not at all. Instead, emphasis is placed on those techniques which are easy to employ in practice and are likely to be useful to applied workers.

Suggested Citation

  • James G. MacKinnon, 1983. "Model Specification Tests Against Non-Nested Alternatives," Working Paper 573, Economics Department, Queen's University.
  • Handle: RePEc:qed:wpaper:573
    as

    Download full text from publisher

    File URL: http://qed.econ.queensu.ca/working_papers/papers/qed_wp_573.pdf
    File Function: First version 1983
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Godfrey, Leslie G & McAleer, Michael & McKenzie, Colin R, 1988. "Variable Addition and LaGrange Multiplier Tests for Linear and Logarithmic Regression Models," The Review of Economics and Statistics, MIT Press, vol. 70(3), pages 492-503, August.
    2. Russell Davidson & James G. MacKinnon, 1985. "Testing Linear and Loglinear Regressions against Box-Cox Alternatives," Canadian Journal of Economics, Canadian Economics Association, vol. 18(3), pages 499-517, August.
    3. Michelis, Leo, 1999. "The distributions of the J and Cox non-nested tests in regression models with weakly correlated regressors," Journal of Econometrics, Elsevier, vol. 93(2), pages 369-401, December.
    4. Godfrey, L. G. & Pesaran, M. H., 1983. "Tests of non-nested regression models: Small sample adjustments and Monte Carlo evidence," Journal of Econometrics, Elsevier, vol. 21(1), pages 133-154, January.
    5. Russell Davidson & James MacKinnon, 2002. "Fast Double Bootstrap Tests Of Nonnested Linear Regression Models," Econometric Reviews, Taylor & Francis Journals, vol. 21(4), pages 419-429.
    6. Godfrey, L. G., 1998. "Tests of non-nested regression models some results on small sample behaviour and the bootstrap," Journal of Econometrics, Elsevier, vol. 84(1), pages 59-74, May.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Davidson, Russell & MacKinnon, James G., 2002. "Bootstrap J tests of nonnested linear regression models," Journal of Econometrics, Elsevier, vol. 109(1), pages 167-193, July.
    2. Hagemann, Andreas, 2012. "A simple test for regression specification with non-nested alternatives," Journal of Econometrics, Elsevier, vol. 166(2), pages 247-254.
    3. Han, Xiaoyi & Lee, Lung-fei, 2013. "Model selection using J-test for the spatial autoregressive model vs. the matrix exponential spatial model," Regional Science and Urban Economics, Elsevier, vol. 43(2), pages 250-271.
    4. McAleer, Michael, 1994. "Sherlock Holmes and the Search for Truth: A Diagnostic Tale," Journal of Economic Surveys, Wiley Blackwell, vol. 8(4), pages 317-370, December.
    5. Luger, Richard, 2006. "Exact permutation tests for non-nested non-linear regression models," Journal of Econometrics, Elsevier, vol. 133(2), pages 513-529, August.
    6. Kenneth Stewart & Kenneth Stewart, 2000. "GNR, MGR, and exact misspeclfication testing," Econometric Reviews, Taylor & Francis Journals, vol. 19(2), pages 233-240.
    7. McAleer, Michael, 1995. "The significance of testing empirical non-nested models," Journal of Econometrics, Elsevier, vol. 67(1), pages 149-171, May.
    8. J. M. C. Santos Silva, 2001. "A score test for non-nested hypotheses with applications to discrete data models," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 16(5), pages 577-597.
    9. West, Kenneth D., 2001. "Encompassing tests when no model is encompassing," Journal of Econometrics, Elsevier, vol. 105(1), pages 287-308, November.
    10. Marzio Galeotti & Alessandro Lanza, 1999. "Desperately Seeking (Environmental) Kuznets," Working Papers 1999.2, Fondazione Eni Enrico Mattei.
    11. Badi H. Baltagi, 1999. "Specification Tests in Panel Data Models Using Artificial Regressions," Annals of Economics and Statistics, GENES, issue 55-56, pages 277-297.
    12. Francisco Cribari-Neto & Sadraque E.F. Lucena, 2015. "Nonnested hypothesis testing in the class of varying dispersion beta regressions," Journal of Applied Statistics, Taylor & Francis Journals, vol. 42(5), pages 967-985, May.
    13. Russell Davidson & James G. MacKinnon, 1988. "Specification Tests Based on Artificial Regressions," Working Paper 707, Economics Department, Queen's University.
    14. Davidson, R. & MacKinnon & J.G., 1999. "Artificial Regressions," G.R.E.Q.A.M. 99a04, Universite Aix-Marseille III.
    15. Nicolas DEBARSY & Cem ERTUR, 2016. "Interaction matrix selection in spatial econometrics with an application to growth theory," LEO Working Papers / DR LEO 2172, Orleans Economics Laboratory / Laboratoire d'Economie d'Orleans (LEO), University of Orleans.
    16. Debarsy, Nicolas & Ertur, Cem, 2019. "Interaction matrix selection in spatial autoregressive models with an application to growth theory," Regional Science and Urban Economics, Elsevier, vol. 75(C), pages 49-69.
    17. Davidson, Russell & MacKinnon, James G, 1988. "Double Length Artificial Regressions," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 50(2), pages 203-217, May.
    18. Beggs, John J, 1988. "Diagnostic Testing in Applied Econometrics," The Economic Record, The Economic Society of Australia, vol. 64(185), pages 81-101, June.
    19. Moheb Ghali & John M. Krieg & K. Surekha Rao, 2011. "A Bayesian Extension of the J-Test for Non-Nested Hypotheses," Journal of Quantitative Economics, The Indian Econometric Society, vol. 9(1), pages 53-72.
    20. Y. K. Tse & Z. L. Yang, 2004. "Tests of Functional Form and Heteroscedasticity," Econometric Society 2004 Far Eastern Meetings 424, Econometric Society.

    More about this item

    Keywords

    Cox test; nonnested hypotheses; J test; specification tests; nonnested hypothesis test;
    All these keywords.

    JEL classification:

    • C10 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - General
    • C12 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Hypothesis Testing: General

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:qed:wpaper:573. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Mark Babcock (email available below). General contact details of provider: https://edirc.repec.org/data/qedquca.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.