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Monte carlo sampling approach to testing nonnested hypothesis: monte carlo results

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  • N. Coulibaly
  • B. Wade Brorsen

Abstract

Alternative ways of using Monte Carlo methods to implement a Cox-type test for separate families of hypotheses are considered. Monte Carlo experiments are designed to compare the finite sample performances of Pesaran and Pesaran's test, a RESET test, and two Monte Carlo hypothesis test procedures. One of the Monte Carlo tests is based on the distribution of the log-likelihood ratio and the other is based on an asymptotically pivotal statistic. The Monte Carlo results provide strong evidence that the size of the Pesaran and Pesaran test is generally incorrect, except for very large sample sizes. The RESET test has lower power than the other tests. The two Monte Carlo tests perform equally well for all sample sizes and are both clearly preferred to the Pesaran and Pesaran test, even in large samples. Since the Monte Carlo test based on the log-likelihood ratio is the simplest to calculate, we recommend using it.

Suggested Citation

  • N. Coulibaly & B. Wade Brorsen, 1999. "Monte carlo sampling approach to testing nonnested hypothesis: monte carlo results," Econometric Reviews, Taylor & Francis Journals, vol. 18(2), pages 195-209.
    • Handle: RePEc:taf:emetrv:v:18:y:1999:i:2:p:195-209
    DOI: 10.1080/07474939908800439
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    References listed on IDEAS

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    1. 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.
    2. Gourieroux,Christian & Monfort,Alain, 1995. "Statistics and Econometric Models," Cambridge Books, Cambridge University Press, number 9780521471626, October.
    3. White,Halbert, 1996. "Estimation, Inference and Specification Analysis," Cambridge Books, Cambridge University Press, number 9780521574464, September.
    4. Hashem Pesaran, M. & Pesaran, Bahram, 1993. "A simulation approach to the problem of computing Cox's statistic for testing nonnested models," Journal of Econometrics, Elsevier, vol. 57(1-3), pages 377-392.
    5. Jung-Hee Lee & B. Wade Brorsen, 1997. "A non-nested test of GARCH vs. EGARCH models," Applied Economics Letters, Taylor & Francis Journals, vol. 4(12), pages 765-768.
    6. Gourieroux,Christian & Monfort,Alain, 1995. "Statistics and Econometric Models 2 volume set," Cambridge Books, Cambridge University Press, number 9780521478373, July.
    7. 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.
    8. Gwyn Aneuryn-Evans & Angus Deaton, 1980. "Testing Linear versus Logarithmic Regression Models," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 47(1), pages 275-291.
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    Cited by:

    1. Dameus, Alix & Brorsen, B. Wade & Sukhdial, Kullapapruk Piewthongngam & Richter, Francisca G.-C., 2001. "Aids Versus Rotterdam: A Cox Nonnested Test With Parametric Bootstrap," 2001 Annual meeting, August 5-8, Chicago, IL 20453, American Agricultural Economics Association (New Name 2008: Agricultural and Applied Economics Association).
    2. Kapetanios, G. & Weeks, M., 2003. "Non-nested Models and the likelihood Ratio Statistic: A Comparison of Simulation and Bootstrap-based Tests," Cambridge Working Papers in Economics 0308, Faculty of Economics, University of Cambridge.
    3. Berg, Nathan, 2004. "No-decision classification: an alternative to testing for statistical significance," Journal of Behavioral and Experimental Economics (formerly The Journal of Socio-Economics), Elsevier, vol. 33(5), pages 631-650, November.
    4. Park, Seong C. & Brorsen, B. Wade & Stoecker, Arthur L. & Hattey, Jeffory A., 2012. "Forage Response to Swine Effluent: A Cox Nonnested Test of Alternative Functional Forms Using a Fast Double Bootstrap," Journal of Agricultural and Applied Economics, Cambridge University Press, vol. 44(4), pages 593-606, November.
    5. Kapetanios, G. & Weeks, M., 2003. "Non-nested Models and the likelihood Ratio Statistic: A Comparison of Simulation and Bootstrap-based Tests," Cambridge Working Papers in Economics 0308, Faculty of Economics, University of Cambridge.
    6. Kaitibie, Simeon & Nganje, William E. & Brorsen, B. Wade & Epplin, Francis M., 2003. "Optimal Grazing Pressure Under Output Price And Production Uncertainty With Alternative Functional Forms," 2003 Annual meeting, July 27-30, Montreal, Canada 22020, American Agricultural Economics Association (New Name 2008: Agricultural and Applied Economics Association).
    7. Dameus, Alix & Richter, Francisca G.-C. & Brorsen, B. Wade & Sukhdial, Kullapapruk Piewthongngam, 2002. "Aids Versus The Rotterdam Demand System: A Cox Test With Parametric Bootstrap," Journal of Agricultural and Resource Economics, Western Agricultural Economics Association, vol. 27(2), pages 1-13, December.

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    More about this item

    Keywords

    Cox test; Monte Carlo test; Nonnested hypotheses; JEL Classification:C12; C15;
    All these keywords.

    JEL classification:

    • C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Statistical Simulation Methods: General

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