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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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    Citations

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    Cited by:

    1. 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(04), pages 593-606, November.
    2. 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).
    3. 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).
    4. 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.
    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. 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(02), December.

    More about this item

    Keywords

    Cox test; Monte Carlo test; Nonnested hypotheses; JEL Classification:C12; C15;

    JEL classification:

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

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