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Finite-Sample Simulation-Based Tests in Seemingly Unrelated Regressions

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Author Info
Dufour, Jean-Marie
Khalaf, Lynda

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Abstract

In this paper, we propose finite and large sample likelihood based test procedures for possibly non-linear hypotheses on the coefficients of SURE systems. Two complementary approaches are described. First, we propose an exact Monte Carlo bounds test based on the standard likelihood ratio criterion. Second, we consider alternative Monte Carlo tests which can be run whenever the bounds are not conclusive. These include, in particular, quasi-likelihood ratio criteria based on non-maximum-likelihood estimators. Illustrative Monte Carlo experiments show that: (i) the bounds are sufficiently tight to yield conclusive results in a large proportion of cases, and (ii) the randomized procedures correct all the usual size distortions in such contexts. The procedures proposed are finally applied to test restrictions on a factor demand model.

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File URL: http://www.green.ecn.ulaval.ca/CahiersGREEN2001/01-05.pdf
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Paper provided by GREEN in its series Cahiers de recherche with number 0105.

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Date of creation: 2001
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Handle: RePEc:lvl:lagrcr:0105

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Related research
Keywords: Multivariate Linear Regression; Seemingly Unrelated Regressions; Monte Carlo Test; Bounds Tests; Nonlinear Hypothesis; Finite-Sample Test; Exact Test; Bootstrap; Factor Demand; Cost Function;

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Find related papers by JEL classification:
C3 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables
C12 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: General - - - Hypothesis Testing
C33 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Models with Panel Data
C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: General - - - Statistical Simulation Methods
O4 - Economic Development, Technological Change, and Growth - - Economic Growth and Aggregate Productivity
O5 - Economic Development, Technological Change, and Growth - - Economywide Country Studies

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  1. Theil, Henri & Shonkwiler, J. S. & Taylor, Timothy G., 1985. "A Monte Carlo test of Slutsky symmetry," Economics Letters, Elsevier, vol. 19(4), pages 331-332. [Downloadable!] (restricted)
  2. Laitinen, Kenneth, 1978. "Why is demand homogeneity so often rejected?," Economics Letters, Elsevier, vol. 1(3), pages 187-191. [Downloadable!] (restricted)
  3. DUFOUR, Jean-Marie & KHALAF, Lynda, 2000. "Simulation-Based Finite and Large Sample Tests in Multivariate Regressions," Cahiers de recherche 2000-10, Universite de Montreal, Departement de sciences economiques. [Downloadable!]
    Other versions:
  4. Berndt, Ernst R & Savin, N Eugene, 1977. "Conflict among Criteria for Testing Hypotheses in the Multivariate Linear Regression Model," Econometrica, Econometric Society, vol. 45(5), pages 1263-77, July. [Downloadable!] (restricted)
  5. Meisner, James F., 1979. "The sad fate of the asymptotic Slutsky symmetry test for large systems," Economics Letters, Elsevier, vol. 2(3), pages 231-233. [Downloadable!] (restricted)
  6. Breusch, T S, 1979. "Conflict among Criteria for Testing Hypotheses: Extensions and Comments," Econometrica, Econometric Society, vol. 47(1), pages 203-07, January. [Downloadable!] (restricted)
  7. Harvey, Andrew C & Phillips, Garry D A, 1982. "Testing for Contemporaneous Correlation of Disturbances in Systems of Regression Equations," Bulletin of Economic Research, Blackwell Publishing, vol. 34(2), pages 79-91, November.
  8. Phillips, Peter C B, 1985. "The Exact Distribution of the SUR Estimator," Econometrica, Econometric Society, vol. 53(4), pages 745-56, July. [Downloadable!] (restricted)
  9. Dufour, Jean-Marie, 2006. "Monte Carlo tests with nuisance parameters: A general approach to finite-sample inference and nonstandard asymptotics," Journal of Econometrics, Elsevier, vol. 133(2), pages 443-477, August. [Downloadable!] (restricted)
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  10. Goffe, William L. & Ferrier, Gary D. & Rogers, John, 1994. "Global optimization of statistical functions with simulated annealing," Journal of Econometrics, Elsevier, vol. 60(1-2), pages 65-99. [Downloadable!] (restricted)
  11. Taylor, Timothy G. & Shonkwiler, J. S. & Theil, Henri, 1986. "Monte Carlo and bootstrap testing of demand homogeneity," Economics Letters, Elsevier, vol. 20(1), pages 55-57. [Downloadable!] (restricted)
  12. Jean-Marie Dufour & Abdeljelil Farhat & Lucien Gardiol & Lynda Khalaf, 1998. "Simulation-based finite sample normality tests in linear regressions," Econometrics Journal, Royal Economic Society, vol. 1(Conferenc), pages C154-C173.
    Other versions:
  13. Dr. Peter Kenning & Hilke Plassmann, 2004. "NeuroEconomics," Experimental 0412005, EconWPA. [Downloadable!]
  14. DUFOUR, Jean-Marie & KHALAF, Lynda, 1998. "Simulation-Based Finite-and Large-sample Inference Methods in Multivariate Regressions and Seemingly Unrelated Regressions," Cahiers de recherche 9813, Universite de Montreal, Departement de sciences economiques. [Downloadable!]
  15. Bera, A. K. & Byron, R. P. & Jarque, C. M., 1981. "Further evidence on asymptotic tests for homogeneity and symmetry in large demand systems," Economics Letters, Elsevier, vol. 8(2), pages 101-105. [Downloadable!] (restricted)
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