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Simulation Based Finite and Large Sample Tests in Multivariate Regressions

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  • Jean-Marie Dufour
  • Lynda Khalaf

Abstract

In the context of multivariate linear regression (MLR) models, it is well known that commonly employed asymptotic test criteria are seriously biased towards overrejection. In this paper, we propose a general method for constructing exact tests of possibly nonlinear hypotheses on the coefficients of MLR systems.
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Suggested Citation

  • Jean-Marie Dufour & Lynda Khalaf, 2000. "Simulation Based Finite and Large Sample Tests in Multivariate Regressions," CIRANO Working Papers 2000s-15, CIRANO.
    • Handle: RePEc:cir:cirwor:2000s-15
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    File URL: https://cirano.qc.ca/files/publications/2000s-15.pdf
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    More about this item

    Keywords

    Multivariate linear regression; seemingly unrelated regressions; uniform linear hypothesis; Monte Carlo test; bounds test. Nonlinear hypothesis; finite sample test; exact test; bootstrap; Modèle de régression multivarié; régressions empilées; hypothès linéaire uniforme; test de Monte Caro; test à borne; hypothèse non linéaire; test à distance finie; test exact; bootstrap;
    All these keywords.

    JEL classification:

    • C3 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables
    • C12 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Hypothesis Testing: General
    • C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Statistical Simulation Methods: General
    • C33 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Models with Panel Data; Spatio-temporal Models
    • O4 - Economic Development, Innovation, Technological Change, and Growth - - Economic Growth and Aggregate Productivity
    • O5 - Economic Development, Innovation, Technological Change, and Growth - - Economywide Country Studies

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