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Simulation Based Finite- and Large-Sample Inference Methods in Simultaneous Equations

Author

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  • Jean-Marie Dufour

    (Université de Montréal)

  • Lynda Khalaf

    (Université Laval)

Abstract

In the context of multivariate regression (MLR) and simultaneous equations (SE), it is well known that commonly employed asymptotic test criteria are seriously biased towards overrejection. In this paper, we propose finite and large sample likelihood based test procedures for possibly nonlinear hypotheses on the coefficients of SE systems. We discuss a number of bounds tests and Monte Carlo simulations based tests. The latter involves maximizing a randomized p -value function over the relevant nuisance parameter space. This is done numerically by using a simulated annealing algorithm. Illustrative Monte Carlo experiments show that (i) bootstrapping standard instrumental variable (IV) based criteria fails to achieve size control, especially (but not exclusively) under near non-identification conditions, and (ii) the tests based on IV estimates do not appear to be boundedly pivotal and so no size-correction may be feasible. By contrast, likelihood ration based tests work well in the experiments performed.

Suggested Citation

  • Jean-Marie Dufour & Lynda Khalaf, 1999. "Simulation Based Finite- and Large-Sample Inference Methods in Simultaneous Equations," Computing in Economics and Finance 1999 824, Society for Computational Economics.
    • Handle: RePEc:sce:scecf9:824
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    Cited by:

    1. Dufour, Jean-Marie & Khalaf, Lynda, 2002. "Simulation based finite and large sample tests in multivariate regressions," Journal of Econometrics, Elsevier, vol. 111(2), pages 303-322, December.
    2. Jean-Marie Dufour & Pascale Valery, 2000. "Monte Carlo Test Applied to Models Estimated by Indirect Inference," Econometric Society World Congress 2000 Contributed Papers 1667, Econometric Society.
    3. Richard Startz & Charles Nelson & Eric Zivot, 1999. "Improved Inference for the Instrumental Variable Estimator," Econometrics 9905001, University Library of Munich, Germany.
    4. David Aristei & Luca Pieroni, 2005. "Estimating the Role of Government Expenditure in Long-run Consumption," Quaderni del Dipartimento di Economia, Finanza e Statistica 13/2005, Università di Perugia, Dipartimento Economia.

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