Simulation Based Finite- and Large-Sample Inference Methods in Simultaneous Equations
AbstractIn 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.
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Bibliographic InfoPaper provided by Society for Computational Economics in its series Computing in Economics and Finance 1999 with number 824.
Date of creation: 01 Mar 1999
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- Richard Startz & Charles Nelson & Eric Zivot, 1999.
"Improved Inference for the Instrumental Variable Estimator,"
Discussion Papers in Economics at the University of Washington
0039, Department of Economics at the University of Washington.
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- 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, Finanza e Statistica.
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- 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.
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