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Estimating Simultaneous Equations Models by a Simulation Technique


  • Kang, Heejoon


For simultaneous equations models, estimates from ordinary least squares (OLS) methods are biased and even inconsistent and those from two-stage least squares (2SLS) methods are, though consistent, still inadequate because of finite sample biases. A new simulation technique developed here produces better estimates by compensating for the simultaneous bias in those conventional estimation methods. In the simulation (SIM) estimation technique, such biases are directly compensated in situ through a synthetic use of simulations. SIM is demonstrated to outperform the existing techniques through a series of Monte Carlo experiments. Klein's macroeconomic model is used to further illustrate the practical application of SIM. The results are compared with those from OLS and 2SLS to elaborate the attractive performance of SIM. Citation Copyright 1995 by Kluwer Academic Publishers.

Suggested Citation

  • Kang, Heejoon, 1995. "Estimating Simultaneous Equations Models by a Simulation Technique," Computational Economics, Springer;Society for Computational Economics, vol. 8(4), pages 255-265, November.
  • Handle: RePEc:kap:compec:v:8:y:1995:i:4:p:255-65

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    References listed on IDEAS

    1. Collard, Fabrice & Juillard, Michel, 2001. "Accuracy of stochastic perturbation methods: The case of asset pricing models," Journal of Economic Dynamics and Control, Elsevier, vol. 25(6-7), pages 979-999, June.
    2. Karp, Larry S., 1985. "Higher moments in the linear-quadratic-gaussian problem," Journal of Economic Dynamics and Control, Elsevier, vol. 9(1), pages 41-54, September.
    3. Peter A. Zadrozny & Baoline Chen, 1999. "Perturbation Solution of Nonlinear Rational Expectations Models," Computing in Economics and Finance 1999 334, Society for Computational Economics.
    4. Baoline Chen & A. Zadrozny, 2000. "Estimated U.S. Manufacturing Capital And Productivity Based On An Estimated Dynamic Economic Model," Computing in Economics and Finance 2000 133, Society for Computational Economics.
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