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Some Practical Issues in Maximum Simulated Likelihood
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Some Practical Issues in Maximum Simulated Likelihood

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  • V A Hajivassiliou

Abstract

In this paper, I explore ways of recapturing the efficiency property for estimators that rely on simulation. In particular, I show that this can be achieved by exploiting two-step maximum stimulated likelihood (SL) estimation methods that are familiar from classical applications. I also construct a diagnostic test for adequacy of number of simulations employed to guarantee negligible bias for the MSL and provide some evidence on the computational requirements of the Geweke-Hajivassiliou-Keane (GHK) simulator as a function of (a) the dimension of the problem and (b) the number of simulations employed in a vectorized context. I outline how one can derive a similar approach for checking the adequacy of the number of Gibbs resamplings in simulation estimation methods the employ this technique. This chapter also shows how to suitably introduce simulation into classical hypothesis testing methods and provide test statistics (simulated Wald, Lagrange Multiplier, and Likelihood Ratio Tests) that are free of influential simulation noise. Finally, I explain how simulation-variance-reduction techniques, notably antithetics, can improve substantially the practical performance of the GHK simulator and present extensive Monte-Carlo evidence confirming this.

Suggested Citation

  • V A Hajivassiliou, 1997. "Some Practical Issues in Maximum Simulated Likelihood," STICERD - Econometrics Paper Series 340, Suntory and Toyota International Centres for Economics and Related Disciplines, LSE.
    • Handle: RePEc:cep:stiecm:340
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    Cited by:

    1. Carlos Arias & Thomas Cox, 2001. "Estimation of a US dairy sector model by maximum simulated likelihood," Applied Economics, Taylor & Francis Journals, vol. 33(9), pages 1201-1211.
    2. Pilar García‐Gómez & Erik Schokkaert & Tom Van Ourti & Teresa Bago d'Uva, 2015. "Inequity in the Face of Death," Health Economics, John Wiley & Sons, Ltd., vol. 24(10), pages 1348-1367, October.
    3. Falk, Martin, 1999. "Technological innovations and the expected demand for skilled labour at the firm level," ZEW Discussion Papers 99-59, ZEW - Leibniz Centre for European Economic Research.
    4. Otto Toivanen & Michael Waterson, 2005. "Market Structure and Entry: Where's the Beef?," RAND Journal of Economics, The RAND Corporation, vol. 36(3), pages 680-699, Autumn.
    5. de Lapparent, M., & Axhausen , K.W. & Frei, A., 2013. "Long distance mode choice and distributions of values of travel time savings in three European countries," European Transport \ Trasporti Europei, ISTIEE, Institute for the Study of Transport within the European Economic Integration, issue 53, pages 1-7.
    6. Lorenzo Cappellari & Stephen P. Jenkins, 2003. "Multivariate probit regression using simulated maximum likelihood," Stata Journal, StataCorp LP, vol. 3(3), pages 278-294, September.
    7. Ronald Goettler & Ron Shachar, 2000. "Estimating Product Characteristics and Spatial Competition in the Network Television Industry," Econometric Society World Congress 2000 Contributed Papers 1691, Econometric Society.
    8. Kankwamba, Henry & Mangisoni, Julius H. & Simtowe, Franklin & Mausch, Kai & Siambi, Moses, 2012. "Improved Legume Seed Demand Systems in Central Malawi: What Do Farmers' Seed Expenditures Say about Their Preferences?," 2012 Conference, August 18-24, 2012, Foz do Iguacu, Brazil 123945, International Association of Agricultural Economists.

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