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Simulation Estimation Methods for Limited Dependent Variable Models



This chapter discusses simulation estimation methods that overcome the computational intractability of classical estimation of limited dependent variable models with flexible correlation structures in the unobservable stochastic terms. These difficulties arise because of the need to evaluate accurately very high dimensional integrals. The methods based on simulation do not require the exact evaluation of these integrals and hence are feasible using computers of even moderate power. I first discuss a series of ideas that had been used in efforts to circumvent these computational problems by employing standard numerical analysis approximation methods. I then show how simulation techniques solve the computational problems without the need to resort to either generally unsatisfactory numerical approximations. All currently known simulation algorithms are then compared in terms of theoretical properties and practical performance.

Suggested Citation

  • Vassilis A. Hajivassiliou, 1991. "Simulation Estimation Methods for Limited Dependent Variable Models," Cowles Foundation Discussion Papers 1007, Cowles Foundation for Research in Economics, Yale University.
  • Handle: RePEc:cwl:cwldpp:1007
    Note: CFP 862.

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    2. Gardebroek, Cornelis & Oude Lansink, Alfons G.J.M., 2008. "Dynamic Microeconometric Approaches To Analysing Agricultural Policy," 107th Seminar, January 30-February 1, 2008, Sevilla, Spain 6592, European Association of Agricultural Economists.
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    5. Calzolari, Giorgio & Neri, Laura, 2002. "Imputation of continuous variables missing at random using the method of simulated scores," MPRA Paper 22986, University Library of Munich, Germany, revised 2002.
    6. Steven T. Yen & Biing-Hwan Lin, 2006. "A Sample Selection Approach to Censored Demand Systems," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 88(3), pages 742-749.
    7. Velandia, Margarita M. & Lambert, Dayton M. & Jenkins, Amanda & Roberts, Roland K. & Larson, James A. & English, Burton C. & Martin, Steven W., 2009. "Factors Influencing Selection of Information Sources by Cotton Producers Considering Adoption of Precision Agriculture Technologies," 2009 Annual Meeting, July 26-28, 2009, Milwaukee, Wisconsin 49326, Agricultural and Applied Economics Association.
    8. Paul Gertler & Roland Sturm & Bruce Davidson, 1994. "Information and the Demand for Supplemental Medicare Insurance," NBER Working Papers 4700, National Bureau of Economic Research, Inc.
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    11. Falk, Martin, 1999. "Technological innovations and the expected demand for skilled labour at the firm level," ZEW Discussion Papers 99-59, ZEW - Zentrum für Europäische Wirtschaftsforschung / Center for European Economic Research.
    12. Inkmann, Joachim, 2000. "Misspecified heteroskedasticity in the panel probit model: A small sample comparison of GMM and SML estimators," Journal of Econometrics, Elsevier, vol. 97(2), pages 227-259, August.
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    14. Vassilis Argyrou Hajivassiliou, 1993. "Simulating Normal Rectangle Probabilities and Their Derivatives: The Effects of Vectorization," Working Papers _025, Yale University.
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    17. Chuanming Gao & Kajal Lahiri & Bernard Wixon, 2000. "Value of Sample Separation Information in a Sequential Probit Model: Another Look at SSA's Disability Determination Process," Econometric Society World Congress 2000 Contributed Papers 0340, Econometric Society.
    18. Raja Chakir & Alban Thomas, 2003. "Simulated maximum likelihood estimation of demand systems with corner solutions and panel data application to industrial energy demand," Revue d'économie politique, Dalloz, vol. 113(6), pages 773-799.
    19. Lesley Chiou, 2005. "Empirical Analysis of Retail Competition: Spatial Differentiation at Wal-Mart,, and Their Competitors," Occidental Economics Working Papers 3, Occidental College, Department of Economics, revised Nov 2006.
    20. Davis, Christopher G. & Yen, Steven T. & Lin, Biing-Hwan, 2007. "Does Consumer Knowledge Affect Meat Consumption in the US?," 2007 Annual Meeting, February 4-7, 2007, Mobile, Alabama 34905, Southern Agricultural Economics Association.
    21. Vassilis A. Hajivassiliou & Daniel McFadden & Paul A. Ruud, 1994. "Simulation of Multivariate Normal Rectangle Probabilities: Theoretical and Computational Results," Cowles Foundation Discussion Papers 1021R, Cowles Foundation for Research in Economics, Yale University.

    More about this item


    Simulation methods; econometric modeling; model estimation; correlation structures;

    JEL classification:

    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
    • C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Statistical Simulation Methods: General
    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation


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