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Estimation of simultaneous equation models with latent dependent variables: a Monte Carlo evaluation

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  • Lixin Cai

Abstract

Three methods can be used to estimate simultaneous equation models with latent dependent variables: two-stage, minimum distance (MD) and full-information maximum likelihood. Theoretically all the three methods provide asymptotically consistent estimates, but the performance of these estimators in finite samples cannot be determined in theory. This letter evaluates the performance of these estimators in finite samples using Monte Carlo simulation. The results show that the MD estimator performs very poorly; overall the full information maximum likelihood estimator performs better than the other two estimators.

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  • Lixin Cai, 2009. "Estimation of simultaneous equation models with latent dependent variables: a Monte Carlo evaluation," Applied Economics Letters, Taylor & Francis Journals, vol. 16(11), pages 1107-1112.
    • Handle: RePEc:taf:apeclt:v:16:y:2009:i:11:p:1107-1112
    DOI: 10.1080/13504850701335368
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    1. Lee, Myoung-Jae, 1995. "Semi-parametric Estimation of Simultaneous Equations with Limited Dependent Variables: A Case Study of Female Labour Supply," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 10(2), pages 187-200, April-Jun.
    2. Steven Stern, 1989. "Measuring the Effect of Disability on Labor Force Participation," Journal of Human Resources, University of Wisconsin Press, vol. 24(3), pages 361-395.
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