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Simulation-based estimation of Tobit model with random effects

Author

Listed:
  • Calzolari, Giorgio
  • Magazzini, Laura
  • Mealli, Fabrizia
Abstract
The estimation of limited dependent variable panel data models usually involves objective functions in which integrals appear without a closed form solution: this is the case of the panel data Tobit model with random effects. Recently, simulation methods have shown to be useful in the inference process, as they offer methods to approximate such integrals (Laroque, Salanie, 1989; Gouri´eroux, Monfort, 1991, 1993; Hajivassiliou, McFadden, 1998; Mealli, Rampichini, 1999; Inkmann, 2000). Although the asymptotic performances of such methods are known and their application has been successfully undertaken, more precise ideas on their finite sample performance and computational efficiency is still needed. In this paper we propose to use the method of indirect inference, using different auxiliary models, and the simulated maximum likelihood to estimate these models. We use a panel data Tobit model with a simple correlation structure in the unobservables (i.e. a one-factor structure), but the model could be easily extended. Using both simulated and real data, we show the perfomances of the proposed methods in finite samples. The application on real data is concerned with a model of female labour supply.

Suggested Citation

  • Calzolari, Giorgio & Magazzini, Laura & Mealli, Fabrizia, 2001. "Simulation-based estimation of Tobit model with random effects," MPRA Paper 22985, University Library of Munich, Germany, revised 2001.
    • Handle: RePEc:pra:mprapa:22985
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    File URL: https://mpra.ub.uni-muenchen.de/22985/1/MPRA_paper_22985.pdf
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    References listed on IDEAS

    as
    1. Gallant, A. Ronald & Tauchen, George, 1996. "Which Moments to Match?," Econometric Theory, Cambridge University Press, vol. 12(4), pages 657-681, October.
    2. Honore, Bo E., 1993. "Orthogonality conditions for Tobit models with fixed effects and lagged dependent variables," Journal of Econometrics, Elsevier, vol. 59(1-2), pages 35-61, September.
    3. Honore, Bo E, 1992. "Trimmed LAD and Least Squares Estimation of Truncated and Censored Regression Models with Fixed Effects," Econometrica, Econometric Society, vol. 60(3), pages 533-565, May.
    4. Gourieroux, Christian & Monfort, Alain, 1993. "Simulation-based inference : A survey with special reference to panel data models," Journal of Econometrics, Elsevier, vol. 59(1-2), pages 5-33, September.
    5. Gourieroux, C & Monfort, A & Renault, E, 1993. "Indirect Inference," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 8(S), pages 85-118, Suppl. De.
    6. Laroque, Guy & Salanie, Bernard, 1989. "Estimation of Multi-market Fix-Price Models: An Application of Pseudo Maximum Likelihood Methods," Econometrica, Econometric Society, vol. 57(4), pages 831-860, July.
    7. Vassilis A. Hajivassiliou & Daniel L. McFadden, 1998. "The Method of Simulated Scores for the Estimation of LDV Models," Econometrica, Econometric Society, vol. 66(4), pages 863-896, July.
    8. Mealli, Fabrizia & Rampichini, Carla, 1999. "Estimating binary multilevel models through indirect inference," Computational Statistics & Data Analysis, Elsevier, vol. 29(3), pages 313-324, January.
    9. James J. Heckman & Thomas E. Macurdy, 1980. "A Life Cycle Model of Female Labour Supply," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 47(1), pages 47-74.
    10. Christian Gouriéroux & Alain Monfort, 1991. "Simulation Based Inference in Models with Heterogeneity," Annals of Economics and Statistics, GENES, issue 20-21, pages 69-107.
    11. 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.
    12. repec:adr:anecst:y:1991:i:20-21:p:04 is not listed on IDEAS
    13. Giorgio Calzolari & F. Di Iorio & G. Fiorentini, 1999. "Indirect Estimation of Just-Identified Models with Control Variates," Econometrics Working Papers Archive quaderno46, Universita' degli Studi di Firenze, Dipartimento di Statistica, Informatica, Applicazioni "G. Parenti".
    14. Amemiya, Takeshi, 1984. "Tobit models: A survey," Journal of Econometrics, Elsevier, vol. 24(1-2), pages 3-61.
    15. G. S. Maddala, 1987. "Limited Dependent Variable Models Using Panel Data," Journal of Human Resources, University of Wisconsin Press, vol. 22(3), pages 307-338.
    Full references (including those not matched with items on IDEAS)

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    Cited by:

    1. Luca Romagnoli & Vincenzo Giaccio & Luigi Mastronardi & Maria Bonaventura Forleo, 2021. "Highlighting the Drivers of Italian Diversified Farms Efficiency: A Two-Stage DEA-Panel Tobit Analysis," Sustainability, MDPI, vol. 13(23), pages 1-16, November.
    2. Campus, Daniela & Giannelli, Gianna Claudia, 2016. "Is the Allocation of Time Gender Sensitive to Food Price Changes? An Investigation of Hours of Work in Uganda," IZA Discussion Papers 10376, Institute of Labor Economics (IZA).
    3. Campus, Daniela, 2017. "Gender differentials in agricultural productivity: an empirical evidence from Uganda," 2017 Sixth AIEAA Conference, June 15-16, Piacenza, Italy 261259, Italian Association of Agricultural and Applied Economics (AIEAA).
    4. Geoffrey Norman Tumwine & Razack B Lokina & John Mary Matovu, 2019. "The Effect of Climate Change on Agricultural Crop Returns in Uganda," Journal of Economics and Behavioral Studies, AMH International, vol. 11(4), pages 71-87.

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    More about this item

    Keywords

    Tobit model; random effects; simulation based estimation; indirect estimation;
    All these keywords.

    JEL classification:

    • C24 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Truncated and Censored Models; Switching Regression Models; Threshold Regression Models
    • C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Statistical Simulation Methods: General
    • C21 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models

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