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Comment on “Simulation and Estimation of Hedonic Models” by Heckman, Matzkin and Nesheim

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  • Keane, Michael

Abstract

This paper contains commentary on the chapter “Simulation and Estimation of Hedonic Models” by Heckman, Matzkin and Nesheim that appeared in the volume "Frontiers in Applied General Equilibrium Modeling," Kehoe, Srinivasan and Whalley (eds.), Cambridge University Press, 2005. I explain in simple terms the sources of identification in their model. I also argue for the importance of economic theory in empirical work in general, and criticize various attempts to do "theory-free" analysis.

Suggested Citation

  • Keane, Michael, 2003. "Comment on “Simulation and Estimation of Hedonic Models” by Heckman, Matzkin and Nesheim," MPRA Paper 55141, University Library of Munich, Germany.
    • Handle: RePEc:pra:mprapa:55141
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    File URL: https://mpra.ub.uni-muenchen.de/55141/1/MPRA_paper_55141.pdf
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    References listed on IDEAS

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    1. William A. Barnett & Michael Wolfe, 2004. "Semi-nonparametric Bayesian Estimation of the Asymptotically Ideal Production Model1," Contributions to Economic Analysis, in: Functional Structure and Approximation in Econometrics, pages 303-349, Emerald Group Publishing Limited.
    2. Ivar Ekeland & James J. Heckman & Lars Nesheim, 2004. "Identification and Estimation of Hedonic Models," Journal of Political Economy, University of Chicago Press, vol. 112(S1), pages 60-109, February.
    3. Stephen Gordon, 1996. "Using Mixtures of Flexible Functional Forms to Estimate Factor Demand Elasticities," Canadian Journal of Economics, Canadian Economics Association, vol. 29(3), pages 717-736, August.
    4. Lancaster, Tony, 1997. "Exact Structural Inference in Optimal Job-Search Models," Journal of Business & Economic Statistics, American Statistical Association, vol. 15(2), pages 165-179, April.
    5. James Heckman, 1997. "Instrumental Variables: A Study of Implicit Behavioral Assumptions Used in Making Program Evaluations," Journal of Human Resources, University of Wisconsin Press, vol. 32(3), pages 441-462.
    6. Angrist, Joshua D. & Krueger, Alan B., 1999. "Empirical strategies in labor economics," Handbook of Labor Economics, in: O. Ashenfelter & D. Card (ed.), Handbook of Labor Economics, edition 1, volume 3, chapter 23, pages 1277-1366, Elsevier.
    7. Hsiao,Cheng & Pesaran,M. Hashem & Lahiri,Kajal & Lee,Lung Fei (ed.), 1999. "Analysis of Panels and Limited Dependent Variable Models," Cambridge Books, Cambridge University Press, number 9780521631693.
    8. Romeo, Charles J, 1999. "Conducting Inference in Semiparametric Duration Models under Inequality Restrictions on the Shape of the Hazard Implied by Job Search Theory," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 14(6), pages 587-605, Nov.-Dec..
    9. Geweke, John & Keane, Michael, 2001. "Computationally intensive methods for integration in econometrics," Handbook of Econometrics, in: J.J. Heckman & E.E. Leamer (ed.), Handbook of Econometrics, edition 1, volume 5, chapter 56, pages 3463-3568, Elsevier.
    10. Keane, Michael P, 1992. "A Note on Identification in the Multinomial Probit Model," Journal of Business & Economic Statistics, American Statistical Association, vol. 10(2), pages 193-200, April.
    11. Heckman, James J & Sedlacek, Guilherme, 1985. "Heterogeneity, Aggregation, and Market Wage Functions: An Empirical Model of Self-selection in the Labor Market," Journal of Political Economy, University of Chicago Press, vol. 93(6), pages 1077-1125, December.
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    Cited by:

    1. Keane, Michael P., 2010. "Structural vs. atheoretic approaches to econometrics," Journal of Econometrics, Elsevier, vol. 156(1), pages 3-20, May.

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    More about this item

    Keywords

    hedonic models; identification; Bayesian semi-parametrics; structural model; theory based empirical analysis; functional form assumptions; instrumental variables; mixture-of-normals; flexible parametric models;
    All these keywords.

    JEL classification:

    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General

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