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Nonparametric Identification in Structural Economic Models

Author

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  • Rosa L. Matzkin

    () (Department of Economics, University of California, Los Angeles, California 90095)

Abstract

Structural economic models allow one to analyze counterfactuals when economic systems change and to evaluate the well-being of economic agents. A key element in such analysis is the ability to identify the primitive functions and distributions of the economic models that are employed to describe the economic phenomena under study. Recent developments have provided ways to achieve identification of these primitive functions and distributions without imposing parametric restrictions. In this article, I consider a small set of stylized models and provide insight into some of the approaches that have been taken to develop nonparametric identification results in those models.

Suggested Citation

  • Rosa L. Matzkin, 2013. "Nonparametric Identification in Structural Economic Models," Annual Review of Economics, Annual Reviews, vol. 5(1), pages 457-486, May.
  • Handle: RePEc:anr:reveco:v:5:y:2013:p:457-486
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    File URL: http://www.annualreviews.org/doi/abs/10.1146/annurev-economics-082912-110231
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    Citations

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    Cited by:

    1. Manuel Arellano & Stéphane Bonhomme, 2017. "Nonlinear Panel Data Methods for Dynamic Heterogeneous Agent Models," Annual Review of Economics, Annual Reviews, vol. 9(1), pages 471-496, September.
    2. Rolf Aaberge & Ugo Colombino, 2018. "Structural Labour Supply Models and Microsimulation," International Journal of Microsimulation, International Microsimulation Association, vol. 11(1), pages 162-197.
    3. repec:eme:ceapzz:s0573-855520140000293006 is not listed on IDEAS
    4. Matzkin, Rosa L., 2016. "On independence conditions in nonseparable models: Observable and unobservable instruments," Journal of Econometrics, Elsevier, vol. 191(2), pages 302-311.
    5. Rolf Aaberge & Ugo Colombino, 2014. "Labour Supply Models," Contributions to Economic Analysis,in: Handbook of Microsimulation Modelling, volume 127, pages 167-221 Emerald Publishing Ltd.
    6. Hyungsik Roger Moon & Matthew Shum & Martin Weidner, 2012. "Estimation of random coefficients logit demand models with interactive fixed effects," CeMMAP working papers CWP08/12, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    7. Otero, Karina V., 2016. "Nonparametric identification of static multinomial choice models," MPRA Paper 86785, University Library of Munich, Germany.

    More about this item

    Keywords

    nonseparable models; independence; endogeneity; simultaneous equations; control function; instrumental variables;

    JEL classification:

    • C21 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models
    • C23 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Models with Panel Data; Spatio-temporal Models
    • C26 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Instrumental Variables (IV) Estimation
    • C31 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models; Quantile Regressions; Social Interaction Models
    • C36 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Instrumental Variables (IV) Estimation
    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation

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