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An Introduction to Nonparametric Regression for Labor Economists

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  • Henderson, Daniel J.

    (University of Alabama)

  • Souto, Anne-Charlotte

    (University of Alabama)

Abstract

In this article we overview nonparametric (spline and kernel) regression methods and illustrate how they may be used in labor economic applications. We focus our attention on issues commonly found in the labor literature such as how to account for endogeneity via instrumental variables in a nonparametric setting. We showcase these methods via data from the Current Population Survey.

Suggested Citation

  • Henderson, Daniel J. & Souto, Anne-Charlotte, 2018. "An Introduction to Nonparametric Regression for Labor Economists," IZA Discussion Papers 11914, Institute of Labor Economics (IZA).
    • Handle: RePEc:iza:izadps:dp11914
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    1. Henderson, Daniel J. & Polachek, Solomon W. & Wang, Le, 2011. "Heterogeneity in schooling rates of return," Economics of Education Review, Elsevier, vol. 30(6), pages 1202-1214.
    2. Hall, Peter G. & Racine, Jeffrey S., 2015. "Infinite order cross-validated local polynomial regression," Journal of Econometrics, Elsevier, vol. 185(2), pages 510-525.
    3. Henderson,Daniel J. & Parmeter,Christopher F., 2015. "Applied Nonparametric Econometrics," Cambridge Books, Cambridge University Press, number 9781107010253, January.
    4. Whitney K. Newey & James L. Powell & Francis Vella, 1999. "Nonparametric Estimation of Triangular Simultaneous Equations Models," Econometrica, Econometric Society, vol. 67(3), pages 565-604, May.
    5. Shujie Ma & Jeffrey S. Racine & Lijian Yang, 2015. "Spline Regression in the Presence of Categorical Predictors," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 30(5), pages 705-717, August.
    6. Deniz Ozabaci & Daniel J. Henderson & Liangjun Su, 2014. "Additive Nonparametric Regression in the Presence of Endogenous Regressors," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 32(4), pages 555-575, October.
    7. Ruppert,David & Wand,M. P. & Carroll,R. J., 2003. "Semiparametric Regression," Cambridge Books, Cambridge University Press, number 9780521780506, November.
    8. Su, Liangjun & Ullah, Aman, 2008. "Local polynomial estimation of nonparametric simultaneous equations models," Journal of Econometrics, Elsevier, vol. 144(1), pages 193-218, May.
    9. Hayfield, Tristen & Racine, Jeffrey S., 2008. "Nonparametric Econometrics: The np Package," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 27(i05).
    10. A. Colin Cameron & Pravin K. Trivedi, 2010. "Microeconometrics Using Stata, Revised Edition," Stata Press books, StataCorp LP, number musr.
    11. Ruppert,David & Wand,M. P. & Carroll,R. J., 2003. "Semiparametric Regression," Cambridge Books, Cambridge University Press, number 9780521785167, November.
    12. Inyoung Kim & Noah D. Cohen & Raymond J. Carroll, 2003. "Semiparametric Regression Splines in Matched Case-Control Studies," Biometrics, The International Biometric Society, vol. 59(4), pages 1158-1169, December.
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    Cited by:

    1. Teresa D. Harrison & Daniel J. Henderson & Deniz Ozabaci & Christopher A. Laincz, 2023. "Does one size fit all in the non‐profit donation production function?," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 85(2), pages 373-402, April.
    2. Daniel J. Henderson & Anne-Charlotte Souto & Le Wang, 2020. "Higher-Order Risk–Returns to Education," JRFM, MDPI, vol. 13(11), pages 1-25, October.
    3. Girard, Alexandre & Gnabo, Jean-Yves & Londoño van Rutten, Rodrigo, 2023. "Firm performance and the crowd effect in lobbying competition," Finance Research Letters, Elsevier, vol. 53(C).
    4. Hervé Cardot & Antonio Musolesi, 2021. "Zero-inflated regression for unobserved effects panel data models and difference-in-differences estimation," SEEDS Working Papers 1121, SEEDS, Sustainability Environmental Economics and Dynamics Studies, revised Dec 2021.
    5. Shr, Yau-Huo & Hsu, Wen & Hwang, Bing-Fang & Jung, Chau-Ren, 2023. "Air quality and risky behaviors on roads," Journal of Environmental Economics and Management, Elsevier, vol. 118(C).
    6. Masayuki Hirukawa & Irina Murtazashvili & Artem Prokhorov, 2022. "Uniform convergence rates for nonparametric estimators smoothed by the beta kernel," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 49(3), pages 1353-1382, September.
    7. Wen Hsu & Bing-Fang Hwang & Chau-Ren Jung & Yau-Huo Jimmy Shr, 2021. "Can Air Pollution Save Lives? Air Quality and Risky Behaviors on Roads," Papers 2111.06837, arXiv.org, revised Dec 2021.

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    More about this item

    Keywords

    endogeneity; kernel; labor; nonparametic; regression; spline;
    All these keywords.

    JEL classification:

    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • C26 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Instrumental Variables (IV) Estimation
    • I24 - Health, Education, and Welfare - - Education - - - Education and Inequality
    • J24 - Labor and Demographic Economics - - Demand and Supply of Labor - - - Human Capital; Skills; Occupational Choice; Labor Productivity
    • J31 - Labor and Demographic Economics - - Wages, Compensation, and Labor Costs - - - Wage Level and Structure; Wage Differentials

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