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Searching for Rehabilitation in Nonparametric Regression Models with Exogenous Treatment Assignment

Author

Listed:
  • Henderson, Daniel J.

    (University of Alabama)

  • Maasoumi, Esfandiar

    (Emory University)

Abstract

This paper offers some new directions in the analysis of nonparamertric models with exogenous treatment assignment. The nonparametric approach opens the door to the examination of potentially different distributed outcomes. When combined with cross-validation, it also identifies potentially irrelevant variables and linear versus nonlinear effects. Examination of the distribution of effects requires distribution metrics, such as stochastic dominance tests for ranking based on a wide range of criterion functions, including dollar valuations. We can identify subgroups with different treatment outcomes. We offer an empirical demonstration based on the GAIN data. In the case of one covariate (English as the primary language), there is support for a statistical inference of uniform first order dominant treatment effects. We also find several others that indicate second and higher order dominance rankings to a statistical degree of confidence.

Suggested Citation

  • Henderson, Daniel J. & Maasoumi, Esfandiar, 2012. "Searching for Rehabilitation in Nonparametric Regression Models with Exogenous Treatment Assignment," IZA Discussion Papers 6874, IZA Network @ LISER.
    • Handle: RePEc:iza:izadps:dp6874
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    References listed on IDEAS

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    1. Oliver Linton & Esfandiar Maasoumi & Yoon-Jae Whang, 2005. "Consistent Testing for Stochastic Dominance under General Sampling Schemes," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 72(3), pages 735-765.
    2. Gary S. Becker, 1981. "A Treatise on the Family," NBER Books, National Bureau of Economic Research, Inc, number beck81-1, January.
    3. Maasoumi, Esfandiar & Racine, Jeff & Stengos, Thanasis, 2007. "Growth and convergence: A profile of distribution dynamics and mobility," Journal of Econometrics, Elsevier, vol. 136(2), pages 483-508, February.
    4. Henderson, Daniel J. & Kumbhakar, Subal C. & Parmeter, Christopher F., 2012. "A simple method to visualize results in nonlinear regression models," Economics Letters, Elsevier, vol. 117(3), pages 578-581.
    5. Racine, Jeff & Li, Qi, 2004. "Nonparametric estimation of regression functions with both categorical and continuous data," Journal of Econometrics, Elsevier, vol. 119(1), pages 99-130, March.
    6. Dehejia, Rajeev H, 2003. "Was There a Riverside Miracle? A Hierarchical Framework for Evaluating Programs with Grouped Data," Journal of Business & Economic Statistics, American Statistical Association, vol. 21(1), pages 1-11, January.
    7. Daniel J. Henderson, 2010. "A test for multimodality of regression derivatives with application to nonparametric growth regressions," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 25(3), pages 458-480.
    8. Ozkan Eren & Daniel J. Henderson, 2008. "The impact of homework on student achievement," Econometrics Journal, Royal Economic Society, vol. 11(2), pages 326-348, July.
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    Citations

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    Cited by:

    1. Daniel J. Henderson & Léopold Simar & Le Wang, 2017. "The three s of public schools: irrelevant inputs, insufficient resources and inefficiency," Applied Economics, Taylor & Francis Journals, vol. 49(12), pages 1164-1184, March.
    2. Deniz Ozabaci & Daniel Henderson, 2015. "Additive kernel estimates of returns to schooling," Empirical Economics, Springer, vol. 48(1), pages 227-251, February.
    3. Durga P. Gautam, 2014. "Remittances and Governance: Does the Government Free Ride?," Working Papers 14-40, Department of Economics, West Virginia University.

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    JEL classification:

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

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