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Searching for Rehabilitation in Nonparametric Regression Models with Exogenous Treatment Assignment

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Author Info

  • 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.

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Bibliographic Info

Paper provided by Institute for the Study of Labor (IZA) in its series IZA Discussion Papers with number 6874.

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Length: 27 pages
Date of creation: Sep 2012
Date of revision:
Publication status: published in: Jeffrey Racine, Liangjun Su and Aman Ullah (eds.), Oxford Handbook of Nonparametric and Semiparametric Econometrics and Statistics, Oxford: OUP, 2014
Handle: RePEc:iza:izadps:dp6874

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Related research

Keywords: bootstrap; stochastic dominance; nonparametric; treatment effects; rehabilitation; GAIN;

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  1. Linton, Oliver & Maasoumi, Esfandiar & Whang, Yoon-Jae, 2003. "Consistent Testing for Stochastic Dominance under General Sampling Schemes," SFB 373 Discussion Papers, Humboldt University of Berlin, Interdisciplinary Research Project 373: Quantification and Simulation of Economic Processes 2003,31, Humboldt University of Berlin, Interdisciplinary Research Project 373: Quantification and Simulation of Economic Processes.
  2. Henderson, Daniel J., 2008. "A Test for Multimodality of Regression Derivatives with an Application to Nonparametric Growth Regressions," MPRA Paper, University Library of Munich, Germany 8768, University Library of Munich, Germany.
  3. Ozkan Eren & Daniel J. Henderson, 2008. "The impact of homework on student achievement," Econometrics Journal, Royal Economic Society, Royal Economic Society, vol. 11(2), pages 326-348, 07.
  4. Henderson, Daniel J. & Kumbhakar, Subal C. & Parmeter, Christopher F., 2012. "A Simple Method to Visualize Results in Nonlinear Regression Models," IZA Discussion Papers, Institute for the Study of Labor (IZA) 6781, Institute for the Study of Labor (IZA).
  5. Racine, Jeff & Li, Qi, 2004. "Nonparametric estimation of regression functions with both categorical and continuous data," Journal of Econometrics, Elsevier, Elsevier, vol. 119(1), pages 99-130, March.
  6. Maasoumi, Esfandiar & Racine, Jeff, 2006. "Growth And Convergence: A Profile Of Distribution Dynamics And Mobility," Departmental Working Papers, Southern Methodist University, Department of Economics 0605, Southern Methodist University, Department of Economics.
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