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Mean-squared-error Calculations for Average Treatment Effects

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Author Info

  • Guido W. Imbens

    ()
    (Department of Economics, UC Berkeley and NBER)

  • Whitney Newey

    ()
    (Department of Economics, MIT)

  • Geert Ridder

    ()
    (Department of Economics, University of Southern California)

Abstract

This paper develops a new nonparametric series estimator for the average treatment effect for the case with unconfounded treatment assignment, that is, where selection for treatment is on observables. The new estimator is efficient. In addition we develop an optimal procedure for choosing the smoothing parameter, the number of terms in the series by minimizing the mean squared error (MSE). The new estimator is linear in the first-stage nonparametric estimator. This simplifies the derivation of the MSE of the estimator as a function of the number of basis functions that is used in the first stage nonparametric regression. We propose an estimator for the MSE and show that in large samples minimization of this estimator is equivalent to minimization of the population MSE.

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File URL: http://www.usc.edu/dept/LAS/economics/IEPR/Working%20Papers/IEPR_06.56_%5BMoon,%20Schorfheide%5D.pdf
File Function: First version, 2006
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Bibliographic Info

Paper provided by Institute of Economic Policy Research (IEPR) in its series IEPR Working Papers with number 06.57.

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Length: 48 pages
Date of creation: Nov 2006
Date of revision:
Handle: RePEc:scp:wpaper:06-57

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Web page: http://www.usc.edu/dept/LAS/economics/IEPR/
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Related research

Keywords: Nonparametric Estimation; Imputation; Mean Squared Error; Order Selection;

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Cited by:
  1. Bryan S. Graham & Cristine Campos De Xavier Pinto & Daniel Egel, 2012. "Inverse Probability Tilting for Moment Condition Models with Missing Data," Review of Economic Studies, Oxford University Press, vol. 79(3), pages 1053-1079.
  2. Richard K. Crump & V. Joseph Hotz & Guido W. Imbens & Oscar A. Mitnik, 2006. "Moving the Goalposts: Addressing Limited Overlap in Estimation of Average Treatment Effects by Changing the Estimand," Working Papers 0608, University of Miami, Department of Economics.
  3. Richard K. Crump & V. Joseph Hotz & Guido W. Imbens & Oscar A. Mitnik, 2006. "Nonparametric Tests for Treatment Effect Heterogeneity," NBER Technical Working Papers 0324, National Bureau of Economic Research, Inc.
  4. Daniel Millimet & Rusty Tchernis, 2006. "On the Specification of Propensity Scores: with an Application to the WTO-Environment Debate," Caepr Working Papers 2006-013, Center for Applied Economics and Policy Research, Economics Department, Indiana University Bloomington.
  5. Guido Imbens & Jeffrey Wooldridge, 2008. "Recent developments in the econometrics of program evaluation," CeMMAP working papers CWP24/08, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
  6. Matias Busso & Patrick Kline, 2008. "Do Local Economic Development Programs Work? Evidence from the Federal Empowerment Zone Program," Cowles Foundation Discussion Papers 1639, Cowles Foundation for Research in Economics, Yale University.
  7. Michael Lechner & Blaise Melly, 2010. "Partial Idendification of Wage Effects of Training Programs," Working Papers 2010-8, Brown University, Department of Economics.
  8. Richard K. Crump & V. Joseph Hotz & Guido W. Imbens & Oscar A. Mitnik, 2006. "Moving the Goalposts: Addressing Limited Overlap in the Estimation of Average Treatment Effects by Changing the Estimand," NBER Technical Working Papers 0330, National Bureau of Economic Research, Inc.

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