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Mean-square-error Calculations for Average Treatment Effects

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Author Info
Guido W. Imbens
Whitney Newey
Geert Ridder

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Abstract

This paper develops a new efficient estimator for the average treatment effect, if selection for treatment is on observables. The new estimator is linear in the first-stage nonparametric estimator. This simplifies the derivation of the means squared error (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_05.34_%5BImbens,Newey,Ridder%5D.pdf
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File Function: Second version, 2005
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Publisher Info
Paper provided by Institute of Economic Policy Research (IEPR) in its series IEPR Working Papers with number 05.34.

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Length: 49 pages
Date of creation: Sep 2005
Date of revision:
Handle: RePEc:scp:wpaper:05-34

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

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Find related papers by JEL classification:
C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: General - - - Semiparametric and Nonparametric Methods
C20 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - General

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References listed on IDEAS
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
  1. Andrews, Donald W. K., 1991. "Asymptotic optimality of generalized CL, cross-validation, and generalized cross-validation in regression with heteroskedastic errors," Journal of Econometrics, Elsevier, vol. 47(2-3), pages 359-377, February. [Downloadable!] (restricted)
  2. James Heckman & Hidehiko Ichimura & Jeffrey Smith & Petra Todd, 1998. "Characterizing Selection Bias Using Experimental Data," Econometrica, Econometric Society, vol. 66(5), pages 1017-1098, September.
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  3. Newey, Whitney K, 1994. "The Asymptotic Variance of Semiparametric Estimators," Econometrica, Econometric Society, vol. 62(6), pages 1349-82, November. [Downloadable!] (restricted)
  4. Guido Imbens, 2000. "Efficient Estimation of Average Treatment Effects Using the Estimated Propensity Score," Econometric Society World Congress 2000 Contributed Papers 1166, Econometric Society. [Downloadable!]
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  5. Hidehiko Ichimura & Oliver Linton, 2001. "Asymptotic expansions for some semiparametric program evaluation estimators," CeMMAP working papers CWP04/01, Centre for Microdata Methods and Practice, Institute for Fiscal Studies. [Downloadable!]
    Other versions:
  6. Heckman, James J & Ichimura, Hidehiko & Todd, Petra, 1998. "Matching as an Econometric Evaluation Estimator," Review of Economic Studies, Blackwell Publishing, vol. 65(2), pages 261-94, April. [Downloadable!] (restricted)
  7. Xiaohong Chen & Han Hong & Alessandro Tarozzi, 2008. "Semiparametric Efficiency in GMM Models of Nonclassical Measurement Errors, Missing Data and Treatment Effects," Cowles Foundation Discussion Papers 1644, Cowles Foundation, Yale University. [Downloadable!]
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  8. Jinyong Hahn, 1998. "On the Role of the Propensity Score in Efficient Semiparametric Estimation of Average Treatment Effects," Econometrica, Econometric Society, vol. 66(2), pages 315-332, March.
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Cited by:
(explanations, Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.)

  1. 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. [Downloadable!] (restricted)
    Other versions:
  2. Michael Lechner & Blaise Melly, 2007. "Earnings Effects of Training Programs," University of St. Gallen Department of Economics working paper series 2007 2007-28, Department of Economics, University of St. Gallen. [Downloadable!]
    Other versions:
  3. Daniel Egel & Bryan S. Graham & Cristine Campos de Xavier Pinto, 2008. "Inverse Probability Tilting and Missing Data Problems," NBER Working Papers 13981, National Bureau of Economic Research, Inc. [Downloadable!] (restricted)
  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. [Downloadable!]
  5. 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, Yale University. [Downloadable!]
    Other versions:
  6. Richard K. Crump & V. Joseph Hotz & Guido W. Imbens, 2006. "Moving the Goalposts: Addressing Limited Overlap in Estimation of Average Treatment Effects by Changing the Estimand," IZA Discussion Papers 2347, Institute for the Study of Labor (IZA). [Downloadable!]
    Other versions:
  7. 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. [Downloadable!] (restricted)
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