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Mean-squared-error Calculations for Average Treatment Effects

Guido Imbens (), Whitney Newey and Geert Ridder ()
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Geert Ridder: Department of Economics, University of Southern California

No 06.57, IEPR Working Papers from Institute of Economic Policy Research (IEPR)

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.

Keywords: Nonparametric Estimation; Imputation; Mean Squared Error; Order Selection (search for similar items in EconPapers)
JEL-codes: C14 C20 (search for similar items in EconPapers)
Pages: 48 pages
Date: 2006-11
New Economics Papers: this item is included in nep-ecm
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Working Paper: Mean-square-error Calculations for Average Treatment Effects (2005)
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