Bandwidth Selection and the Estimation of Treatment Effects with Unbalanced Data

Jose Galdo, Jeffrey Smith and Dan Black

No 3095, IZA Discussion Papers from Institute of Labor Economics (IZA)

Abstract: This paper addresses the selection of smoothing parameters for estimating the average treatment effect on the treated using matching methods. Because precise estimation of the expected counterfactual is particularly important in regions containing the mass of the treated units, we define and implement weighted cross-validation approaches that improve over conventional methods by considering the location of the treated units in the selection of the smoothing parameters. We also implement a locally varying bandwidth method that uses larger bandwidths in areas where the mass of the treated units is located. A Monte Carlo study compares our proposed methods to the conventional unweighted method and to a related method inspired by Bergemann et al. (2005). The Monte Carlo analysis indicates efficiency gains from all methods that take account of the location of the treated units. We also apply all five methods to bandwidth selection in the context of the data from LaLonde’s (1986) study of the performance of non-experimental estimators using the experimental data from the National Supported Work (NSW) Demonstration program as a benchmark. Overall, both the Monte Carlo analysis and the empirical application show feasible precision gains for the weighted cross-validation and the locally varying bandwidth approaches.

Keywords: matching; Monte Carlo simulation; cross-validation; kernel regression (search for similar items in EconPapers)
JEL-codes: C13 C14 (search for similar items in EconPapers)
Pages: 46 pages
Date: 2007-10
New Economics Papers: this item is included in nep-ecm
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Citations: View citations in EconPapers (30)

Published - published in: Annales d'Economie et Statistique, 2008, 91-92, 189-216

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Journal Article: Bandwidth Selection and the Estimation of Treatment Effects with Unbalanced Data (2008)
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