Evaluating The Performance Of Non-Experimental Estimators: Evidence From A Randomized Ui Program
AbstractOne of the lessons of the treatment effects literature is the lack of consensus about the ability of statistical and econometric methods to replicate experimental estimates. In this paper, we provide new evidence using an unusual unemployment insurance experiment that allows the identification of discontinuities in the assignment mechanism. In particular, we use a set of regression functions and matching estimators based on kernel methods with mixed categorical and continuous data. A crucial issue with the kernel approach is the choice of the smoothing parameters. We develop a leave-one-out cross-validation algorithm that minimizes the mean square error of the average treatment effect on the treated weighting each comparison unit according to their distribution of covariates in the support region. Two main findings emerge. First, local constant and nearest-neighbor matching on kernel-based propensity score with mixed categorical and continuous data produces a closer approximation to the experimental estimates than traditional parametric propensity score models do. Second, the regression-discontinuity design emerges as a promising method for solving the evaluation problem. When restricted to sample observations in the neighborhood of the discontinuity points, the estimates are close approximation to the experimental estimates and are robust across different subsamples and estimators.
Download InfoTo our knowledge, this item is not available for download. To find whether it is available, there are three options:
1. Check below under "Related research" whether another version of this item is available online.
2. Check on the provider's web page whether it is in fact available.
3. Perform a search for a similarly titled item that would be available.
Bibliographic InfoPaper provided by Econometric Society in its series Econometric Society 2004 Latin American Meetings with number 92.
Date of creation: 11 Aug 2004
Date of revision:
Contact details of provider:
Phone: 1 212 998 3820
Fax: 1 212 995 4487
Web page: http://www.econometricsociety.org/pastmeetings.asp
More information through EDIRC
Treatment Effects; Kernels with Mixed Data; Cross-Validation; Matching; Regression-Discontinuity Design;
Find related papers by JEL classification:
- C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
- C21 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models
- C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
You can help add them by filling out this form.
reading list or among the top items on IDEAS.Access and download statisticsgeneral information about how to correct material in RePEc.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Christopher F. Baum).
If references are entirely missing, you can add them using this form.