IDEAS home Printed from
MyIDEAS: Log in (now much improved!) to save this paper

A matching estimator based on a bi-level optimization problem

Listed author(s):
  • Juan Díaz
  • Tomás Rau
  • Jorge Rivera

This paper proposes a matching estimator where the size of the weights and the number of neighbors are endogenously determined from the solution of a bi-level optimization problem. The first level problem minimizes the distance between the characteristics of an individual and a convex combination of characteristics of individuals belonging to the corresponding counterfactual set, and with the second level we choose a solution point of the first level that minimizes the sum of the distances between the characteristics of the individual under analysis and those from the counterfactuals employed in the optimal convex combination. We show that this estimator is consistent and asymptotically normal. Finally we study its behavior in finite samples by performing Monte Carlo experiments with designs based on the related literature. In terms of bias, standard deviation and mean square error, we find significant improvements using our estimator in comparison to the simple matching estimator, widely employed in the literature.

If you experience problems downloading a file, check if you have the proper application to view it first. In case of further problems read the IDEAS help page. Note that these files are not on the IDEAS site. Please be patient as the files may be large.

File URL:
Download Restriction: no

Paper provided by University of Chile, Department of Economics in its series Working Papers with number wp351.

in new window

Length: 18 pages
Date of creation: Mar 2012
Handle: RePEc:udc:wpaper:wp351
Contact details of provider: Web page:

More information through EDIRC

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.:

in new window

  1. Guido W. Imbens & Jeffrey M. Wooldridge, 2009. "Recent Developments in the Econometrics of Program Evaluation," Journal of Economic Literature, American Economic Association, vol. 47(1), pages 5-86, March.
  2. de Luna, Xavier & Johansson, Per & Sjöstedt-de Luna, Sara, 2010. "Bootstrap inference for K-nearest neighbour matching estimators," Working Paper Series 2010:13, IFAU - Institute for Evaluation of Labour Market and Education Policy.
  3. Alberto Abadie & Guido W. Imbens, 2008. "On the Failure of the Bootstrap for Matching Estimators," Econometrica, Econometric Society, vol. 76(6), pages 1537-1557, November.
Full references (including those not matched with items on IDEAS)

This item is not listed on Wikipedia, on a reading list or among the top items on IDEAS.

When requesting a correction, please mention this item's handle: RePEc:udc:wpaper:wp351. See general 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: (Mohit Karnani)

If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

If references are entirely missing, you can add them using this form.

If the full references list an item that is present in RePEc, but the system did not link to it, you can help with this form.

If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your profile, as there may be some citations waiting for confirmation.

Please note that corrections may take a couple of weeks to filter through the various RePEc services.

This information is provided to you by IDEAS at the Research Division of the Federal Reserve Bank of St. Louis using RePEc data.