GMM Efficiency and IPW Estimation for Nonsmooth Functions

Otavio Bartalotti

No 1301, Working Papers from Tulane University, Department of Economics

Abstract: In a GMM setting this paper analyzes the problem in which we have two sets of moment conditions, where two sets of parameters enter into one set of moment conditions, while only one set of parameters enters into the other, extending Prokhorov and Schmidt's (2009) redundancy results to nonsmooth objective functions, and obtains relatively efficient estimates of interesting parameters in the presence of nuisance parameters. One-step GMM estimation for both set of parameters is asymptotically more efficient than two-step procedures. These results are applied to Wooldridge's (2007) inverse probability weighted estimator (IPW), generalizing the framework to deal with missing data in this context. Two-step estimation of beta_0 is more efficient than using known probabilities of selection, but this is dominated by one-step joint estimation. Examples for missing data quantile regression and instrumental variable quantile regression are provided.

Keywords: generalized method of moments; nonsmooth objective functions; inverse probability weighting; missing data; quantile regression (search for similar items in EconPapers)
JEL-codes: C13 (search for similar items in EconPapers)
Pages: 29 pages
Date: 2013-01
New Economics Papers: this item is included in nep-ecm
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

Downloads: (external link)
http://repec.tulane.edu/RePEc/pdf/tul1301.pdf First Version, January 2013 (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:tul:wpaper:1301

Access Statistics for this paper

More papers in Working Papers from Tulane University, Department of Economics Contact information at EDIRC.
Bibliographic data for series maintained by Kerui Geng ().