Article
GMM Efficiency and IPW Estimation for Nonsmooth Functions
Tulane University Economics Working Papers
(2013)
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 ecient 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 o is more ecient 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,
- quantil regression
Disciplines
Publication Date
2013
Publisher Statement
Copyright 2013 The Authors
Citation Information
Otávio Bartalotti. "GMM Efficiency and IPW Estimation for Nonsmooth Functions" Tulane University Economics Working Papers (2013) p. 1302 Available at: http://works.bepress.com/otavio-bartalotti/7/