Nonparametric Matching and Efficient Estimators of Homothetically Separable Functions

Arthur Lewbel () and Oliver Bruce Linton ()

Econometrica, 2007, vol. 75, issue 4, pages 1209-1227

Abstract: For vectors z and w and scalar v, let r(v, z, w) be a function that can be nonparametrically estimated consistently and asymptotically normally, such as a distribution, density, or conditional mean regression function. We provide consistent, asymptotically normal nonparametric estimators for the functions G and H, where r(v, z, w) = H[vG(z), w], and some related models. This framework encompasses homothetic and homothetically separable functions, and transformed partly additive models r(v, z, w) = h[v + g(z), w] for unknown functions gand h Such models reduce the curse of dimensionality, provide a natural generalization of linear index models, and are widely used in utility, production, and cost function applications. We also provide an estimator of Gthat is oracle efficient, achieving the same performance as an estimator based on local least squares when H is known. Copyright The Econometric Society 2007.

Date: 2007

Downloads: (external link)
http://hdl.handle.net/10.1111/j.1468-0262.2007.00787.x link to full text (text/html)
Access to full text is restricted to subscribers.

Related works:
Working Paper: Nonparametric Matching and Efficient Estimators of Homothetically Separable Functions (2006)
This item may be available elsewhere in EconPapers: Search for items with the same title.

Ordering information: This journal article can be ordered from
http://www.blackwell ... mb.asp?ref=0012-9682

Access Statistics for this article

Econometrica is edited by Stephen Morris

More articles in Econometrica from Econometric Society
Contact information at EDIRC.
Series data maintained by Christopher F. Baum ().