Estimating covariance matrices using estimating functions in nonparametric and semiparametric regression

We use ideas from estimating function theory to derive new, simply computed consistent covariance matrix estimates in nonparametric regression and in a class of semiparametric problems. Unlike other estimates in the literature, ours do not require auxiliary or additional nonparametric regressions.

MoreLess

Year of publication:	1997
Authors:	Carroll, Raymond J. ; Iturria, Stephen J. ; Gutierrez, Roberto G.
Publisher:	Berlin : Humboldt University of Berlin, Interdisciplinary Research Project 373: Quantification and Simulation of Economic Processes
Subject:	Nonparametric regression \| Estimating Equations \| Kernel regression \| Plug-in Semiparametrics \| Smoothing

freely available

Full text |

More access options

Check Google Scholar

In German libraries (KVK)

I need help

More details

Series:	SFB 373 Discussion Paper ; 1997,14
Type of publication:	Book / Working Paper
Type of publication (narrower categories):	Working Paper
Language:	English
Other identifiers:	72832475X [GVK] hdl:10419/66254 [Handle] RePEc:zbw:sfb373:199714 [RePEc]
Source:	EconStor

Persistent link: https://www.econbiz.de/10010310772