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Cited by 294

The DOI matches its Crossref record; 19 of the 19 references it cites were found in a registry or an index.

Asymptotically Efficient Estimation in the Presence of Heteroskedasticity of Unknown Form

Robinson, P. M.·1987·Econometrica
DOI 10.2307/1911033
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Abstract

In a multiple-regression model the residual variance is an unknown function of the explanatory variables, and estimated by nearest-neighbor nonparametric regression. The resulting weighted least-squares estimator of the regression coefficients is shown to be adaptive, in the sense of having the same asymptotic distribution, to first order, as estimators based on knowledge of the actual variance function or a finite parameterization of it. A similar result was established by R. J. Carrol l (1982) using kernel estimation and under substantially more restrictive conditions on the data generating process than ours. Extensions to various other models seem to be possible. Copyright 1987 by The Econometric Society.

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Metadata

Authors
Robinson, P. M.
Venue
Econometrica
Publisher
JSTOR
Published
1987-07
Volume
55
Issue
4
Pages
875
ISSN
0012-9682 (ISSN-L)
DOI
10.2307/1911033
OpenAlex
W2072838328
Cited by
294
References
19
FWCI
6.15
Type
journal article
Language
en
Open access
closed

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CoChat. (2026). Citation check: Asymptotically Efficient Estimation in the Presence of Heteroskedasticity of Unknown Form. https://app.cochat.ai/papers/10.2307/1911033
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Robinson, P. M. (1987). Asymptotically Efficient Estimation in the Presence of Heteroskedasticity of Unknown Form. Econometrica. https://doi.org/10.2307/1911033

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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.