H
Halbert White
Researcher at University of California, San Diego
Publications - 264
Citations - 83407
Halbert White is an academic researcher from University of California, San Diego. The author has contributed to research in topics: Estimator & Artificial neural network. The author has an hindex of 78, co-authored 264 publications receiving 76774 citations. Previous affiliations of Halbert White include University of California, Berkeley & University of California.
Papers
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Journal ArticleDOI
A Heteroskedasticity-Consistent Covariance Matrix Estimator and a Direct Test for Heteroskedasticity
TL;DR: In this article, a parameter covariance matrix estimator which is consistent even when the disturbances of a linear regression model are heteroskedastic is presented, which does not depend on a formal model of the structure of the heteroSkewedness.
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Multilayer feedforward networks are universal approximators
TL;DR: It is rigorously established that standard multilayer feedforward networks with as few as one hidden layer using arbitrary squashing functions are capable of approximating any Borel measurable function from one finite dimensional space to another to any desired degree of accuracy, provided sufficiently many hidden units are available.
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Maximum likelihood estimation of misspecified models
TL;DR: In this article, the consequences and detection of model misspecification when using maximum likelihood techniques for estimation and inference are examined, and the properties of the quasi-maximum likelihood estimator and the information matrix are exploited to yield several useful tests.
Book
Asymptotic theory for econometricians
TL;DR: The Linear Model and Instrumental Variables Estimators as mentioned in this paper have been used to estimate Asymptotic Covariance Matrices, and Central Limit Theory has been applied to this problem.
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A Reality Check for Data Snooping
TL;DR: The purpose here is to provide a straightforward procedure for testing the null hypothesis that the best model encountered in a specification search has no predictive superiority over a given benchmark model.