• Publications
  • Influence
A Heteroskedasticity-Consistent Covariance Matrix Estimator and a Direct Test for Heteroskedasticity
  • H. White
  • Mathematics, Economics
  • 1 May 1980
This paper presents a parameter covariance matrix estimator which is consistent even when the disturbances of a linear regression model are heteroskedastic. This estimator does not depend on a formal
Maximum Likelihood Estimation of Misspecified Models
This paper examines the consequences and detection of model misspecification when using maximum likelihood techniques for estimation and inference. The quasi-maximum likelihood estimator (QMLE)
Asymptotic theory for econometricians
The Linear Model and Instrumental Variables Estimators. Consistency. Laws of Large Numbers. Asymptotic Normality. Central Limit Theory. Estimating Asymptotic Covariance Matrices. Functional Central
Tests of Conditional Predictive Ability
This work proposes an alternative framework for out-of-sample comparison of predictive ability which delivers more practically relevant conclusions and is based on inference about conditional expectations of forecasts and forecast errors rather than the unconditional expectations that are the focus of the existing literature.
A Reality Check for Data Snooping
  • H. White
  • Computer Science
  • 1 September 2000
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.
Estimation, inference, and specification analysis
The underlying motivation for maximum-likelihood estimation is explored, the interpretation of the MLE for misspecified probability models is treated, and the conditions under which parameters of interest can be consistently estimated despite misspecification are given.
Data‐Snooping, Technical Trading Rule Performance, and the Bootstrap
In this paper we utilize Whites Reality Check bootstrap methodology (White (1997)) to evaluate simple technical trading rules while quantifying the data-snooping bias and fully adjusting for its