• Publications
  • Influence
Asymptotic theory for econometricians
  • H. White
  • Economics, Mathematics
  • 1 December 1985
The Linear Model and Instrumental Variables Estimators. Consistency. Laws of Large Numbers. Asymptotic Normality. Central Limit Theory. Estimating Asymptotic Covariance Matrices. Functional CentralExpand
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Tests of Conditional Predictive Ability
We argue that the current framework for predictive ability testing (e.g., West, 1996) is not necessarily useful for real-time forecast selection, i.e., for assessing which of two competing forecasting methods will perform better in the future. Expand
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A Reality Check for Data Snooping
Data snooping occurs when a given set of data is used more than once for purposes of inference or model selection. When such data reuse occurs, there is always the possibility that any satisfactoryExpand
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Estimation, inference, and specification analysis
  • H. White
  • Computer Science, Mathematics
  • 1 September 1996
This book examines the consequences of misspecifications ranging from the fundamental to the nonexistent for the interpretation of likelihood-based methods of statistical estimation and interference, and the conditions under which parameters of interest can be consistently estimated despite misspecification. Expand
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Can Mutual Fund 'Stars' Really Pick Stocks? New Evidence from a Bootstrap Analysis
We apply an innovative bootstrap statistical technique to examine the performance of the U.S. equity mutual fund industry during the 1962 to 1994 period. Using this new method, we bootstrap theExpand
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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 itsExpand
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A Unified Theory of Estimation and Inference for Nonlinear Dynamic Models
2. The data generation process and optimization estimators 3. Consistency of optimization estimators 4. More on near epoch dependence 5. Asymptotic mormality 6. Estimating asymptotic cavarianceExpand
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Universal approximation of an unknown mapping and its derivatives using multilayer feedforward networks
A shoulder strap retainer having a base to be positioned on the exterior shoulder portion of a garment with securing means attached to the undersurface of the base. Expand
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Testing for neglected nonlinearity in time series models: A comparison of neural network methods and alternative tests
A new neural network test for neglected nonlinearity is based on the approximating ability of neural network modeling techniques recently developed by cognitive scientists. Expand
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Connectionist nonparametric regression: Multilayer feedforward networks can learn arbitrary mappings
  • H. White
  • Mathematics, Computer Science
  • Neural Networks
  • 1 October 1990
We show that sufficiently complex multilayer feedforward networks are capable of representing arbitrarily accurate approximations to arbitrary mappings by proving the consistency of a class of connectionist nonparametric regression estimators for arbitrary (square integrable) regression functions. Expand
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