NATIONAL BUREAU OF ECONOMIC RESEARCH
NATIONAL BUREAU OF ECONOMIC RESEARCH

Whitney Newey

Department of Economics, E52-424
MIT
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Cambridge, MA 02139
Tel: 617/253-6420
Fax: 617/253-1330

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NBER Program Affiliations: LS
NBER Affiliation: Faculty Research Fellow

NBER Working Papers and Publications

June 2017Double/Debiased Machine Learning for Treatment and Structural Parameters
with Victor Chernozhukov, Denis Chetverikov, Mert Demirer, Esther Duflo, Christian Hansen, James Robins: w23564
We revisit the classic semiparametric problem of inference on a low dimensional parameter θ_0 in the presence of high-dimensional nuisance parameters η_0. We depart from the classical setting by allowing for η_0 to be so high-dimensional that the traditional assumptions, such as Donsker properties, that limit complexity of the parameter space for this object break down. To estimate η_0, we consider the use of statistical or machine learning (ML) methods which are particularly well-suited to estimation in modern, very high-dimensional cases. ML methods perform well by employing regularization to reduce variance and trading off regularization bias with overfitting in practice. However, both regularization bias and overfitting in estimating η_0 cause a heavy bias in estimators of θ_0 that are...
February 1995Automatic Lag Selection in Covariance Matrix Estimation
with Kenneth D. West: t0144
We propose a nonparametric method for automatically selecting the number of autocovariances to use in computing a heteroskedasticity and autocorrelation consistent covariance matrix. For a given kernel for weighting the autocovariances, we prove that our procedure is asymptotically equivalent to one that is optimal under a mean squared error loss function. Monte Carlo simulations suggest that our procedure performs tolerably well, although it does result in size distortions.

Published:

  • Review of Economic Studies, 1994, 61, pp 631-653
  • "A Comparison of Alternative Instrumental Variables Estimators of a Dynamic Linear Model," (with David Wilcox) Journal of Business and Economic Statistics 14 (1996), pp. 281-293.

 
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