NATIONAL BUREAU OF ECONOMIC RESEARCH
NATIONAL BUREAU OF ECONOMIC RESEARCH
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Whitney Newey

Department of Economics, E52-424
MIT
50 Memorial Drive
Cambridge, MA 02142
Tel: 617/253-6420
Fax: 617/253-1330

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NBER Program Affiliations: LS
NBER Affiliation: Research Associate

NBER Working Papers and Publications

December 2017The Bunching Estimator Cannot Identify the Taxable Income Elasticity
with Sören Blomquist: w24136
Bunching estimators were developed and extended by Saez (2010) and Chetty et. al. (2011). Using this method one can get an estimate of the taxable income elasticity from the bunching pattern around a kink point. The bunching estimator has become popular, with a large number of papers applying the method. In this paper, we show that the bunching estimators cannot identify the taxable income elasticity when the functional form of the distribution of preference heterogeneity is unknown. We find that an observed distribution of taxable income around a kink point or over the whole budget set can be consistent with any positive taxable income elasticity if the distribution of heterogeneity is unrestricted. If one is willing to assume restrictions on the heterogeneity density some information ab...
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...

Published: Victor Chernozhukov & Denis Chetverikov & Mert Demirer & Esther Duflo & Christian Hansen & Whitney Newey & James Robins, 2018. "Double/debiased machine learning for treatment and structural parameters," The Econometrics Journal, vol 21(1), pages C1-C68. citation courtesy of

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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