Whitney K Newey
Biographic Data
| ID | 5867671 |
|---|---|
| NAME | Whitney K Newey |
| GIVEN NAMES | Whitney K |
| FAMILY NAME | Newey |
| SIGNATURE | NEWEY W K |
| AFFILIATIONS | Massachusetts Institute of Technology |
| VERIFIED | No |
| TOTAL WORKS | 15 |
| TOTAL CITATIONS | 0 |
| AUTHOR COUNT | 15 |
| EDITOR COUNT | 0 |
| FIRST PUBLICATION YEAR | 1987 |
| LATEST PUBLICATION YEAR | 2026 |
| H-INDEX | 0 |
Long Story Short: Omitted Variable Bias in Causal Machine Learning
We develop a general theory of omitted variable bias for a wide range of common causal parameters, including average treatment effects, average causal derivatives, and policy effects from covariate shifts. We show how plausibility judgments on the maximum explanatory power of omitted variables are sufficient to bound the bias, facilitating sensitivity analysis in otherwise complex models. Finally, we provide statistical inference methods that can…
On Bunching and Identification of the Taxable Income Elasticity
The elasticity of taxable income is vital when predicting the effect of taxes. Bunching at kinks/notches has been used to estimate this elasticity. We show that when the preference distribution is unrestricted, bunching at a kink or a notch is not informative about the size of the elasticity, and neither is the entire distribution of taxable income. Bunching identifies the taxable income elasticity when the preference distribution is correctly sp…
Double/debiased machine learning for treatment and structural parameters
We revisit the classic semi‐parametric problem of inference on a low‐dimensional parameter θ0 in the presence of high‐dimensional nuisance parameters η0. We depart from the classical setting by allowing for η0 to be so high‐dimensional that the traditional assumptions (e.g. Donsker properties) that limit complexity of the parameter space for this object break down. To estimate η0, we consider the use of statistical or machine learning (ML) method…
Econometrics and Economic Theory in the 20th Century: The Ragnar Frisch Centennial Symposium
Instrumental Variables Estimation With Flexible Distributions
Instrumental variables are often associated with low estimator precision. This article explores efficiency gains that might be achievable using moment conditions that are nonlinear in the disturbances and are based on flexible parametric families for error distributions. We show that these estimators can achieve the semiparametric efficiency bound when the true error distribution is a member of the parametric family. Monte Carlo simulations demon…
Estimation With Many Instrumental Variables
Using many valid instrumental variables has the potential to improve efficiency but makes the usual inference procedures inaccurate. We give corrected standard errors, an extension of Bekker to nonnormal disturbances, that adjust for many instruments. We find that this adjustment is useful in empirical work, simulations, and in the asymptotic theory. Use of the corrected standard errors in t-ratios leads to an asymptotic approximation order that …
Generalized Method of Moments, Efficient Bootstrapping, and Improved Inference
Generalized method of moments (GMM) has been an important innovation in econometrics. Its usefulness has motivated a search for good inference procedures based on GMM. This article presents a novel method of bootstrapping for GMM based on resampling from the empirical likelihood distribution that imposes the moment restrictions. We show that this approach yields a large-sample improvement and is efficient, and give examples. We also discuss the d…
Flexible Simulated Moment Estimation of Nonlinear Errors-in-Variables Models
Nonlinear regression with measurement error is important for estimation from microeconomic data. One approach to identification and estimation is a causal model, in which the unobserved true variable is predicted by observable variables. This paper details the estimation of such a model using simulated moments and a flexible disturbance distribution. An estimator of the asymptotic variance is given for parametric models. Also, a semiparametric co…
Automatic Lag Selection in Covariance Matrix Estimation
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 doe…
Chapter 36 Large sample estimation and hypothesis testing
Over-Identification Tests in Earnings Functions With Fixed Effects
The fixed-effects model for panel data imposes restrictions on coefficients from regressions of all leads and lags of the dependent variable on all leads and lags of right-side variables. In the standard fixed-effects model, the omnibus goodness-of-fit statistic is shown to simplify to the degrees of freedom times the Rz from a regression of analysis of covariance residuals on all leads and lags of right-side variables. This result is applied to …
Estimating Vector Autoregressions with Panel Data
This paper considers estimation and testing of vector autoregressio n coefficients in panel data, and applies the techniques to analyze the dynamic relationships between wages an d hours worked in two samples of American males. The model allows for nonstationary individual effects and is estimated by applying instrumental variables to the quasi-differenced autoregressive equations. The empirical results suggest the absence of lagged hours in the …
Efficient estimation of limited dependent variable models with endogenous explanatory variables
Hypothesis Testing with Efficient Method of Moments Estimation
Efficient method of moments estimation techniques include many commonly used techniques, including ordinary least squares, two- and three-stage least squares, quasi maximum likelihood, and versions of these for nonlinear environments. For models estimated by any efficient method of moments technique, the authors define analogues to the maximum likeliho od based Wald, likelihood ratio, Lagrange multiplier, and minimum chi-squared statistics. They …
A Simple, Positive Semi-Definite, Heteroskedasticity and Autocorrelation Consistent Covariance Matrix
This paper describes a simple method of calculating a heteroskedasticity and autocorrelation consistent covariance matrix that is positive semi-definite by construction. It also establishes consistency of the estimated covariance matrix under fairly general conditions.
No prominent works on this page.
Efficient estimation of limited dependent variable models with endogenous explanatory variables
Hypothesis Testing with Efficient Method of Moments Estimation
Efficient method of moments estimation techniques include many commonly used techniques, including ordinary least squares, two- and three-stage least squares, quasi maximum likelihood, and versions of these for nonlinear environments. For models estimated by any efficient method of moments technique, the authors define analogues to the maximum likeliho od based Wald, likelihood ratio, Lagrange multiplier, and minimum chi-squared statistics. They …
A Simple, Positive Semi-Definite, Heteroskedasticity and Autocorrelation Consistent Covariance Matrix
This paper describes a simple method of calculating a heteroskedasticity and autocorrelation consistent covariance matrix that is positive semi-definite by construction. It also establishes consistency of the estimated covariance matrix under fairly general conditions.
Estimating Vector Autoregressions with Panel Data
This paper considers estimation and testing of vector autoregressio n coefficients in panel data, and applies the techniques to analyze the dynamic relationships between wages an d hours worked in two samples of American males. The model allows for nonstationary individual effects and is estimated by applying instrumental variables to the quasi-differenced autoregressive equations. The empirical results suggest the absence of lagged hours in the …
Over-Identification Tests in Earnings Functions With Fixed Effects
The fixed-effects model for panel data imposes restrictions on coefficients from regressions of all leads and lags of the dependent variable on all leads and lags of right-side variables. In the standard fixed-effects model, the omnibus goodness-of-fit statistic is shown to simplify to the degrees of freedom times the Rz from a regression of analysis of covariance residuals on all leads and lags of right-side variables. This result is applied to …
Automatic Lag Selection in Covariance Matrix Estimation
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 doe…
Chapter 36 Large sample estimation and hypothesis testing
Flexible Simulated Moment Estimation of Nonlinear Errors-in-Variables Models
Nonlinear regression with measurement error is important for estimation from microeconomic data. One approach to identification and estimation is a causal model, in which the unobserved true variable is predicted by observable variables. This paper details the estimation of such a model using simulated moments and a flexible disturbance distribution. An estimator of the asymptotic variance is given for parametric models. Also, a semiparametric co…
Generalized Method of Moments, Efficient Bootstrapping, and Improved Inference
Generalized method of moments (GMM) has been an important innovation in econometrics. Its usefulness has motivated a search for good inference procedures based on GMM. This article presents a novel method of bootstrapping for GMM based on resampling from the empirical likelihood distribution that imposes the moment restrictions. We show that this approach yields a large-sample improvement and is efficient, and give examples. We also discuss the d…
Estimation With Many Instrumental Variables
Using many valid instrumental variables has the potential to improve efficiency but makes the usual inference procedures inaccurate. We give corrected standard errors, an extension of Bekker to nonnormal disturbances, that adjust for many instruments. We find that this adjustment is useful in empirical work, simulations, and in the asymptotic theory. Use of the corrected standard errors in t-ratios leads to an asymptotic approximation order that …
Instrumental Variables Estimation With Flexible Distributions
Instrumental variables are often associated with low estimator precision. This article explores efficiency gains that might be achievable using moment conditions that are nonlinear in the disturbances and are based on flexible parametric families for error distributions. We show that these estimators can achieve the semiparametric efficiency bound when the true error distribution is a member of the parametric family. Monte Carlo simulations demon…
Econometrics and Economic Theory in the 20th Century: The Ragnar Frisch Centennial Symposium
Double/debiased machine learning for treatment and structural parameters
We revisit the classic semi‐parametric problem of inference on a low‐dimensional parameter θ0 in the presence of high‐dimensional nuisance parameters η0. We depart from the classical setting by allowing for η0 to be so high‐dimensional that the traditional assumptions (e.g. Donsker properties) that limit complexity of the parameter space for this object break down. To estimate η0, we consider the use of statistical or machine learning (ML) method…
On Bunching and Identification of the Taxable Income Elasticity
The elasticity of taxable income is vital when predicting the effect of taxes. Bunching at kinks/notches has been used to estimate this elasticity. We show that when the preference distribution is unrestricted, bunching at a kink or a notch is not informative about the size of the elasticity, and neither is the entire distribution of taxable income. Bunching identifies the taxable income elasticity when the preference distribution is correctly sp…
Long Story Short: Omitted Variable Bias in Causal Machine Learning
We develop a general theory of omitted variable bias for a wide range of common causal parameters, including average treatment effects, average causal derivatives, and policy effects from covariate shifts. We show how plausibility judgments on the maximum explanatory power of omitted variables are sufficient to bound the bias, facilitating sensitivity analysis in otherwise complex models. Finally, we provide statistical inference methods that can…
Econometrics (12 works) · Mathematics (12 works) · Statistics (11 works) · Statistical Methods and Inference (8 works) · Computer Science (7 works) · Economics (7 works) · Applied Mathematics (6 works) · Monetary Policy and Economic Impact (6 works) · Estimator (5 works) · Financial Risk and Volatility Modeling (5 works)