Jeffrey M Wooldridge
Biographic Data
| ID | 1456033 |
|---|---|
| NAME | Jeffrey M Wooldridge |
| GIVEN NAMES | Jeffrey M |
| FAMILY NAME | Wooldridge |
| SIGNATURE | WOOLDRIDGE J M |
| AFFILIATIONS | Michigan State University |
| ORCID | 0000-0002-1579-6406 |
| VERIFIED | Yes |
| TOTAL WORKS | 30 |
| TOTAL CITATIONS | 48 |
| AUTHOR COUNT | 30 |
| EDITOR COUNT | 0 |
| FIRST PUBLICATION YEAR | 1988 |
| LATEST PUBLICATION YEAR | 2026 |
| H-INDEX | 3 |
Simple Transformation Approach to Difference-in-Differences Estimation for Panel Data
Heterogeneity and Heteroskedasticity in Endogenous Switching Models: Estimating the Effects of Physician Advice on Calorie Consumption
We describe two‐step control function estimation procedures for estimating average treatment effects in constant coefficient endogenous switching models with a binary treatment. We allow for additional heterogeneity by allowing for heteroskedasticity in the latent error in the treatment assignment equation. We apply our estimation procedures to evaluate the causal effect of physician advice on calorie consumption using National Health and Nutriti…
Two-way fixed effects, the two-way mundlak regression, and difference-in-differences estimators
I derive a result on the equivalence between the two-way fixed effects (TWFE) estimator and an estimator obtained from a pooled ordinary least squares regression that includes unit-specific time averages and time-period-specific cross-sectional averages—the two-way Mundlak (TWM) regression. The equivalence between TWFE and TWM implies that various estimators used for intervention analysis can be computed using pooled OLS that controls for time-co…
Abadie’s Kappa and Weighting Estimators of the Local Average Treatment Effect
Recent research has demonstrated the importance of flexibly controlling for covariates in instrumental variables estimation. In this paper we study the finite sample and asymptotic properties of various weighting estimators of the local average treatment effect (LATE), motivated by Abadie’s (2003) kappa theorem and offering the requisite flexibility relative to standard practice. We argue that two of the estimators under consideration, which are …
Robust and Efficient Estimation of Potential Outcome Means Under Random Assignment
We study efficiency improvements in randomized experiments for estimating a vector of potential outcome means using regression adjustment (RA) when there are more than two treatment levels. We show that linear RA which estimates separate slopes for each assignment level is never worse, asymptotically, than using the subsample averages. We also show that separate RA improves over pooled RA except in the obvious case where slope parameters in the l…
Difference-in-Differences Estimator of Quantile Treatment Effect on the Treated
We propose a new difference-in-differences (DID) estimator of the quantile treatment effect on the treated (QTT). The model assumes a common time effect on the cumulative distribution functions of untreated potential outcomes, allowing for covariates. This condition holds if and only if the net change in the untreated outcome densities is common across treated and control groups. Unlike the Changes-in-Changes model proposed by Athey and Imbens (2…
A simple, robust test for choosing the level of fixed effects in linear panel data models
When Should You Adjust Standard Errors for Clustering?
Clustered standard errors, with clusters defined by factors such as geography, are widespread in empirical research in economics and many other disciplines. Formally, clustered standard errors adjust for the correlations induced by sampling the outcome variable from a data-generating process with unobserved cluster-level components. However, the standard econometric framework for clustering leaves important questions unanswered: (i) Why do we adj…
Two-Way Fixed Effects, the Two-Way Mundlak Regression, and Difference-in-Differences Estimators
Inference in Approximately Sparse Correlated Random Effects Probit Models With Panel Data
We propose a simple procedure based on an existing “debiased” l1-regularized method for inference of the average partial effects (APEs) in approximately sparse probit and fractional probit models with panel data, where the number of time periods is fixed and small relative to the number of cross-sectional observations. Our method is computationally simple and does not suffer from the incidental parameters problems that come from attempting to est…
Correlated random effects models with unbalanced panels
When Should You Adjust Standard Errors for Clustering?
In empirical work in economics it is common to report standard errors that account for clustering of units.Typically, the motivation given for the clustering adjustments is that unobserved components in outcomes for units within clusters are correlated.However, because correlation may occur across more than one dimension, this motivation makes it difficult to justify why researchers use clustering in some dimensions, such as geographic, but not o…
Control Function Methods in Applied Econometrics
This paper provides an overview of control function (CF) methods for solving the problem of endogenous explanatory variables (EEVs) in linear and nonlinear models. CF methods often can be justified in situations where “plug-in” approaches are known to produce inconsistent estimators of parameters and partial effects. Usually, CF approaches require fewer assumptions than maximum likelihood, and CF methods are computationally simpler. The recent fo…
What Are We Weighting For
When estimating population descriptive statistics, weighting is called for if needed to make the analysis sample representative of the target population. With regard to research directed instead at estimating causal effects, we discuss three distinct weighting motives: (1) to achieve precise estimates by correcting for heteroskedasticity; (2) to achieve consistent estimates by correcting for endogenous sampling; and (3) to identify average partia…
How do Principals Assign Students to Teachers? Finding Evidence in Administrative Data and the Implications for Value Added: How do Principals Assign Students to Teachers
The federal government's Race to the Top competition has promoted the adoption of test-based value-added measures (VAM) of performance as a component of teacher evaluations throughout many states, but the validity of these measures has been controversial among researchers and widely contested by teachers' unions. A key concern is the extent to which nonrandom sorting of students to teachers may bias the results and lead to a misclassification of …
Estimating panel data models in the presence of endogeneity and selection
On estimating firm-level production functions using proxy variables to control for unobservables
Efficient Estimation of Average Treatment Effects with Mixed Categorical and Continuous Data
In this article, we consider the nonparametric estimation of average treatment effects when there exist mixed categorical and continuous covariates. One distinguishing feature of the approach presented herein is the use of kernel smoothing for both the continuous and the discrete covariates. This approach, together with the cross-validation method. which we use for selecting the smoothing parameters, has the ability to automatically remove irrele…
Recent Developments in the Econometrics of Program Evaluation
Many empirical questions in economics and other social sciences depend on causal effects of programs or policies. In the last two decades, much research has been done on the econometric and statistical analysis of such causal effects. This recent theoretical literature has built on, and combined features of, earlier work in both the statistics and econometrics literatures. It has by now reached a level of maturity that makes it an important tool …
Panel data methods for fractional response variables with an application to test pass rates
Inverse probability weighted estimation for general missing data problems
Simple solutions to the initial conditions problem in dynamic, nonlinear panel data models with unobserved heterogeneity
I study a simple, widely applicable approach to handling the initial conditions problem in dynamic, nonlinear unobserved effects models. Rather than attempting to obtain the joint distribution of all outcomes of the endogenous variables, I propose finding the distribution conditional on the initial value (and the observed history of strictly exogenous explanatory variables). The approach is flexible, and results in simple estimation strategies fo…
Fixed-Effects and Related Estimators for Correlated Random-Coefficient and Treatment-Effect Panel Data Models
I derive conditions under which a class of fixed-effects estimators consistently estimates the population-averaged slope coefficients in panel data models with individual-specific slopes, where the slopes are allowed to be correlated with the covariates. In addition to including the usual fixed-effects estimator, the results apply to estimators that eliminate individual-specific trends. I apply the results, and propose alternative estimators, to …
Cluster-Sample Methods in Applied Econometrics
Inference methods that recognize the clustering of individual observations have been available for more than 25 years. Brent Moulton (1990) caught the attention of economists when he demonstrated the serious biases that can result in estimating the effects of aggregate explanatory variables on individual-specific response variables. The source of the downward bias in the usual ordinary least-squares (OLS) standard errors is the presence of an uno…
Inverse probability weighted M-estimators for sample selection, attrition, and stratification
A Capital Asset Pricing Model with Time-Varying Covariances
The capital asset pricing model provides a theoretical structure for the pricing of assets with uncertain returns. The premium to induc e risk-averse investors to bear risk is proportional to the nondivers ifiable risk, which is measured by the covariance of the asset return with the market portfolio return. In this paper, a multivariate, gen eralized-autoregressive, conditional, heteroscedastic process is esti mated for returns to bills, bonds, …
Applications of Generalized Method of Moments Estimation
I describe how the method of moments approach to estimation, including the more recent generalized method of moments (GMM) theory, can be applied to problems using cross section, time series, and panel data. Method of moments estimators can be attractive because in many circumstances they are robust to failures of auxiliary distributional assumptions that are not needed to identify key parameters. I conclude that while sophisticated GMM estimator…
How do Principals Assign Students to Teachers? Finding Evidence in Administrative Data and the Implications for Value Added: How do Principals Assign Students to Teachers
The federal government's Race to the Top competition has promoted the adoption of test-based value-added measures (VAM) of performance as a component of teacher evaluations throughout many states, but the validity of these measures has been controversial among researchers and widely contested by teachers' unions. A key concern is the extent to which nonrandom sorting of students to teachers may bias the results and lead to a misclassification of …
A Capital Asset Pricing Model with Time-Varying Covariances
The capital asset pricing model provides a theoretical structure for the pricing of assets with uncertain returns. The premium to induc e risk-averse investors to bear risk is proportional to the nondivers ifiable risk, which is measured by the covariance of the asset return with the market portfolio return. In this paper, a multivariate, gen eralized-autoregressive, conditional, heteroscedastic process is esti mated for returns to bills, bonds, …
Quasi-maximum likelihood estimation and inference in dynamic models with time-varying covariances
We study the properties of the quasi-maximum likelihood estimator (QMLE) and related test statistics in dynamic models that jointly parameterize conditional means and conditional covariances, when a normal log-likelihood os maximized but the assumption of normality is violated. Because the score of the normal log-likelihood has the martingale difference property when the forst two conditional moments are correctly specified, the QMLE is generally…
A Simple Specification Test for the Predictive Ability of Transformation Models
A specification test is proposed to test the adequacy of the implicit conditional mean specification in a transformation model. Regardless of the transformation under the null, the statistic is computable from ordinary least squares output via auxiliary ordinary least squares regressions. Further, the test presumes no particular form for the conditional variance of the transformed and untransformed regressands. A simple goodness-of-fit measure th…
Distribution-free estimation of some nonlinear panel data models
Applications of Generalized Method of Moments Estimation
I describe how the method of moments approach to estimation, including the more recent generalized method of moments (GMM) theory, can be applied to problems using cross section, time series, and panel data. Method of moments estimators can be attractive because in many circumstances they are robust to failures of auxiliary distributional assumptions that are not needed to identify key parameters. I conclude that while sophisticated GMM estimator…
Inverse probability weighted M-estimators for sample selection, attrition, and stratification
Cluster-Sample Methods in Applied Econometrics
Inference methods that recognize the clustering of individual observations have been available for more than 25 years. Brent Moulton (1990) caught the attention of economists when he demonstrated the serious biases that can result in estimating the effects of aggregate explanatory variables on individual-specific response variables. The source of the downward bias in the usual ordinary least-squares (OLS) standard errors is the presence of an uno…
Simple solutions to the initial conditions problem in dynamic, nonlinear panel data models with unobserved heterogeneity
I study a simple, widely applicable approach to handling the initial conditions problem in dynamic, nonlinear unobserved effects models. Rather than attempting to obtain the joint distribution of all outcomes of the endogenous variables, I propose finding the distribution conditional on the initial value (and the observed history of strictly exogenous explanatory variables). The approach is flexible, and results in simple estimation strategies fo…
Fixed-Effects and Related Estimators for Correlated Random-Coefficient and Treatment-Effect Panel Data Models
I derive conditions under which a class of fixed-effects estimators consistently estimates the population-averaged slope coefficients in panel data models with individual-specific slopes, where the slopes are allowed to be correlated with the covariates. In addition to including the usual fixed-effects estimator, the results apply to estimators that eliminate individual-specific trends. I apply the results, and propose alternative estimators, to …
Inverse probability weighted estimation for general missing data problems
Panel data methods for fractional response variables with an application to test pass rates
On estimating firm-level production functions using proxy variables to control for unobservables
Efficient Estimation of Average Treatment Effects with Mixed Categorical and Continuous Data
In this article, we consider the nonparametric estimation of average treatment effects when there exist mixed categorical and continuous covariates. One distinguishing feature of the approach presented herein is the use of kernel smoothing for both the continuous and the discrete covariates. This approach, together with the cross-validation method. which we use for selecting the smoothing parameters, has the ability to automatically remove irrele…
Recent Developments in the Econometrics of Program Evaluation
Many empirical questions in economics and other social sciences depend on causal effects of programs or policies. In the last two decades, much research has been done on the econometric and statistical analysis of such causal effects. This recent theoretical literature has built on, and combined features of, earlier work in both the statistics and econometrics literatures. It has by now reached a level of maturity that makes it an important tool …
Estimating panel data models in the presence of endogeneity and selection
How do Principals Assign Students to Teachers? Finding Evidence in Administrative Data and the Implications for Value Added: How do Principals Assign Students to Teachers
The federal government's Race to the Top competition has promoted the adoption of test-based value-added measures (VAM) of performance as a component of teacher evaluations throughout many states, but the validity of these measures has been controversial among researchers and widely contested by teachers' unions. A key concern is the extent to which nonrandom sorting of students to teachers may bias the results and lead to a misclassification of …
Control Function Methods in Applied Econometrics
This paper provides an overview of control function (CF) methods for solving the problem of endogenous explanatory variables (EEVs) in linear and nonlinear models. CF methods often can be justified in situations where “plug-in” approaches are known to produce inconsistent estimators of parameters and partial effects. Usually, CF approaches require fewer assumptions than maximum likelihood, and CF methods are computationally simpler. The recent fo…
What Are We Weighting For
When estimating population descriptive statistics, weighting is called for if needed to make the analysis sample representative of the target population. With regard to research directed instead at estimating causal effects, we discuss three distinct weighting motives: (1) to achieve precise estimates by correcting for heteroskedasticity; (2) to achieve consistent estimates by correcting for endogenous sampling; and (3) to identify average partia…
When Should You Adjust Standard Errors for Clustering?
In empirical work in economics it is common to report standard errors that account for clustering of units.Typically, the motivation given for the clustering adjustments is that unobserved components in outcomes for units within clusters are correlated.However, because correlation may occur across more than one dimension, this motivation makes it difficult to justify why researchers use clustering in some dimensions, such as geographic, but not o…
Correlated random effects models with unbalanced panels
Inference in Approximately Sparse Correlated Random Effects Probit Models With Panel Data
We propose a simple procedure based on an existing “debiased” l1-regularized method for inference of the average partial effects (APEs) in approximately sparse probit and fractional probit models with panel data, where the number of time periods is fixed and small relative to the number of cross-sectional observations. Our method is computationally simple and does not suffer from the incidental parameters problems that come from attempting to est…
Two-Way Fixed Effects, the Two-Way Mundlak Regression, and Difference-in-Differences Estimators
When Should You Adjust Standard Errors for Clustering?
Clustered standard errors, with clusters defined by factors such as geography, are widespread in empirical research in economics and many other disciplines. Formally, clustered standard errors adjust for the correlations induced by sampling the outcome variable from a data-generating process with unobserved cluster-level components. However, the standard econometric framework for clustering leaves important questions unanswered: (i) Why do we adj…
A simple, robust test for choosing the level of fixed effects in linear panel data models
Heterogeneity and Heteroskedasticity in Endogenous Switching Models: Estimating the Effects of Physician Advice on Calorie Consumption
We describe two‐step control function estimation procedures for estimating average treatment effects in constant coefficient endogenous switching models with a binary treatment. We allow for additional heterogeneity by allowing for heteroskedasticity in the latent error in the treatment assignment equation. We apply our estimation procedures to evaluate the causal effect of physician advice on calorie consumption using National Health and Nutriti…
Econometrics (27 works) · Mathematics (25 works) · Statistics (23 works) · Computer Science (20 works) · Estimator (16 works) · Spatial and Panel Data Analysis (13 works) · Economics (12 works) · Panel data (10 works) · Statistical Methods and Inference (10 works) · Advanced Causal Inference Techniques (9 works)