Publications

2009
Imbens G, Wooldridge J. Recent Developments in the Econometrics of Program Evaluation. Journal of Economic Literature. 2009;47 (1) :5-86. PDF
Imbens G, Newey W. Identification and Estimation of Triangular Simultaneous Equations Models Without Additivity. Econometrica. 2009;77 (5) :1481-1512.Abstract

This paper uses control variables to identify and estimate models with nonseparable, multidimensional disturbances. Triangular simultaneous equations models are considered, with instruments and disturbances independent and reduced form that is strictly mono- tonic in a scalar disturbance. Here it is shown that the conditional cumulative distribution function of the endogenous variable given the instruments is a control variable. Also, for any control variable, identification results are given for quantile, average, and policy effects. Bounds are given when a common support assumption is not satisfied. Estimators of identified objects and bounds are provided and a demand analysis empirical example given.

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2008
Imbens G, Lemieux T. Regression Discontinuity Designs: A Guide to Practice. Journal of Econometrics. 2008;142 (2) :615-635. PDF
Imbens G, Lemieux T. Special Issue Editors’ Introduction: The Regression Discontinuity Design - Theory and Applications. Journal of Econometrics. 2008;142 (2) :611-614. Website PDF
Imbens G, Crump R, Hotz JV, Mitnik O. Nonparametric Tests for Treatment Effect Heterogeneity. Review of Economics and Statistics. 2008;90 (3) :389-405.Abstract

In this paper we develop two nonparametric tests of treatment effect heterogeneity. The first test is for the null hypothesis that the treatment has a zero average effect for all subpopulations defined by covariates. The second test is for the null hypothesis that the average effect conditional on the covariates is identical for all subpopulations, that is, that there is no heterogeneity in average treatment effects by covariates. We derive tests that are straightforward to implement and illustrate the use of these tests on data from two sets of experimental evaluations of the effects of welfare-to-work programs.

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Imbens G, Abadie A. On the Failure of the Bootstrap for Matching Estimators. Econometrica. 2008;76 (6) :1537-1557. PDF
2007
Imbens G, Chernozhukov V, Newey W. Instrumental Variable Estimation of Nonseparable Models. Journal of Econometrics. 2007;139 (1) :4-14. PDF
Imbens G, Blundell R, Newey W, Persson T. Nonadditive Models with Endogenous Regressors. In: Advances in Economics and Econometrics, Ninth World Congress of the Econometric Society, Vol.III. ; 2007. pp. Chapter 2. PDF
Imbens G, Athey S. Discrete Choice Models with Multiple Unobserved Choice Characteristics. International Economic Review. 2007;48 (4) :1159-1192. PDF
2006
Imbens G, Hotz JV, Klerman J. Evaluating the Differential Effects of Alternative Welfare-to-Work Training Components: A Re-Analysis of the California GAIN Program. Journal of Labor Economics. 2006;24 (3) :521-566. PDF
Imbens G, Lynch L. Re-employment Probabilities Over the Business Cycle. Portuguese Economic Journal. 2006;5 (2) :111-134. PDF
Imbens G, Abadie A. Large Sample Properties of Matching Estimators for Average Treatment Effects. Econometrica. 2006;74 (1) :235-267. PDF
Imbens G, Athey S. Identification and Inference in Nonlinear Difference-In-Differences Models. Econometrica. 2006;74 (2) :431-497. PDF
2005
Imbens G, Porter J. Bias-adjusted Nearest Neighbor Estimation for the Partial Linear Model. 2005.Abstract

In semiparametric models estimation methods interest is often in the finite dimensional parameter, with the nonparametric component a nuisance function. In many examples, including Robinson’s partial linear model and the estimation of average treatment effects, the nuisance function is a conditional expectation. For the large sample properties of the estimators of the parameters of interest it is typically important that the estimators for these nuisance functions satisfy certain bias and variance properties. Estimators that have been used in these settings include series estimators and higher order kernel methods. In both cases the smoothing parameters have to be choosen in a sample-size dependent manner. On the other hand, nearest neighbor methods with a fixed number of neighbours do not rely on sample size dependent smoothing parameters, but they often violate the conditions on the rate of the bias unless the covariates in the regression are of very low dimension. In many cases only scalar covariates are allowed. In this paper we develop an alternative method for estimating the unknown regression functions that, like nearest neighbor methods, does not rely on sample-size dependent smoothing parameters, but that, like the series and higher order kernel methods, does not suffer from bias-rate problems. We do so by combining nearest neighbor methods with local polynomial regression using a fixed number of neighbors.

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Imbens G, Graham B, Ridder G. Measuring the Average Outcome and Inequality Effects of Segregation in the Presence of Social Spillovers. 2005.Abstract

In this paper we provide a nonparametric treatment of identification in models with social spillovers. We consider a setting with ‘high’ and ‘low’ type individuals. Individual outcomes depend upon the fraction of high types in one’s group. We refer to this dependence as a social spillover or peer group effect. We define estimands measuring local and global spillover strength as well as the outcome and inequality effects of increasing segregation (by type) across groups. We relate our estimands to the theory of sorting in the presence of social externalities.

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Imbens G, Rosenbaum P. Randomization Inference with an Instrumental Variable. Journal of the Royal Statistical Society, Series A. 2005;168 (1) :109-126. PDF
Imbens G, Spady R. The Performance of Empirical Likelihood and its Generalizations. Identification and Inference for Econometric Models, Essays in Honor of Thomas Rothenberg, Cambridge University Press. 2005.Abstract

We calculate higher-order asymptotic biases and mean-squared errors (MSE) for a simple model with a sequence of moment conditions. In this setup, generalized empirical likelihood (GEL) and infeasible optimal GMM (OGMM) have the same higher-order biases, with GEL apparently having an MSE that exceeds OGMM's by an additional term of order (M - 1)/N, i.e. the degree of overidentification divided by sample size. In contrast, any two-step GMM estimator has an additional bias relative to OGMM of order (M - 1)/N and an additional MSE of order (M-1)^2/N. Consequently, GEL must be expected to dominate two-step GMM. In our simple model all GEL's have equivalent next higher order behavior because generalized third moments of moment conditions are assumed to be zero; we explore in further analysis and simulations the implications of dropping this assumption.

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Imbens G, Hotz J, Mortimer J. Predicting the Efficacy of Future Training Programs Using Past. Journal of Econometrics. 2005;125 (1-2) :241-270. PDF

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