revised version of Discussion paper 2001-16#### A new semiparametric estimator for estimating conditional expectation functions from incomplete data is proposed, which integrates parametric regression with nonparametric matching estimators. Besides its applicability to missing data situations due to non-response or attrition, the estimator can also be used for analyzing treatment effect heterogeneity and statistical treatment rules, where data on potential outcomes is missing by definition. By combining moments from a parametric specification with nonparametric estimates of mean outcomes in the non-responding population within a GMM framework, the estimator seeks to balance a good fit in the responding population with low bias in the non-responding population. The estimator is applied to analyzing treatment effect heterogeneity among Swedish rehabilitation programmes. Download Discussion Paper: (pdf, 562 kb) Download Appendix: (pdf, 354 kb) former title: Treatment Choice based on semiparametric evaluation methods
This book provides a comprehensive summary of the most promising estimation methods for the (dichotomous) selectivity models. Selectivity models, often referred to as sample selection models, are frequently used in structural analysis and evaluation studies, wherever individuals select among different alternatives. Selectivity models strive to estimate structural outcome equations under explicit consideration of the fact that individuals are heterogeneous and that the selection into or out of different alternatives (e.g. treatment/non-treatment) is not random and based on observed and unobserved characteristics. Hence individuals that are selected into one group are likely to be inherently different from individuals that selected into any other group. Neglecting this non-random selection leads to selection bias, either on the basis of observed characteristics or on unobservables, which is the focus of this work. The core idea of all approaches modelling this selection problem is to forecast counterfactual outcomes, that are the hypothetical outcomes a certain individual would have acquired if it selected into an other alternative. At first structural models contaminated by selectivity and the nature of the selection problem are defined rigorously. Different identifying assumptions such as exclusion restrictions, an index assumption, or identification at infinity are illuminated. An extensive discussion of parametric and semiparametric procedures for the 2-categories selectivity model exposes how the different estimators cope with the selection problem. In contrast to the parametric ones, like the Heckman two-step, the semiparametric estimators do not impose tight restrictions on the error terms. The estimators of Gallant/Nychka, Klein/Spady, Powell, Newey, Ahn/Powell, Robinson, Chen and Andrews/Schafgans are presented. Finally, the properties of these estimators, an illustrating example estimating the effect of unionism on wages, and recommendations for the application of these estimators are discussed. http://www.novapublishers.com/detailed_search.asp?id=1-59033-277-6
In this paper, the regression discontinuity design (RDD) is generalized to account for differences in observed covariates X in a fully nonparametric way. It is shown that the treatment effect can be estimated at the rate for one-dimensional nonparametric regression irrespective of the dimension of X. It thus extends the analysis of Hahn, Todd and van der Klaauw (2001) and Porter (2003), who examined identification and estimation without covariates, requiring assumptions that may often be too strong in applications. In many applications, individuals to the left and right of the threshold differ in observed characteristics. Houses may be constructed in different ways across school attendance district boundaries. Firms may differ around a threshold that implies certain legal changes, etc. Accounting for these differences in covariates is important to reduce bias. In addition, accounting for covariates may also reduces variance. Finally, estimation of quantile treatment effects (QTE) is also considered.
revised version of Discussion paper 2002-08#### Propensity score matching is frequently used for estimating average treatment effects. Its applicability, however, is not confined to treatment evaluation. In this paper it is shown that propensity score matching does not hinge on a selection on observables assumption and can be used to estimate not only adjusted means but also their distributions, even with non-iid sampling. Propensity score matching is used to analyze the gender wage gap among graduates in the UK. It is found that subject of degree contributes substantially to explaining the gender wage gap, particularly at higher quantiles of the wage distribution. Download Discussion Paper: (pdf, 910 kb)
This paper reviews the main identification and estimation strategies for microeconometric policy evaluation. Particular emphasis is laid on evaluating policies consisting of multiple programmes, which is of high relevance in practice. For example, active labour market policies may consist of different training programmes, employment programmes and wage subsidies. Similarly, sickness rehabilitation policies often offer different vocational as well as non-vocational rehabilitation measures. First, the main identification strategies (control-for-confounding-variables, difference-in-difference, instrumental-variable, and regression-discontinuity identification) are discussed in the multiple-programme setting. Thereafter, the different nonparametric matching and weighting estimators of the average treatment effects and their properties are examined. Download Discussion Paper: (pdf, 484 kb)
Lecture Notes in Economics and Mathematical Systems, Vol. 524, Heidelberg: Springer#### Policy evaluation and programme choice are important tools for informed decision-making, for the administration of active labour market programmes, training programmes, tuition subsidies, rehabilitation programmes etc. Whereas the evaluation of programmes and policies is mainly concerned with an overall assessment of impact, benefits and costs, programme choice considers an optimal allocation of individuals to the programmes. This book surveys potential evaluation strategies for policies with multiple programmes and discusses evaluation and treatment choice in a coherent framework. Recommendations for choosing appropriate evaluation estimators are derived. Furthermore, a semiparametric estimator of optimal treatment choice is developed to assist in the optimal allocation of participants. http://www.springeronline.com/sgw/cda/frontpage/0,10735,5-169-22-2272343...
revised version of Discussion paper 2001-12#### Finite-sample properties of nonparametric regression for binary dependent variables are analyzed. Nonparametric regression is generally considered as highly variable in small samples when the number of regressors is large. In binary choice models, however, it may be more reliable since its variance is bounded. The precision in estimating conditional means as well as marginal effects is investigated in settings with many explanatory variables (14 regressors) and small sample sizes (250 or 500 observations). The Klein Spady estimator, Nadaraya-Watson regression and local linear regression often perform poorly. Local logit regression, on the other hand, is 10 to 70% more precise than parametric regression. In an application to female labour supply, local logit finds heterogeneity in the effects of children on employment that is not detected by parametric nor semiparametric estimation. Download Discussion Paper: (pdf, 921 kb) former title: Applied higher-dimensional nonparametric regression
