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1 Headnote.The ability of quantile regression models to characterize the heterogeneous impact of variables on different points of an outcome distribution makes them appealing in many economic applications. However, in observational studies, the variables of interest (e.g. education, prices) are often endogenous, making conventional quantile regression(More)
We develop results for the use of LASSO and Post-LASSO methods to form first-stage predictions and estimate optimal instruments in linear instrumental variables (IV) models with many instruments, p, that apply even when p is much larger than the sample size, n. We rigorously develop asymptotic distribution and inference theory for the resulting IV(More)
We introduce a class of instrumental quantile regression methods for heterogeneous treatment effect models and simultaneous equations models with nonadditive errors and offer computable methods for estimation and inference. These methods can be used to evaluate the impact of endogenous variables or treatments on the entire distribution of outcomes. We(More)
Quantile regression is an increasingly important tool that estimates the conditional quantiles of a response Y given a vector of regressors D. It usefully generalizes Laplace's median regression and can be used to measure the effect of covariates not only in the center of a distribution, but also in the upper and lower tails. For the linear quantile model(More)
—We use instrumental quantile regression approach to examine the effects of 401(k) plans on wealth using data from the Survey of Income and Program Participation. Using 401(k) eligibility as an instrument for 401(k) participation, we estimate the quantile treatment effects of participation in a 401(k) plan on several measures of wealth. The results show the(More)
In this paper, we consider estimation of nonlinear panel data models that include individual specific fixed effects. Estimation of these models is complicated by the incidental parameters problem; that is, noise in the estimation of the fixed effects when the time dimension is short generally results in inconsistent estimates of the common parameters due to(More)
In this paper, we consider simple and practical methods for performing het-eroskedasticity and autocorrelation consistent inference in linear instrumental variables models with weak instruments. We show that conventional inference procedures based on the reduced form about the relevance of the instruments excluded from the structural equation lead to tests(More)
The MIT Faculty has made this article openly available. Please share how this access benefits you. Your story matters. Abstract. Under minimal assumptions finite sample confidence bands for quantile regression models can be constructed. These confidence bands are based on the " conditional pivotal property " of estimating equations that quantile regression(More)