Learn More
Classical least squares regression may b e v i e w ed as a natural way of extending the idea of estimating an unconditional mean parameter to the problem of estimating conditional mean functions; the crucial link is the formulation of an optimization problem that encompasses both problems. Likewise, quantile regression ooers an extension of univariate(More)
Terms and Conditions of Use provides, in part, that unless you have obtained prior permission, you may not download an entire issue of a journal or multiple copies of articles, and you may use content in the JSTOR archive only for your personal, non-commercial use. Each copy of any part of a JSTOR transmission must contain the same copyright notice that(More)
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)
The penalized least squares interpretation of the classical random effects estimator suggests a possible way forward for quantile regression models with a large number of " fixed effects ". The introduction of a large number of individual fixed effects can significantly inflate the variability of estimates of other covariate effects. Regularization, or(More)
Quantile regression (QR) fits a linear model for conditional quantiles, just as ordinary least squares (OLS) fits a linear model for conditional means. An attractive feature of OLS is that it gives the minimum mean square error linear approximation to the conditional expectation function even when the linear model is misspecified. Empirical research using(More)
This paper proposes a method to address the longstanding problem of lack of monotonicity in estimation of conditional and structural quantile functions, also known as the quantile crossing problem. The method consists in sorting or monotone rearranging the original estimated non-monotone curve into a monotone rearranged curve. We show that the rearranged(More)
This paper identifies and evaluates rationales for team participation and for the effects of team composition on productivity using novel data from a garment plant that shifted from individual piece rate to group piece rate production over three years. The adoption of teams at the plant improved worker productivity by 14% on average. Productivity(More)
We consider quantile autoregression (QAR) models in which the au-toregressive coefficients can be expressed as monotone functions of a single, scalar random variable. The models can capture systematic influences of conditioning variables on the location, scale and shape of the conditional distribution of the response, and therefore constitute a significant(More)