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- ROGER KOENKER
- 2001

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)

- Christian Hansen, Takeshi Amemiya, +8 authors Igor Makarov
- 2001

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)

- ROGER KOENKER, ZHIJIE XIAO
- 2000

T ests based on the quantile regression process can be formulated like the classical Kolmogorov-Smirnov and Cr amer-von-Mises tests of goodness-of-t employing the theory of Bessel processes as in ?. H o wever, it is frequently desirable to formulate hypotheses involving unknown nuisance parameters, thereby jeopardizing the distribution free character of… (More)

- Joshua Angrist, Jinyong Hahn, Jerry Hausman, Frank Kleibergen, Roger Koenker, Rafael Lalive
- 2004

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)

- VICTOR CHERNOZHUKOV, IVÁN FERNÁNDEZ-VAL, +7 authors Shinichi Sakata
- 2005

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)

- BARTON H. HAMILTON, JACK A. NICKERSON, +6 authors Steven Levitt
- 2002

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)

- Ivan Fernandez-Val, VICTOR CHERNOZHUKOV, +5 authors Joonhwan Lee
- 2008

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- ROGER KOENKER, ZHIJIE XIAO
- 2004

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)