Economic applications of quantile regression
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Bibliographic Information
Economic applications of quantile regression
（Studies in empirical economics）
Physica-Verlag, c2002
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Includes bibliographical references
Description and Table of Contents
Description
Quantile regression has emerged as an essential statistical tool of contemporary empirical economics and biostatistics. Complementing classical least squares regression methods which are designed to estimate conditional mean models, quantile regression provides an ensemble of techniques for estimating families of conditional quantile models, thus offering a more complete view of the stochastic relationship among variables. This volume collects 12 outstanding empirical contributions in economics and offers an indispensable introduction to interpretation, implementation, and inference aspects of quantile regression.
Table of Contents
Individual heterogeneity in the returns to schooling: instrumental variables quantile regression using twins data.- Testing for uniform wage trends in West-Germany: A cohort analysis using quantile regressions for censored data.- Quantile regression with sample selection: Estimating women's return to education in the U.S..- Earning functions in Portugal 1982-1994: Evidence from quantile regressions.- Wage inequality in a developing country: decrease in minimum wage or increase in education returns.- How wide is the gap? An investigation of gender wage differences using quantile regression.- The public-private sector wage gap in Zambia in the 1990s: A quantile regression approach.- Asymmetric labor supply.- Quantile regression for duration data: A reappraisal of the Pennsylvania Reemployment Bonus Experiments.- For whom the reductions count: A quantile regression analysis of class size and peer effects on scholastic achievement.- The effects of demographics and maternal behavior on the distribution of birth outcomes.- Nonparametric quantile regression analysis of R & D-sales relationship for Korean firms.- Conditional value-at-risk: Aspects of modeling and estimation.- Portfolio style: Return-based attribution using quantile regression.- Integrated Conditional Moment testing of quantile regression models.
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