This brief addresses the estimation of quantile regression models from a practical perspective, which will support researchers who need to use conditional quantile regression to measure economic relationships among a set of variables. It will also benefit students using the methodology for the first time, and practitioners at private or public organizations who are interested in modeling different fragments of the conditional distribution of a given variable. The book pursues a practical approach with reference to energy markets, helping readers learn the main features of the technique more quickly. Emphasis is placed on the implementation details and the correct interpretation of the quantile regression coefficients rather than on the technicalities of the method, unlike the approach used in the majority of the literature. All applications are illustrated with R.

Jorge M. Uribe is an Associate Professor at the Universitat Oberta de Catalunya, Spain. He received a PhD in Economics from the University of Barcelona, Spain, in 2018. He is an Associate Researcher at UB Riskcenter, Barcelona, and has lead the Research Group in Quantitative Finance, Universidad del Valle, Colombia, since 2015. 

Montserrat Guillen is a Professor of Quantitative Methods and the Director of UB Riskcenter, a research center for risk analysis at the University of Barcelona, Spain. She is also an Honorary Professor of the Faculty of Actuarial Science and Insurance at the City University London, United Kingdom. She was honored with the ICREA Academia Distinction award for outstanding research. 

Why and When Should Quantile Regression Be Used?- A Case of Study: Modelling Energy Markets by the Means of Quantile Regression.- Quantile Regression: A Methodological Overview.- Cross-Sectional Quantile Regression.- Time Series Quantile Regression.- Goodness of Fit in Quantile Regression Models.- Novel Approaches in Quantile Regression.- What Have We Learned from Quantile Regression? Implications for Economics and Finance.- Appendix: Programs for Quantile Regression and Implementation in R.