Applied Quantile Regression for Economics and Finance (June 25 to 29)

Prof. J. M. Uribe (Univ. del Valle, Colombia), Language: English, MORNING: 10.00 am to 1.00 pm.


Applied Quantile Regression for Economics and Finance



Jorge M. Uribe is a professor at the Department of Economics at Universidad del Valle, Colombia. He is also head of the research group in Applied Macroeconomics and Finance at the same institution and a research associate at the Riskcenter of the University of Barcelona. He got two Bachelor degrees in economics and finance, one Master in Economics at Universidad de los Andes, one Master of Research in Economics at the European University Institute, and a PhD in Economics at the University of Barcelona. His main research interests are financial econometrics, international finance and financial risk management.


Course language

Lectures and course materials are in English. Final assignments may be written in English or Spanish according to each student’s preferences.



June 25th to 29nd, from 10:00 to 13:00h.



Quantile regression is a powerful tool for the analysis of economic and financial systems in which cross sectional and time series observations are available. Since Koenker and Basset’s seminal contribution, quantile models have been of growing interest in many fields of economics, being applied in disciplines that range from finance to macroeconomics and labor economics. Quantile regression allows the researcher to study the relationship between economic variables not only at the center but also across the entire conditional distribution of the dependent variable. In traditional quantile regression, the quantiles of a dependent variable are assumed to be linearly dependent on a set of conditioning variables. Early applications of quantile regression typically relied on cross-sectional data, recorded for many individuals in a given period. But in recent times quantile regression has also become fundamental for the analysis of time series. In this course we examine both and introduce the students to recent advances in the literature, always from an applied perspective. The applications are illustrated with R.


Course goals

  • Identify situations in which quantile regression models may enhance our comprehension of a research phenomenon.
  • Understand the differences between quantile estimation and quantile regression.
  • Understand with a practical implementation for asset pricing, the use of quantile regression for cross-sectional data.
  • Understand via a practical implementation, for risk management in energy and stock markets, how quantile regression should be used for analyzing time-series data.
  • Utilize R for running quantile regression models, diagnostics analysis, and data visualizing.


Course contents

  1. Preliminaries: When to use quantile regression? Quantile estimation versus quantile regression.
  2. Cross-sectional quantile regression: Nonlinear asset pricing models.
  3. Time-series quantile regression: Risk management in energy and stock markets.
  4. Advanced tools in quantile regression: cross-quantilograms, quantile breaks, VaR models.
  5. Unconditional quantile regression.



Students will be evaluated based on a project to be submitted by the final class. This project will be an application of one of the methodologies covered during the lectures.