Quantile Regression for Cross-Sectional and Time Series Data: Applications in Energy Markets Using R

Description

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.    Preface About This Book Contents 1 Why and When Should Quantile Regression Be Used? References 2 A Case Study: Modeling Energy Markets by the Means of Quantile Regression 2.1 Energy Markets 2.2 Energy and Quantile Regression: An Overview of Existing Analysis References 3 Quantile Regression: A Methodological Overview 3.1 Definition of Quantile and Conditional Quantile 3.2 Estimating the Quantile in the Univariate Case 3.3 Quantile Regression Estimation 3.4 Quantile Regression Estimation Versus Weighted Quantile Regression Estimation References 4 Cross-sectional Quantile Regression 4.1 Data Source 4.2 Weighted Versus Unweighted Linear Regression: A Simple Example 4.3 Quantile Regression in a Simple One-Covariate Model 4.4 Coefficient Interpretation 4.5 Quantile Regression in a Multiple-Covariate Model 4.6 Conditional Versus Unconditional Quantile Regression 4.7 Summarizing Remarks References 5 Time Series Quantile Regression 5.1 Data Source 5.2 Natural Gas Prices as a Determinant of Electricity Prices—An OLS Example 5.3 Quantile Regression in a Simple One-Covariate Model 5.4 Coefficient Interpretation 5.5 Autoregressive Quantiles 5.6 Summarizing Remarks Reference 6 Goodness of Fit in Quantile Regression Models Reference 7 Novel Approaches in Quantile Regression 7.1 Nonparametric Quantile Regression 7.2 The Cross-Quantilogram for Time Series 7.2.1 The Cross-Quantilogram Definition 7.2.2 Q-Test for Directional Predictability 7.2.3 The Stationary Bootstrap 7.3 Quantile Regression Forests References 8 What Have We Learned from Quantile Regression? Implications for Economics and Finance Appendix Programs for Quantile Regression and Implementation in R