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

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

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Bibliographische Detailangaben
1. Verfasser: Uribe, Jorge M. (VerfasserIn)
Format: Elektronisch E-Book
Sprache:English
Veröffentlicht: Cham Springer International Publishing AG 2020
Schriftenreihe:SpringerBriefs in Finance Ser
Schlagworte:
Quantile regression
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Inhaltsangabe:
  • Intro
  • 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.

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