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.



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

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Paperback / softback by Jorge M. Uribe , Montserrat Guillen

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This brief addresses the estimation of quantile regression models from a practical perspective, which will support researchers who need to... Read more

    Publisher: Springer Nature Switzerland AG
    Publication Date: 31/03/2020
    ISBN13: 9783030445034, 978-3030445034
    ISBN10: 3030445038

    Number of Pages: 63

    Non Fiction , Business, Finance & Law

    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.



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