Quantile regression for cross-sectional and time series data
Material type:
TextPublication details: Springer Switzerland 2020 Description: x, 63 pISBN: 9783030445034Subject(s): R (Computer program language) | Time-series analysis | Quantile regressionDDC classification: 519.536 Summary: About this book
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.
| Item type | Current library | Collection | Call number | Copy number | Status | Date due | Barcode |
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Book
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Indian Institute of Management LRC General Stacks | IT & Decisions Sciences | 519.536 URI (Browse shelf(Opens below)) | 1 | Available | 003521 |
About this book
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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