US Natural Gas Market Classification Using Pooled Regression

Natural gas marketing has considerably evolved since the early 1990s, when a set of liberalizing rules were passed in both the United States and the European Union that eliminated state-driven regulations in favor of open energy markets. These new rules changed many things in the business of energet...

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Main Authors: Kalashnikov, Vyacheslav V., Pérez Valdés, Gerardo A., Matis, Timothy I., Kalashnykova, Nataliya I.
Format: Article
Language:English
Published: 2014
Online Access:http://eprints.uanl.mx/15252/1/360.pdf
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author Kalashnikov, Vyacheslav V.
Pérez Valdés, Gerardo A.
Matis, Timothy I.
Kalashnykova, Nataliya I.
author_facet Kalashnikov, Vyacheslav V.
Pérez Valdés, Gerardo A.
Matis, Timothy I.
Kalashnykova, Nataliya I.
author_sort Kalashnikov, Vyacheslav V.
collection Repositorio Institucional
description Natural gas marketing has considerably evolved since the early 1990s, when a set of liberalizing rules were passed in both the United States and the European Union that eliminated state-driven regulations in favor of open energy markets. These new rules changed many things in the business of energetics, and therefore new research opportunities arose. Econometric studies about natural gas emerged as an important area of study since natural gas may now be sold and traded in a number of stock markets, each one responding to potentially different behavioral drives. In this work, we present a method to differentiate sets of time series based on a regression model relating price, consumption, supply, and other factors. Our objective is to develop a method to classify different areas, regions, or states into groups or classes that share similar regression parameters. Once obtained, these groups may be used to make assumptions about corresponding natural gas prices in further studies.
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spelling eprints-152522019-04-29T18:32:05Z http://eprints.uanl.mx/15252/ US Natural Gas Market Classification Using Pooled Regression Kalashnikov, Vyacheslav V. Pérez Valdés, Gerardo A. Matis, Timothy I. Kalashnykova, Nataliya I. Natural gas marketing has considerably evolved since the early 1990s, when a set of liberalizing rules were passed in both the United States and the European Union that eliminated state-driven regulations in favor of open energy markets. These new rules changed many things in the business of energetics, and therefore new research opportunities arose. Econometric studies about natural gas emerged as an important area of study since natural gas may now be sold and traded in a number of stock markets, each one responding to potentially different behavioral drives. In this work, we present a method to differentiate sets of time series based on a regression model relating price, consumption, supply, and other factors. Our objective is to develop a method to classify different areas, regions, or states into groups or classes that share similar regression parameters. Once obtained, these groups may be used to make assumptions about corresponding natural gas prices in further studies. 2014 Article PeerReviewed text en cc_by_nc_nd http://eprints.uanl.mx/15252/1/360.pdf http://eprints.uanl.mx/15252/1.haspreviewThumbnailVersion/360.pdf Kalashnikov, Vyacheslav V. y Pérez Valdés, Gerardo A. y Matis, Timothy I. y Kalashnykova, Nataliya I. (2014) US Natural Gas Market Classification Using Pooled Regression. Mathematical Problems in Engineering, 2014. pp. 1-9. ISSN 1024-123X http://doi.org/10.1155/2014/695084 doi:10.1155/2014/695084
spellingShingle Kalashnikov, Vyacheslav V.
Pérez Valdés, Gerardo A.
Matis, Timothy I.
Kalashnykova, Nataliya I.
US Natural Gas Market Classification Using Pooled Regression
thumbnail https://rediab.uanl.mx/themes/sandal5/images/online.png
title US Natural Gas Market Classification Using Pooled Regression
title_full US Natural Gas Market Classification Using Pooled Regression
title_fullStr US Natural Gas Market Classification Using Pooled Regression
title_full_unstemmed US Natural Gas Market Classification Using Pooled Regression
title_short US Natural Gas Market Classification Using Pooled Regression
title_sort us natural gas market classification using pooled regression
url http://eprints.uanl.mx/15252/1/360.pdf
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