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International Conference of the Thailand Econometrics Society

TES 2018: Predictive Econometrics and Big Data pp 613–628Cite as

Asymmetric Effect with Quantile Regression for Interval-Valued Variables

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Part of the book series: Studies in Computational Intelligence ((SCI,volume 753))

Abstract

In this paper, we propose a quantile regression with interval valued data using a convex combination method. The model we propose generalizes series of existing models, say typically with the center method. Three estimation techniques consisting EM algorithm, Least squares, Lasso penalty are presented to estimate the unknown parameters of our model. A series of Monte Carlo experiments are conducted to assess the performance of our proposed model. The results support our theoretical properties. Finally, we apply our model to empirical data in order to show the usefulness of the proposed model. The results imply that the EM algorithm provides a best fit estimation for our data set and captures the effect of oil differently across various quantile levels.

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Author information

Authors and Affiliations

  1. Faculty of Economics, Chiang Mai University, Chiang Mai, 52000, Thailand

    Teerawut Teetranont, Woraphon Yamaka & Songsak Sriboonchitta

  2. Puey Ungphakorn Center of Excellence in Econometrics, Chiang Mai University, Chiang Mai, 52000, Thailand

    Woraphon Yamaka & Songsak Sriboonchitta

Authors
  1. Teerawut Teetranont
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  2. Woraphon Yamaka
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  3. Songsak Sriboonchitta
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Corresponding author

Correspondence to Teerawut Teetranont .

Editor information

Editors and Affiliations

  1. Computer Science Department, University of Texas at El Paso, El Paso, Texas, USA

    Vladik Kreinovich

  2. International College, Chiang Mai University, Chiang Mai, Thailand

    Songsak Sriboonchitta

  3. International College, Chiang Mai University, Chiang Mai, Thailand

    Nopasit Chakpitak

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Teetranont, T., Yamaka, W., Sriboonchitta, S. (2018). Asymmetric Effect with Quantile Regression for Interval-Valued Variables. In: Kreinovich, V., Sriboonchitta, S., Chakpitak, N. (eds) Predictive Econometrics and Big Data. TES 2018. Studies in Computational Intelligence, vol 753. Springer, Cham. https://doi.org/10.1007/978-3-319-70942-0_44

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  • DOI: https://doi.org/10.1007/978-3-319-70942-0_44

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-70941-3

  • Online ISBN: 978-3-319-70942-0

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