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Seemingly Unrelated Regression Based Copula: An Application on Thai Rice Market

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Causal Inference in Econometrics

Part of the book series: Studies in Computational Intelligence ((SCI,volume 622))

Abstract

This paper introduced the seemingly unrelated regression (SUR) model based on Copula to improve a linear regression system since the conventional SUR model has a strong assumption of normally distributed residuals. The Copula density functions were incorporated into the likelihood to relax the restriction of the marginal distribution. The real dataset of Thai rice was used for an application comparing the conventional SUR model estimated by GLS and the Copula-based SUR model. The result indicated that the Copula-based SUR model performed slightly better than the conventional SUR. In addition, the estimated results showed that Gaussian Copula was the most appropriate function for being the linkage between the marginal distributions. Moreover, the marginal distributions also were tested, and the result showed that a normal distribution and student-t distribution were the best fit for the marginal distributions of demand and supply equations, respectively.

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Acknowledgments

The authors are grateful to Puey Ungphakorn Centre of Excellence in Econometrics, Faculty of Economics, Chiang Mai University for the financial support. We also would like to thank Prof. Thierry Denoeux for his insightful comment and encoragement.

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Authors and Affiliations

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

    Pathairat Pastpipatkul, Paravee Maneejuk & Songsak Sriboonchitta

  2. Faculty of Economics, Prince of Songkla University, Hat Yai, Thailand

    Aree Wiboonpongse

  3. Faculty of Agriculture, Department of Agricultural Economics and Agricultural Extension, Chiang Mai University, Chiang Mai, Thailand

    Aree Wiboonpongse

  4. Faculty of Economics, Institute for Sufficiency Economy Research and Promotion, Chiang Mai University, Chiang Mai, Thailand

    Aree Wiboonpongse

Authors
  1. Pathairat Pastpipatkul
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  2. Paravee Maneejuk
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  3. Aree Wiboonpongse
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  4. Songsak Sriboonchitta
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Corresponding author

Correspondence to Paravee Maneejuk .

Editor information

Editors and Affiliations

  1. School of Knowledge Science, Japan Advanced Ins. of Sci. & Tech, Ishikawa, Japan

    Van-Nam Huynh

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

    Vladik Kreinovich

  3. Faculty of Economics, Chiang Mai University, Chiangmai, Thailand

    Songsak Sriboonchitta

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Pastpipatkul, P., Maneejuk, P., Wiboonpongse, A., Sriboonchitta, S. (2016). Seemingly Unrelated Regression Based Copula: An Application on Thai Rice Market. In: Huynh, VN., Kreinovich, V., Sriboonchitta, S. (eds) Causal Inference in Econometrics. Studies in Computational Intelligence, vol 622. Springer, Cham. https://doi.org/10.1007/978-3-319-27284-9_28

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  • DOI: https://doi.org/10.1007/978-3-319-27284-9_28

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-27283-2

  • Online ISBN: 978-3-319-27284-9

  • eBook Packages: EngineeringEngineering (R0)

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