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BAYESIAN APPROACH IN MULTINOMIAL PROBIT MODEL: INVESTIGATION OF COOKING OIL CONSUMPTION IN TURKEY

Year 2017, Volume: 19 Issue: 2, 441 - 459, 27.12.2017

Abstract

In this study, it was aimed to
determine the factors effecting oil consumption in Turkey utilizing the
TurkStat’s Household Budget Survey of 2009. Multinomial Probit model was fitted
to the data, and model parameters were estimated using maximum likelihood and bayesian
approaches. While the significance and signs of parameters estimated by both
approaches exhibited similarities, the magnitudes of parameter estimates are
observed differently.

References

  • Albert, J. H., & Chib, S. (1993). Bayesian analysis of binary and polychotomous response data. Journal of the American statistical Association, 88(422), 669-679.
  • Berrett, C., & Calder, C. A. (2012). Data augmentation strategies for the Bayesian spatial probit regression model. Computational Statistics & Data Analysis, 56(3), 478-490.
  • Burgette, L. F., & Hahn, P. R. (2010). Symmetric Bayesian multinomial probit models. Duke University Statistical Science Technical Report, 1-20.
  • Dow, J. K., & Endersby, J. W. (2004). Multinomial probit and multinomial logit: a comparison of choice models for voting research. Electoral studies, 23(1), 107-122.
  • Greene, W. H. (2003). Econometric analysis. Pearson Education India.
  • Gelman, A., & Rubin, D. B. (1992). Inference from iterative simulation using multiple sequences. Statistical science, 457-472.
  • Gujarati, D. N. (2009). Basic econometrics. Tata McGraw-Hill Education.
  • Gupta, A. D. (2014). Multinomial Probit Model for Panel Data. Yüksek Lisans Tezi, California Üniversitesi, Los Angeles. http://escholarship.org/uc/item/24r48411. (Erişim: 05.07.2016 )
  • Hassan, R., & Nhemachena, C. (2008). Determinants of African farmers’ strategies for adapting to climate change: Multinomial choice analysis. African Journal of Agricultural and Resource Economics, 2(1), 83-104.
  • Hruschka, H. (2007). Using a heterogeneous multinomial probit model with a neural net extension to model brand choice. Journal of Forecasting, 26(2), 113-127.
  • Imai, K., & Van Dyk, D. A. (2005). MNP: R package for fitting the multinomial probit model. Journal of Statistical Software, 14(3), 1-32.
  • Jiao, X., & van Dyk, D. A. (2015). A corrected and more efficient suite of MCMC samplers for the multinomal probit model. arXiv preprint arXiv:1504.07823.
  • Koop, G. M. (2008). An introduction to econometrics. John Wiley and Sons.
  • McCulloch, R., & Rossi, P. E. (1994). An exact likelihood analysis of the multinomial probit model. Journal of Econometrics, 64(1), 207-240.
  • McCulloch, R. E., Polson, N. G., & Rossi, P. E. (2000). A Bayesian analysis of the multinomial probit model with fully identified parameters. Journal of econometrics, 99(1), 173-193.
  • Nobile, A. (1998). A hybrid Markov chain for the Bayesian analysis of the multinomial probit model. Statistics and Computing, 8(3), 229-242.
  • Nobile, A. (2000). Comment: Bayesian multinomial probit models with a normalization constraint. Journal of Econometrics, 99(2), 335-345.
  • TÜİK, Hanehalkı Bütçe Anketi Mikro Veri Seti, 2009.
  • Veettil, P. C., Speelman, S., Frija, A., Buysse, J., & Van Huylenbroeck, G. (2011). Complementarity between water pricing, water rights and local water governance: A Bayesian analysis of choice behaviour of farmers in the Krishna river basin, India. Ecological Economics, 70(10), 1756-1766. Vincent, T. L., Green, P. J., & Woolfson, D. N. (2012). LOGICOIL—multi-state prediction of coiled-coil oligomeric state. Bioinformatics, 29(1), 69-76.
  • Yavuz, S., & Yüceşahin, M. M. (2012). Türkiye'de hanehalkı kompozisyonlarında değişimler ve bölgesel farklılaşmalar. Sosyoloji Araştırmaları Dergisi, 15(1). Yu, L., & Xie, Q. (2011). Bayesian estimation of multinomial probit model for commuter mode choice. In Service Operations, Logistics, and Informatics (SOLI), 2011 IEEE International Conference on (pp. 12-15). IEEE.

MULTİNOMİAL PROBİT MODELİNDE BAYES YAKLAŞIMI: TÜRKİYE’DE YAĞ TÜKETİM TERCİHİNİN İNCELENMESİ

Year 2017, Volume: 19 Issue: 2, 441 - 459, 27.12.2017

Abstract

Çalışmada Türkiye İstatistik Kurumu’nun 2009 yılı Hanehalkı Bütçe Anketi
verilerinden yararlanılarak Türkiye’de
yağ tüketim tercihlerini etkileyen
etmenlerin belirlenmesi amaçlanmıştır. Dört yağ çeşidinin tercihine ilişkin
olarak oluşturulan multinomial probit modelin parametre tahminleri en çok
olabilirlik ve bayes yaklaşımları kullanılarak elde edilmiştir. Çalışmanın
sonucuna göre, tahmin edilen parametrelerin anlamlılıkları ve işaretleri
benzerlik göstermektedir. Bunun yanında, yöntemler arasındaki farklılıklardan
kaynaklı olarak parametre tahminlerinin büyüklüklerinde değişiklik
görülmektedir.

References

  • Albert, J. H., & Chib, S. (1993). Bayesian analysis of binary and polychotomous response data. Journal of the American statistical Association, 88(422), 669-679.
  • Berrett, C., & Calder, C. A. (2012). Data augmentation strategies for the Bayesian spatial probit regression model. Computational Statistics & Data Analysis, 56(3), 478-490.
  • Burgette, L. F., & Hahn, P. R. (2010). Symmetric Bayesian multinomial probit models. Duke University Statistical Science Technical Report, 1-20.
  • Dow, J. K., & Endersby, J. W. (2004). Multinomial probit and multinomial logit: a comparison of choice models for voting research. Electoral studies, 23(1), 107-122.
  • Greene, W. H. (2003). Econometric analysis. Pearson Education India.
  • Gelman, A., & Rubin, D. B. (1992). Inference from iterative simulation using multiple sequences. Statistical science, 457-472.
  • Gujarati, D. N. (2009). Basic econometrics. Tata McGraw-Hill Education.
  • Gupta, A. D. (2014). Multinomial Probit Model for Panel Data. Yüksek Lisans Tezi, California Üniversitesi, Los Angeles. http://escholarship.org/uc/item/24r48411. (Erişim: 05.07.2016 )
  • Hassan, R., & Nhemachena, C. (2008). Determinants of African farmers’ strategies for adapting to climate change: Multinomial choice analysis. African Journal of Agricultural and Resource Economics, 2(1), 83-104.
  • Hruschka, H. (2007). Using a heterogeneous multinomial probit model with a neural net extension to model brand choice. Journal of Forecasting, 26(2), 113-127.
  • Imai, K., & Van Dyk, D. A. (2005). MNP: R package for fitting the multinomial probit model. Journal of Statistical Software, 14(3), 1-32.
  • Jiao, X., & van Dyk, D. A. (2015). A corrected and more efficient suite of MCMC samplers for the multinomal probit model. arXiv preprint arXiv:1504.07823.
  • Koop, G. M. (2008). An introduction to econometrics. John Wiley and Sons.
  • McCulloch, R., & Rossi, P. E. (1994). An exact likelihood analysis of the multinomial probit model. Journal of Econometrics, 64(1), 207-240.
  • McCulloch, R. E., Polson, N. G., & Rossi, P. E. (2000). A Bayesian analysis of the multinomial probit model with fully identified parameters. Journal of econometrics, 99(1), 173-193.
  • Nobile, A. (1998). A hybrid Markov chain for the Bayesian analysis of the multinomial probit model. Statistics and Computing, 8(3), 229-242.
  • Nobile, A. (2000). Comment: Bayesian multinomial probit models with a normalization constraint. Journal of Econometrics, 99(2), 335-345.
  • TÜİK, Hanehalkı Bütçe Anketi Mikro Veri Seti, 2009.
  • Veettil, P. C., Speelman, S., Frija, A., Buysse, J., & Van Huylenbroeck, G. (2011). Complementarity between water pricing, water rights and local water governance: A Bayesian analysis of choice behaviour of farmers in the Krishna river basin, India. Ecological Economics, 70(10), 1756-1766. Vincent, T. L., Green, P. J., & Woolfson, D. N. (2012). LOGICOIL—multi-state prediction of coiled-coil oligomeric state. Bioinformatics, 29(1), 69-76.
  • Yavuz, S., & Yüceşahin, M. M. (2012). Türkiye'de hanehalkı kompozisyonlarında değişimler ve bölgesel farklılaşmalar. Sosyoloji Araştırmaları Dergisi, 15(1). Yu, L., & Xie, Q. (2011). Bayesian estimation of multinomial probit model for commuter mode choice. In Service Operations, Logistics, and Informatics (SOLI), 2011 IEEE International Conference on (pp. 12-15). IEEE.
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Authors

Çiler Sizege This is me

Publication Date December 27, 2017
Published in Issue Year 2017 Volume: 19 Issue: 2

Cite

APA Sizege, Ç. (2017). MULTİNOMİAL PROBİT MODELİNDE BAYES YAKLAŞIMI: TÜRKİYE’DE YAĞ TÜKETİM TERCİHİNİN İNCELENMESİ. Trakya Üniversitesi Sosyal Bilimler Dergisi, 19(2), 441-459.
AMA Sizege Ç. MULTİNOMİAL PROBİT MODELİNDE BAYES YAKLAŞIMI: TÜRKİYE’DE YAĞ TÜKETİM TERCİHİNİN İNCELENMESİ. Trakya Üniversitesi Sosyal Bilimler Dergisi. December 2017;19(2):441-459.
Chicago Sizege, Çiler. “MULTİNOMİAL PROBİT MODELİNDE BAYES YAKLAŞIMI: TÜRKİYE’DE YAĞ TÜKETİM TERCİHİNİN İNCELENMESİ”. Trakya Üniversitesi Sosyal Bilimler Dergisi 19, no. 2 (December 2017): 441-59.
EndNote Sizege Ç (December 1, 2017) MULTİNOMİAL PROBİT MODELİNDE BAYES YAKLAŞIMI: TÜRKİYE’DE YAĞ TÜKETİM TERCİHİNİN İNCELENMESİ. Trakya Üniversitesi Sosyal Bilimler Dergisi 19 2 441–459.
IEEE Ç. Sizege, “MULTİNOMİAL PROBİT MODELİNDE BAYES YAKLAŞIMI: TÜRKİYE’DE YAĞ TÜKETİM TERCİHİNİN İNCELENMESİ”, Trakya Üniversitesi Sosyal Bilimler Dergisi, vol. 19, no. 2, pp. 441–459, 2017.
ISNAD Sizege, Çiler. “MULTİNOMİAL PROBİT MODELİNDE BAYES YAKLAŞIMI: TÜRKİYE’DE YAĞ TÜKETİM TERCİHİNİN İNCELENMESİ”. Trakya Üniversitesi Sosyal Bilimler Dergisi 19/2 (December 2017), 441-459.
JAMA Sizege Ç. MULTİNOMİAL PROBİT MODELİNDE BAYES YAKLAŞIMI: TÜRKİYE’DE YAĞ TÜKETİM TERCİHİNİN İNCELENMESİ. Trakya Üniversitesi Sosyal Bilimler Dergisi. 2017;19:441–459.
MLA Sizege, Çiler. “MULTİNOMİAL PROBİT MODELİNDE BAYES YAKLAŞIMI: TÜRKİYE’DE YAĞ TÜKETİM TERCİHİNİN İNCELENMESİ”. Trakya Üniversitesi Sosyal Bilimler Dergisi, vol. 19, no. 2, 2017, pp. 441-59.
Vancouver Sizege Ç. MULTİNOMİAL PROBİT MODELİNDE BAYES YAKLAŞIMI: TÜRKİYE’DE YAĞ TÜKETİM TERCİHİNİN İNCELENMESİ. Trakya Üniversitesi Sosyal Bilimler Dergisi. 2017;19(2):441-59.
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