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Principal Component Regression For Tobit Model And Purchases Of Gold

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  • Fadel Hamid Hadi ALHUSSEINI
  • Meshal Harbi ODAH

Abstract

This study focuses on Tobit principal component regression model in the analysis of studied data when the response variable is censored at zero point. The studied model focuses on the gold quantity purchase by individuals. When examining the model, it suffers from multicollinearity problem. Which is considered a risky problem for the stability of the model and affects the accuracy of parameters estimation. Estimating regression model coefficients under multicollinearity problem will produce inaccurate estimations. Therefore, in order to obtain a viable estimation, the multicollinearity problem must first be handled. There are various methods for treating this problem, one of them being the method of principal component. In this paper, the are five dominant principal components selected based on the Eigenvalue greater than one. There are also six important independent variables in Quantities of gold purchased (gender of the respondent, monthly income of the respondent (in US dollars), (price of gold during the period of the study, age of the respondent, region of residence and marital status of the respondent). The rest of the independent variables was unimportant in Quantities of gold purchased ,with more details presented in section four.

Suggested Citation

  • Fadel Hamid Hadi ALHUSSEINI & Meshal Harbi ODAH, 2016. "Principal Component Regression For Tobit Model And Purchases Of Gold," Proceedings of the INTERNATIONAL MANAGEMENT CONFERENCE, Faculty of Management, Academy of Economic Studies, Bucharest, Romania, vol. 10(1), pages 491-500, November.
    • Handle: RePEc:rom:mancon:v:10:y:2016:i:1:p:491-500
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    References listed on IDEAS

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    1. J. N. R. Jeffers, 1967. "Two Case Studies in the Application of Principal Component Analysis," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 16(3), pages 225-236, November.
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