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Journal of Engineering and Applied Sciences
Year: 2018 | Volume: 13 | Issue: 2 | Page No.: 286-290
DOI: 10.36478/jeasci.2018.286.290  
Overcoming the Multicollinearity by Using Principal Component Regression in Economic Growth Model
Julita Nahar , Sri Purwani , Sudradjat Supian and Fatimah Khonsa Syahidah
 
Abstract: Development of a developing country is essentially aimed at improving the welfare and prosperity of its people. National or regional development puts more emphasis on development in the economic field. In the implementation of their economic development factors that influence it should be considered. One measure of the success can be seen from the economic growth. In modeling the economic growth we are often constrained by models that do not meet the assumptions, one of which is multicollinearity. This occurs because the data obtained is taken from uncontrollable circumstances. The existence of these cases can cause difficulty in separating the influence of each independent variable on the response variable, so, we need a method to solve it. One method that can be used is Principal Component Regression (PCR). PCR is one method that has been developed to overcome the problem of multicollinearity. PCR is a regression analysis of the variables in response to the principal components that are not correlated with each other, where each principal component is a linear combination of all predictor variables.
 
How to cite this article:
Julita Nahar, Sri Purwani, Sudradjat Supian and Fatimah Khonsa Syahidah, 2018. Overcoming the Multicollinearity by Using Principal Component Regression in Economic Growth Model. Journal of Engineering and Applied Sciences, 13: 286-290.
DOI: 10.36478/jeasci.2018.286.290
URL: http://medwelljournals.com/abstract/?doi=jeasci.2018.286.290