Application of Principal Component Analysis (PCA) to Reduce Multicollinearity Exchange Rate Currency of Some Countries in Asia Period 2004-2014
Sri Rahayu , Teguh Sugiarto , Ludiro Madu , Holiawati , Ahmad Subagyo
This study aims to apply the model Principal component Analysis to reduce multicollinearity on variable currency exchange rate in eight countries in A.
- Pub. date: December 15, 2017
- Pages: 75-83
- 902 Downloads
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- 15 Citations
This study aims to apply the model Principal component Analysis to reduce multicollinearity on variable currency exchange rate in eight countries in Asia against US Dollar including the Yen (Japan), Won (South Korea), Dollar (Hongkong), Yuan (China), Bath (Thailand), Rupiah (Indonesia), Ringgit (Malaysia), Dollar (Singapore). It looks at yield levels of multicolinierity which is smaller in comparison with PCA applications using multiple regression. This study used multiple regression test and PCA application to investigate the differences in multicollinearity at yield. From this research, it can be concluded that the use of PCA analysis applications can reduce multicollinearity in variables in doing research.
Keywords: Principal component analysis (PCA), multiple regression, matrix var-cov, exchange rate
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References
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