Comparison between best subset and lasso regression on consumer price index Malaysia
This research is aimed to determine the factors contributing to the prediction of the total Consumer Price Index (CPI) in Malaysia through model selection using two methods which are the best subset and LASSO regression. The outliers are identified using the leverage values and studentized delet...
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| Main Authors: | , , |
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| Format: | Conference or Workshop Item |
| Language: | English |
| Published: |
2021
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| Subjects: | |
| Online Access: | http://eprints.uthm.edu.my/6507/1/P13550_499a158fa53fb23c456f577fae1fa84c.pdf http://eprints.uthm.edu.my/6507/ https://doi.org/10.1063/5.0075657 |
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| Summary: | This research is aimed to determine the factors contributing to the prediction of the total Consumer Price Index
(CPI) in Malaysia through model selection using two methods which are the best subset and LASSO regression. The outliers
are identified using the leverage values and studentized deleted residuals while the multicollinearity variables will undergo
progressive elimination identified through Variance Inflation Factor (VIF) values. Both methods were compared using the
Mean Square Error of Prediction (MSE(P)) to find the best approach to display the CPI data. The model with the smallest
MSE(P) will be chosen as the best model. The result showed that the MSE(P) of the best model using both the best subset
regression and LASSO regression is almost the same. Therefore, the model selection using LASSO regression will be
chosen as the best approach due to the simple process in identifying the best model. The best LASSO model consists of
nine major categories such as food and non-alcoholic beverages (X1), alcoholic beverages and tobacco (X2), clothing and
footwear (X3), transport (X7), communication (X8), recreation service and culture (X9), education (X10), restaurants and
hotels (X11), miscellaneous goods and services (X12). |
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