Forecasting of Thailand's rice exports price: Based on ridge and Lasso regression
© 2019 Association for Computing Machinery. Forecasting Thai rice exports price is principal for both producers and buyers. The broad set of potential factors influence the price, which might lead to multicollinearity problems. Ridge and Lasso regressions are able to solve these problems via shrinki...
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| Main Authors: | , , |
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| Format: | Conference Proceeding |
| Published: |
2020
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| Subjects: | |
| Online Access: | https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85074836336&origin=inward http://cmuir.cmu.ac.th/jspui/handle/6653943832/67713 |
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| Institution: | Chiang Mai University |
| Summary: | © 2019 Association for Computing Machinery. Forecasting Thai rice exports price is principal for both producers and buyers. The broad set of potential factors influence the price, which might lead to multicollinearity problems. Ridge and Lasso regressions are able to solve these problems via shrinking the parameters. Thus, we employ ridge and lasso to forecast Thai rice exports price. Estimated results of forecasting of Thailand's rice exports price show that Lasso model provides better forecasting performance based on MAE, MSE, RMSE, and MAPE criterion. The estimated results from ridge regression also suggest that Thai rice production, Indian rice export quantity, Indian rice production, Indian rice ending stock, Indian rice export price, Vietnamese rice export quantity, Vietnamese exchange rate Vietnamese rice export price, GDP of importer Thai rice, and population of importer Thai rice have the positive effects on Thai rice exports price. |
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