Forecasting Oil Price by Hierarchical Shrinkage in Dynamic Parameter Models
The aim of this paper is to forecast monthly crude oil price with a hierarchical shrinkage approach, which utilizes not only LASSO for predictor selection, but a hierarchical Bayesian method to determine whether constant coefficient (CC) or time-varying parameter (TVP) predictive regression should b...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2020-01-01
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| Series: | Discrete Dynamics in Nature and Society |
| Online Access: | http://dx.doi.org/10.1155/2020/6640180 |
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| _version_ | 1850161671044071424 |
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| author | Yuntong Liu Yu Wei Yi Liu Wenjuan Li |
| author_facet | Yuntong Liu Yu Wei Yi Liu Wenjuan Li |
| author_sort | Yuntong Liu |
| collection | DOAJ |
| description | The aim of this paper is to forecast monthly crude oil price with a hierarchical shrinkage approach, which utilizes not only LASSO for predictor selection, but a hierarchical Bayesian method to determine whether constant coefficient (CC) or time-varying parameter (TVP) predictive regression should be employed in each out-of-sample forecasting step. This newly developed method has the advantages of both model shrinkage and automatic switch between CC and TVP forecasting models; thus, this may produce more accurate predictions of crude oil prices. The empirical results show that this hierarchical shrinkage model can outperform many commonly used forecasting benchmark methods, such as AR, unobserved components stochastic volatility (UCSV), and multivariate regression models in forecasting crude oil price on various forecasting horizons. |
| format | Article |
| id | doaj-art-ff41c36e30d5437ba24bc0d1b60361b5 |
| institution | OA Journals |
| issn | 1026-0226 1607-887X |
| language | English |
| publishDate | 2020-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Discrete Dynamics in Nature and Society |
| spelling | doaj-art-ff41c36e30d5437ba24bc0d1b60361b52025-08-20T02:22:45ZengWileyDiscrete Dynamics in Nature and Society1026-02261607-887X2020-01-01202010.1155/2020/66401806640180Forecasting Oil Price by Hierarchical Shrinkage in Dynamic Parameter ModelsYuntong Liu0Yu Wei1Yi Liu2Wenjuan Li3School of Finance, Yunnan University of Finance and Economics, Kunming, ChinaSchool of Finance, Yunnan University of Finance and Economics, Kunming, ChinaFaculty of Transportation Engineering, Kunming University of Science and Technology, Kunming, ChinaSchool of Statistics & Mathematics, Yunnan University of Finance and Economics, Kunming, ChinaThe aim of this paper is to forecast monthly crude oil price with a hierarchical shrinkage approach, which utilizes not only LASSO for predictor selection, but a hierarchical Bayesian method to determine whether constant coefficient (CC) or time-varying parameter (TVP) predictive regression should be employed in each out-of-sample forecasting step. This newly developed method has the advantages of both model shrinkage and automatic switch between CC and TVP forecasting models; thus, this may produce more accurate predictions of crude oil prices. The empirical results show that this hierarchical shrinkage model can outperform many commonly used forecasting benchmark methods, such as AR, unobserved components stochastic volatility (UCSV), and multivariate regression models in forecasting crude oil price on various forecasting horizons.http://dx.doi.org/10.1155/2020/6640180 |
| spellingShingle | Yuntong Liu Yu Wei Yi Liu Wenjuan Li Forecasting Oil Price by Hierarchical Shrinkage in Dynamic Parameter Models Discrete Dynamics in Nature and Society |
| title | Forecasting Oil Price by Hierarchical Shrinkage in Dynamic Parameter Models |
| title_full | Forecasting Oil Price by Hierarchical Shrinkage in Dynamic Parameter Models |
| title_fullStr | Forecasting Oil Price by Hierarchical Shrinkage in Dynamic Parameter Models |
| title_full_unstemmed | Forecasting Oil Price by Hierarchical Shrinkage in Dynamic Parameter Models |
| title_short | Forecasting Oil Price by Hierarchical Shrinkage in Dynamic Parameter Models |
| title_sort | forecasting oil price by hierarchical shrinkage in dynamic parameter models |
| url | http://dx.doi.org/10.1155/2020/6640180 |
| work_keys_str_mv | AT yuntongliu forecastingoilpricebyhierarchicalshrinkageindynamicparametermodels AT yuwei forecastingoilpricebyhierarchicalshrinkageindynamicparametermodels AT yiliu forecastingoilpricebyhierarchicalshrinkageindynamicparametermodels AT wenjuanli forecastingoilpricebyhierarchicalshrinkageindynamicparametermodels |