Model Averaging Estimation Method by Kullback–Leibler Divergence for Multiplicative Error Model
In this paper, we propose the model averaging estimation method for multiplicative error model and construct the corresponding weight choosing criterion based on the Kullback–Leibler divergence with a hyperparameter to avoid the problem of overfitting. The resulting model average estimator is proved...
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| Format: | Article |
| Language: | English |
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Wiley
2022-01-01
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| Series: | Complexity |
| Online Access: | http://dx.doi.org/10.1155/2022/7706992 |
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| author | Wanbo Lu Wenhui Shi |
| author_facet | Wanbo Lu Wenhui Shi |
| author_sort | Wanbo Lu |
| collection | DOAJ |
| description | In this paper, we propose the model averaging estimation method for multiplicative error model and construct the corresponding weight choosing criterion based on the Kullback–Leibler divergence with a hyperparameter to avoid the problem of overfitting. The resulting model average estimator is proved to be asymptotically optimal. It is shown that the Kullback–Leibler model averaging (KLMA) estimator asymptotically minimizes the in-sample Kullback–Leibler divergence and improves the forecast accuracy of out-of-sample even under different loss functions. In simulations, we show that the KLMA estimator compares favorably with smooth-AIC estimator (SAIC), smooth-BIC estimator (SBIC), and Mallows model averaging estimator (MMA), especially when some nonlinear noise is added to the data generation process. The empirical applications in the daily range of S&P500 and price duration of IBM show that the out-of-sample forecasting capacity of the KLMA estimator is better than that of other methods. |
| format | Article |
| id | doaj-art-9ac79e565cfd477aa2b9e3a3b75c0870 |
| institution | Kabale University |
| issn | 1099-0526 |
| language | English |
| publishDate | 2022-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Complexity |
| spelling | doaj-art-9ac79e565cfd477aa2b9e3a3b75c08702025-02-03T05:53:39ZengWileyComplexity1099-05262022-01-01202210.1155/2022/7706992Model Averaging Estimation Method by Kullback–Leibler Divergence for Multiplicative Error ModelWanbo Lu0Wenhui Shi1School of StatisticsSchool of StatisticsIn this paper, we propose the model averaging estimation method for multiplicative error model and construct the corresponding weight choosing criterion based on the Kullback–Leibler divergence with a hyperparameter to avoid the problem of overfitting. The resulting model average estimator is proved to be asymptotically optimal. It is shown that the Kullback–Leibler model averaging (KLMA) estimator asymptotically minimizes the in-sample Kullback–Leibler divergence and improves the forecast accuracy of out-of-sample even under different loss functions. In simulations, we show that the KLMA estimator compares favorably with smooth-AIC estimator (SAIC), smooth-BIC estimator (SBIC), and Mallows model averaging estimator (MMA), especially when some nonlinear noise is added to the data generation process. The empirical applications in the daily range of S&P500 and price duration of IBM show that the out-of-sample forecasting capacity of the KLMA estimator is better than that of other methods.http://dx.doi.org/10.1155/2022/7706992 |
| spellingShingle | Wanbo Lu Wenhui Shi Model Averaging Estimation Method by Kullback–Leibler Divergence for Multiplicative Error Model Complexity |
| title | Model Averaging Estimation Method by Kullback–Leibler Divergence for Multiplicative Error Model |
| title_full | Model Averaging Estimation Method by Kullback–Leibler Divergence for Multiplicative Error Model |
| title_fullStr | Model Averaging Estimation Method by Kullback–Leibler Divergence for Multiplicative Error Model |
| title_full_unstemmed | Model Averaging Estimation Method by Kullback–Leibler Divergence for Multiplicative Error Model |
| title_short | Model Averaging Estimation Method by Kullback–Leibler Divergence for Multiplicative Error Model |
| title_sort | model averaging estimation method by kullback leibler divergence for multiplicative error model |
| url | http://dx.doi.org/10.1155/2022/7706992 |
| work_keys_str_mv | AT wanbolu modelaveragingestimationmethodbykullbackleiblerdivergenceformultiplicativeerrormodel AT wenhuishi modelaveragingestimationmethodbykullbackleiblerdivergenceformultiplicativeerrormodel |