Model averaging based on leave-subject-out cross-validation for vector autoregressions
The vector autoregressive (VAR) model is a useful tool for economic evaluation and prediction. This paper develops a leave-subject-out cross-validation model averaging (LsoMA) method to average predictions from VAR models. The approximate unbiasedness of LsoMA and its asymptotic optimality in terms...
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| Published in | Journal of econometrics Vol. 209; no. 1; pp. 35 - 60 |
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| Main Authors | , , , |
| Format | Journal Article |
| Language | English |
| Published |
Amsterdam
Elsevier B.V
01.03.2019
Elsevier Sequoia S.A |
| Subjects | |
| Online Access | Get full text |
| ISSN | 0304-4076 1872-6895 |
| DOI | 10.1016/j.jeconom.2018.10.007 |
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| Abstract | The vector autoregressive (VAR) model is a useful tool for economic evaluation and prediction. This paper develops a leave-subject-out cross-validation model averaging (LsoMA) method to average predictions from VAR models. The approximate unbiasedness of LsoMA and its asymptotic optimality in terms of obtaining the lowest possible quadratic errors are established. The rate of the LsoMA based weights converging to the optimal weights minimizing the expected quadratic errors is also derived. Simulation experiments show that our method is generally more efficient than the other frequently used model selection and averaging methods. Two empirical applications further illustrate that the proposed method is promising. |
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| AbstractList | The vector autoregressive (VAR) model is a useful tool for economic evaluation and prediction. This paper develops a leave-subject-out cross-validation model averaging (LsoMA) method to average predictions from VAR models. The approximate unbiasedness of LsoMA and its asymptotic optimality in terms of obtaining the lowest possible quadratic errors are established. The rate of the LsoMA based weights converging to the optimal weights minimizing the expected quadratic errors is also derived. Simulation experiments show that our method is generally more efficient than the other frequently used model selection and averaging methods. Two empirical applications further illustrate that the proposed method is promising. |
| Author | Zhang, Xinyu Liao, Jun Zou, Guohua Zong, Xianpeng |
| Author_xml | – sequence: 1 givenname: Jun surname: Liao fullname: Liao, Jun organization: School of Mathematical Sciences, Capital Normal University, Beijing 100048, China – sequence: 2 givenname: Xianpeng surname: Zong fullname: Zong, Xianpeng organization: School of Mathematical Sciences, Capital Normal University, Beijing 100048, China – sequence: 3 givenname: Xinyu surname: Zhang fullname: Zhang, Xinyu organization: Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing 100190, China – sequence: 4 givenname: Guohua surname: Zou fullname: Zou, Guohua email: ghzou@amss.ac.cn organization: School of Mathematical Sciences, Capital Normal University, Beijing 100048, China |
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| Keywords | C53 C52 Model averaging Consistency Asymptotic optimality Vector autoregressions Leave-subject-out cross-validation |
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| Snippet | The vector autoregressive (VAR) model is a useful tool for economic evaluation and prediction. This paper develops a leave-subject-out cross-validation model... |
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| SubjectTerms | Algebra Asymptotic optimality Averaging Consistency Cost analysis econometric models Econometrics economic analysis economic evaluation economic theory Leave-subject-out cross-validation Model averaging prediction Regression analysis Simulation Validity Vector autoregressions |
| Title | Model averaging based on leave-subject-out cross-validation for vector autoregressions |
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