Time-varying model averaging for Favar models with smooth structural changes
Bibliographic Data
| ID | 19420158 |
|---|---|
| Authors | Qitong Chen (0000-0002-8254-9886, Guangdong University of Foreign Studies), Yongmiao Hong (0000-0002-9711-9061, Center for Forecasting Science, Academy of Mathematics and Systems Science, Chinese Academy of Sciences), Haiqi Li (Hunan University, corresponding author), Xia Wang (0000-0002-5256-0611, Renmin University of China) |
| Year | 2026 |
| Pages | 1-34 |
| Publication date | 2026-05-18 |
| Peer Reviewed | Yes |
| Open Access | No |
| Type | ARTICLE |
| Venue | Journal of Business and Economic Statistics (JOURNAL) |
| Journal identifiers | ISSN: 0735-0015 • E-ISSN: 1537-2707 |
| Publisher | Informa UK Limited (PUBLISHER • GB) |
| DOI | 10.1080/07350015.2026.2674157 |
| OpenAlex | W7161594946 |
| Language | EN |
| References cited | 58 |
Factor-augmented vector autoregressive (FAVAR) models serve as a powerful tool for forecasting economic and financial variables, particularly in environments with a large number of predictors. In this study, we propose a time-varying model-averaging method for FAVAR (TVMA-FAVAR) that accommodates smooth structural changes. To estimate the optimal time-varying combination weights, we develop a local leave-one-out cross-validation (LLOCV) criterion that is asymptotically unbiased for the local mean squared error (LMSE), as it excludes a term irrelevant to the weight vector. We establish the asymptotic optimality of the TVMA-FAVAR method by demonstrating that its LMSE attains an infeasible lower bound. In addition, we derive the convergence rates of the selected weights and the TVMA-FAVAR estimator. Monte Carlo simulations show that the proposed method outperforms existing popular model averaging and selection approaches. An empirical application to forecasting U.S. macroeconomic variables further illustrates the superior predictive performance of the TVMA-FAVAR method
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| Citation velocity | historical |
|---|---|
| Highly cited | No |