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平滑结构变化下 FAVAR 模型的时变模型平均AI 解读

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…

平滑结构变化下因子增强回归的时变预测组合AI 解读

This study proposes a time-varying forecast combination for factor-augmented (TVFCFA) regressions with smooth structural changes. First, we establish the limiting distribution of the estimators of the time-varying factor-augmented regressions. To estimate the optimal time-varying combination weights, we propose a local leave-l-out cross-validation (LLOCV) criterion that is asymptotically unbiased for the local mean squared forecast error (LMSFE). The TVFCFA method was shown…