Forecasting with Bayesian vector autoregressive models: comparison of direct and iterated multistep methods
Forecasting with Bayesian vector autoregressive models: comparison of direct and iterated multistep methods
复制标题
使用贝叶斯向量自回归模型进行预测：直接法和迭代多步法的比较
DOI：
10.1108/ajeb-04-2022-0044
复制
发表时间：
2022
影响因子：
--
通讯作者：
Sugita Katsuhiro
中科院分区：
文献类型：
--
作者：
Sugita Katsuhiro
本文采用贝叶斯向量自回归模型（Bayesian Vector Autoregressive，VAR），分别采用独立的Normal-Wishart先验、Minnesota先验和随机搜索变量选择（Stochastic Search Variable Selection，SSVS）三种不同的先验，通过直接和迭代两种方法比较多期预测性能。蒙特卡洛模拟进行比较预测性能。一个实证研究，使用美国宏观经济数据显示作为一个illustrations.FindingsIn理论上直接预测更有效的渐近和更强大的模型误指定比迭代预测，迭代预测往往偏向，但更有效，如果一个时期的未来模型是正确的指定。从蒙特卡罗模拟的结果来看，迭代预测往往优于直接预测，特别是采用较长滞后模型和较长预测期的预测。实施SSVS先验一般提高预测性能，无论是非平稳或平稳data.Originality/value-the无限制的VAR模型，本文发现，迭代预测使用模型与SSVS先验一般最好跑赢大盘，这表明，SSVS对无关紧要的参数的限制，简化了一步预测VAR的过参数化问题，从而提供了一个明显的改善迭代预测的预测性能。
PurposeThe paper compares multi-period forecasting performances by direct and iterated method using Bayesian vector autoregressive (VAR) models.Design/methodology/approachThe paper adopts Bayesian VAR models with three different priors – independent Normal-Wishart prior, the Minnesota prior and the stochastic search variable selection (SSVS). Monte Carlo simulations are conducted to compare forecasting performances. An empirical study using US macroeconomic data are shown as an illustration.FindingsIn theory direct forecasts are more efficient asymptotically and more robust to model misspecification than iterated forecasts, and iterated forecasts tend to bias but more efficient if the one-period ahead model is correctly specified. From the results of the Monte Carlo simulations, iterated forecasts tend to outperform direct forecasts, particularly with longer lag model and with longer forecast horizons. Implementing SSVS prior generally improves forecasting performance over unrestricted VAR model for either nonstationary or stationary data.Originality/valueThe paper finds that iterated forecasts using model with the SSVS prior generally best outperform, suggesting that the SSVS restrictions on insignificant parameters alleviates over-parameterized problem of VAR in one-step ahead forecast and thus offers an appreciable improvement in forecast performance of iterated forecasts.
用于预测的多步估计
DOI：
--
发表时间：
2009
期刊：
影响因子：
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作者：
Michael P. Clements;D. Hendry
通讯作者：
D. Hendry
影响因子：
1.4
作者：
E. George;R. McCulloch
通讯作者：
E. George;R. McCulloch