Time Series Forecasting Using a Markov Switching Vector Autoregressive Model with Stochastic Search Variable Selection Method
Time Series Forecasting Using a Markov Switching Vector Autoregressive Model with Stochastic Search Variable Selection Method
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使用带有随机搜索变量选择方法的马尔可夫切换向量自回归模型进行时间序列预测
DOI：
10.1007/978-3-030-98689-6_10
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发表时间：
2022
期刊：
影响因子：
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通讯作者：
Sugita Katsuhiro
中科院分区：
文献类型：
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作者：
加藤真紀;太田知彩;Nobuhiro Nakamura;加藤真紀;Nobuhiro Nakamura and Kazuhiko Ohashi;加藤真紀;Maki KATO;Maki KATO;Sugita Katsuhiro
本文研究了基于随机搜索变量选择方法的马尔可夫切换向量自回归（MSVAR）模型的预测性能。MSVAR模型在实证宏观经济学中得到了广泛应用。然而，相对于线性VAR模型，MSVAR模型通常在样本内拟合得更好，但预测效果较差，并且通常具有大量参数，导致过度参数化问题。通过自动将无关紧要的参数设置为零，预先使用SSVS有望缓解这种过度参数化问题。在实证研究和蒙特卡罗模拟的递归预测练习中，我发现将SSVS应用于无限制VAR或MSVAR模型通常可以提高预测性能。然而，结果表明，与具有SSVS先验的线性VAR模型相比，具有SSVS先验的MSVAR模型的预测性能并不总是更好，而且改进的幅度并不大，不足以缓解MSVAR模型的过度参数化问题。
This paper investigates forecasting performance using a Markov switching vector autoregressive (MSVAR) model with stochastic search variable selection (SSVS) method. An MSVAR model has been widely used for empirical macroeconomics. However, an MSVAR model usually fits in-sample better but forecasts poorly relative to a linear VAR model, and typically has a large number of parameters, leading to over-parameterization problem. The use of SSVS prior is expected to mitigate this over-parameterization problem by setting insignificant parameters to be zero in an automatic fashion. In recursive forecasting exercises of empirical study and Monte Carlo simulation, I find that implementing SSVS to unrestricted VAR or MSVAR model typically improve forecasting performance. However, the results show that the MSVAR model with the SSVS prior does not always provide superior forecasting performance relative to the linear VAR model with SSVS prior, and that the improvement is not significantly large enough to alleviate over-parametrization problem of an MSVAR model.