Forecasting Markov switching vector autoregressions: Evidence from simulation and application

IF 3.4 3区 经济学 Q1 ECONOMICS Journal of Forecasting Pub Date : 2024-08-15 DOI:10.1002/for.3180
Maddalena Cavicchioli
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Abstract

We derive the optimal forecasts for multivariate autoregressive time series processes subject to Markov switching in regime. Optimality means that the trace of the mean square forecast error matrix is minimized by using suitable weighting observations. Then we provide neat analytic expressions for the optimal weights in terms of the matrices involved in a state space representation of the considered process. Our matrix expressions in closed form improve computational performance since they are readily programmable. Numerical simulations and an empirical application illustrate the feasibility of the proposed approach. We provide evidence that the forecasts using optimal weights increase forecast precision and are more accurate than the traditional Markov switching alternatives.
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马尔科夫切换向量自回归预测:模拟和应用证据
我们推导出了受马尔科夫体制转换影响的多元自回归时间序列过程的最优预测。最优性意味着通过使用合适的加权观测值,可使均方预测误差矩阵的迹最小化。然后,我们根据所考虑过程的状态空间表示所涉及的矩阵,提供了最佳权重的简明解析表达式。我们的矩阵表达式采用闭合形式,易于编程,从而提高了计算性能。数值模拟和经验应用说明了所提方法的可行性。我们提供的证据表明,使用最优权重的预测提高了预测精度,而且比传统的马尔可夫切换方法更准确。
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来源期刊
CiteScore
5.40
自引率
5.90%
发文量
91
期刊介绍: The Journal of Forecasting is an international journal that publishes refereed papers on forecasting. It is multidisciplinary, welcoming papers dealing with any aspect of forecasting: theoretical, practical, computational and methodological. A broad interpretation of the topic is taken with approaches from various subject areas, such as statistics, economics, psychology, systems engineering and social sciences, all encouraged. Furthermore, the Journal welcomes a wide diversity of applications in such fields as business, government, technology and the environment. Of particular interest are papers dealing with modelling issues and the relationship of forecasting systems to decision-making processes.
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