多元宏观时间序列的拟合及预测—基于VAR模型和状态空间模型

Abstract

预测是一直以来关注的问题，尤其在宏观经济方面。单变量时间序列的预测已不能满足基本的需要，多元宏观经济时间序列对拟合合理的模型需求迫切，当下AR模型和VAR模型发展较为完善，在一定程度上用于宏观领域分析及政策分析。状态空间模型在验证可观测变量的同时，加入不可观测变量，在经济开放且发展迅速的前提下更能适应实际的需要。本文选取三个宏观经济中三个方面(工业，货币供给和CPI)的基本变量，拟合VAR模型和状态空间模型并进行预测，比较预测效果。结果表明，状态空间模型的预测精度要优于VAR模型。 Predictions have been concerned about the issue, especially in the macroeconomic. Univariate time series prediction can not meet basic needs. Multiple macroeconomic time series has urgent demand for reasonable model. Currently AR model and VAR model develop better, and to some extent, are used for analysis and policy analysis in macro fields. While state space model validates observable variables, unobserved variables are added. In an open economy and the rapid development background, state-space model can adapt to the actual needs. This paper selects the three basic macroeconomic variables in three areas (industrial, money supply and CPI), fitting VAR model and state space model and predicting, comparing predictions. The results show that the prediction accuracy of the state space model is superior to the VAR model.