宏观数据发布与经济周期实时测度方法研究

Abstract

为充分利用实时发布的最新数据来改善宏观经济分析的时效性,本文扩展了一种能够处理不规则数据的混频区制转移动态因子模型及其贝叶斯估计方法.数值模拟结; 果表明贝叶斯方法提高了模型估计的准确性,并发现含噪音成分低的指标,其新数据对实时测度的贡献更大.基于2008年以来的256组实时数据的研究结果表; 明,文中模型不仅较好刻画了1992年以来我国经济周期波动及阶段性变化,而且对GDP数据修订具有很好的稳健性,此外对经济周期拐点的实时识别则存在2; 至8个月的滞后.最后,在每月依序发布的指标中,进出口数据含有较高噪音成分,工业增加值和财政税收等数据对当月经济状况测度的更新修正幅度大且可靠性高; ,对于提高经济周期测度时效性具有重要价值.

To make better use of the new information from macroeconomic data; releasing in the realtime, this paper develops a mixed-frequency; Markov-switching dynamic factor model for an unbalanced datasets; together with its Bayesian estimation procedure. Simulation studies show; that the Bayesian method improves the estimation accuracy, and; indicators with less noise are more efficient in reducing the measure; errors in the real-time. Based on 256 real-time data sets collected on; the data releasing dates since 2008, it shows that our model well; characterizes China's business cycle since 1992, and its estimation is; robust and reliable with respect to GDP data revisions. In addition,; there may exist about 2 to 8 month delays in real-time dating business; cycle turning points. Furthermore, among all indicators released in turn; within every month, export and import total amount is released earlier; but with more noise, while the real-time revision impacts of indicators; like industrial product, fiscal taxation on the contemporary business; cycle fluctuation are substantial and reliable.