基于动态随机一般均衡模型的中国经济波动数量分析
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摘要
我国自改革开放以来,经济在高速增长的同时也经历了较大幅度的波动。GDP增长速度有时高达15%,而有时却低至不足4%,相差超过11个百分点。而同时,我国通货膨胀的波动还更大。面对如此幅度的经济波动,一个自然的问题是:什么原因造成了我国的经济波动?这正是本文研究的主要课题。
当今宏观经济学认为经济波动是由外生的随机冲击所驱动的。弄清冲击的来源,是了解经济波动机制的关键一步。目前,通常被认为是经济波动驱动力的冲击有技术冲击、货币政策冲击、偏好冲击、及成本冲击等数种。但究竟哪几种冲击在经济波动的过程中更为重要,不同的理论有着不同的看法。本文的研究从冲击的分解入手,来研究我国经济波动的机制。在总结了我国经济波动的典型事实后,本文构建了两个动态随机一般均衡模型作为分析的工具。利用贝叶斯方法估计了模型参数后,又通过卡尔曼平滑算法估计了模型中各个外生冲击的实现值。将估计得到的各个冲击分别带回模型进行模拟,可以研究在各冲击单独作用下,模型中各个内生变量的波动情况。这样,在模型的帮助下我们分解了各个冲击对经济波动的影响,进而找出了其中比较重要的冲击,最终帮助我们加深了对中国经济波动机制的理解。
通过以上的数量分析,本文得到了如下主要结论:第一,造成我国经济波动的主要因素是技术进步和制度变化。这二者的波动在最近十几年随着我国经济的发展而逐步趋缓,造成了我国经济波动幅度在最近十几年的缓和。第二,我国通货膨胀的波动主要是由货币政策的变化造成的。第三,政府消费与净出口对我国经济波动的影响不大。
与现有的研究相比,本文的工作在以下几点上做出了一定的创新:第一,本文在动态随机一般均衡模型的框架下,首次完成了对我国经济波动冲击来源的分解,找到了影响我国经济波动的主要因素。第二,在建模中,本文在模型中引入了基于货币总量调控的货币政策规则、以及货币流通速度变化这两大因素,使构建的模型相比现有模型更为贴近我国现实。第三,本文首次注意并确认了我国经济在1992年间发生的结构突变,并在数量分析中对这一因素进行了处理,避免了这一结构变化给估计结果可能带来的偏差。以上的创新使本文得到了一系列有意义的新结论。除此而外,本文系统的总结了当前最前沿的基于DSGE模型的数量分析方法,并完整的通过计算机编程加以了实现,这在国内的研究中还属首次。
Since 1978, when the reform and opening up started, China has been experiencing wild business cycle fluctuations as well as high growth speed. In some times, China's GDP growth reached 15 percent. But there were also some times GDP growth slowed to only 4 percent. Meanwhile, fluctuations in inflation saw even bigger volatility. Facing such big economic fluctuations, we may ask: What caused them? To answer this question is the main task of this research.
In nowadays, it is widely believed by macro economists that business fluctuations are driven by various exogenous stochastic shocks including technology shock, monetary policy shock, preference shock, cost shock, etc. However, economists from different macroeconomic schools have different opinions on the relative importance of different shocks. Our research started from the decomposition of quantitative impacts of different shocks. After the summarization of stylized facts of China's business fluctuations, two dynamic stochastic general equilibrium (DSGE for short hence force) models were established. Parameter values in these models were estimated with Bayesian method. Realizations of different shocks were also obtained with Kalman smoothing algorithm. Substituting each estimated shock back into the model and running simulations yield the dynamics of endogenous variables under the influence of a single shock. Hence, the quantitative impact of each shock on the whole economy could be studied. Quantitatively important shocks could also be identified.
The following main conclusions could be drawn from the quantitative analysis: First, technology advances and institutional changes were the main drivers of China's business fluctuations. The fluctuations of these two factors themselves moderated in the past several years, which leaded to the moderation of output fluctuations. Second, the fluctuations in inflation were mainly caused by monetary polices. Third, government consumption and net exports had only ignorable impacts on business fluctuations.
Our work sheds some new lights on the research of China's business fluctuations for the following reasons: First, quantitative impacts of different shocks were decomposed with DSGE models for the first time. The main driving forces of China's business fluctuations were also identified. Second, monetary policy implementation based on the control of monetary aggregate and varying circulation velocity of money were both introduced into a DSGE model for the first time, which enhanced the external consistency of the constructed model. Third, the structural break occurred around 1992 was identified for the first time. The possible bias brought by it was carefully removed in the quantitative analysis. In addition to these three breakthroughs, contributions of our research can also be identified from a methodological perspective. State of art quantitative analyzing methods based on DSGE models were summarized and, more importantly, implemented with computer programs in our research, which will facilitate the quantitative research of other researchers.
当今宏观经济学认为经济波动是由外生的随机冲击所驱动的。弄清冲击的来源,是了解经济波动机制的关键一步。目前,通常被认为是经济波动驱动力的冲击有技术冲击、货币政策冲击、偏好冲击、及成本冲击等数种。但究竟哪几种冲击在经济波动的过程中更为重要,不同的理论有着不同的看法。本文的研究从冲击的分解入手,来研究我国经济波动的机制。在总结了我国经济波动的典型事实后,本文构建了两个动态随机一般均衡模型作为分析的工具。利用贝叶斯方法估计了模型参数后,又通过卡尔曼平滑算法估计了模型中各个外生冲击的实现值。将估计得到的各个冲击分别带回模型进行模拟,可以研究在各冲击单独作用下,模型中各个内生变量的波动情况。这样,在模型的帮助下我们分解了各个冲击对经济波动的影响,进而找出了其中比较重要的冲击,最终帮助我们加深了对中国经济波动机制的理解。
通过以上的数量分析,本文得到了如下主要结论:第一,造成我国经济波动的主要因素是技术进步和制度变化。这二者的波动在最近十几年随着我国经济的发展而逐步趋缓,造成了我国经济波动幅度在最近十几年的缓和。第二,我国通货膨胀的波动主要是由货币政策的变化造成的。第三,政府消费与净出口对我国经济波动的影响不大。
与现有的研究相比,本文的工作在以下几点上做出了一定的创新:第一,本文在动态随机一般均衡模型的框架下,首次完成了对我国经济波动冲击来源的分解,找到了影响我国经济波动的主要因素。第二,在建模中,本文在模型中引入了基于货币总量调控的货币政策规则、以及货币流通速度变化这两大因素,使构建的模型相比现有模型更为贴近我国现实。第三,本文首次注意并确认了我国经济在1992年间发生的结构突变,并在数量分析中对这一因素进行了处理,避免了这一结构变化给估计结果可能带来的偏差。以上的创新使本文得到了一系列有意义的新结论。除此而外,本文系统的总结了当前最前沿的基于DSGE模型的数量分析方法,并完整的通过计算机编程加以了实现,这在国内的研究中还属首次。
Since 1978, when the reform and opening up started, China has been experiencing wild business cycle fluctuations as well as high growth speed. In some times, China's GDP growth reached 15 percent. But there were also some times GDP growth slowed to only 4 percent. Meanwhile, fluctuations in inflation saw even bigger volatility. Facing such big economic fluctuations, we may ask: What caused them? To answer this question is the main task of this research.
In nowadays, it is widely believed by macro economists that business fluctuations are driven by various exogenous stochastic shocks including technology shock, monetary policy shock, preference shock, cost shock, etc. However, economists from different macroeconomic schools have different opinions on the relative importance of different shocks. Our research started from the decomposition of quantitative impacts of different shocks. After the summarization of stylized facts of China's business fluctuations, two dynamic stochastic general equilibrium (DSGE for short hence force) models were established. Parameter values in these models were estimated with Bayesian method. Realizations of different shocks were also obtained with Kalman smoothing algorithm. Substituting each estimated shock back into the model and running simulations yield the dynamics of endogenous variables under the influence of a single shock. Hence, the quantitative impact of each shock on the whole economy could be studied. Quantitatively important shocks could also be identified.
The following main conclusions could be drawn from the quantitative analysis: First, technology advances and institutional changes were the main drivers of China's business fluctuations. The fluctuations of these two factors themselves moderated in the past several years, which leaded to the moderation of output fluctuations. Second, the fluctuations in inflation were mainly caused by monetary polices. Third, government consumption and net exports had only ignorable impacts on business fluctuations.
Our work sheds some new lights on the research of China's business fluctuations for the following reasons: First, quantitative impacts of different shocks were decomposed with DSGE models for the first time. The main driving forces of China's business fluctuations were also identified. Second, monetary policy implementation based on the control of monetary aggregate and varying circulation velocity of money were both introduced into a DSGE model for the first time, which enhanced the external consistency of the constructed model. Third, the structural break occurred around 1992 was identified for the first time. The possible bias brought by it was carefully removed in the quantitative analysis. In addition to these three breakthroughs, contributions of our research can also be identified from a methodological perspective. State of art quantitative analyzing methods based on DSGE models were summarized and, more importantly, implemented with computer programs in our research, which will facilitate the quantitative research of other researchers.
引文
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