摘要
利用SVAR方法揭示了投资冲击对劳动就业的动态影响以及劳动就业的动态特征,从而确立了相关经验事实。在动态一般均衡框架内构建了包含多种结构特征的新凯恩斯主义宏观模型,并用贝叶斯方法估计了模型的结构参数,进而对不同结构模型的动态特征进行了详细的比较研究。结果显示,刚性价格模型不能解释劳动就业的相关经验事实,而刚性工资模型和刚性价格-工资模型对经验特征具有良好的解释能力。同时,还就投资冲击对劳动就业波动的解释力和历史贡献进行了评估,方差分解的结果说明投资冲击对劳动就业波动具有很强解释力,历史分解和反事实模拟的结果显示投资冲击对2005年后劳动就业的繁荣有显著拉动作用。需要强调的是,如果没有正向投资冲击的拉动,2008年金融危机所导致的就业衰退要比实际情形严重得多。这与中国政府在2008年底推出的系列投资项目有关,这些投资过程所伴随的投资冲击有效地改善了这一阶段的劳动就业条件。
In this paper,we have investigated the effect of investment shocks on the labor employment dynamics and the characteristics of labor employment through SVAR,by doing so,we have established the related stylized facts.then construct a New-Keynesian macroeconomic model which embeds several kinds of micro-structures,and the structural parameters are estimated with Bayesian methods.Based on the parameterized model,we have done a detailed analysis of the different model dynamics.The analysis shows that rigid price model and flexible price-wage model can not explain the related stylized facts,but rigid wage model and rigid price-wage model have the potential in successfully explaining these facts.Furthermore,we have evaluated the explanation ability and historical contributions of investment shocks with respect to the labor employment dynamics.The variance decomposition shows that investment shocks can at least explain thirty percent fluctuations of labor employment.The historical decomposition and counterfactual simulation show that investment shocks have positive effect on the prosperous labor employment after 2005,in particular,and if there have no the positive effect of investment shocks,the decline of labor employment during the globally financial crisis in 2008 will have been more serious than it has occurred.We believe that this positive effect has something to do with the investment stimulus measures adopted by Chinese government to avoid a deep crisis.
出处
《南开经济研究》
CSSCI
北大核心
2011年第6期66-93,共28页
Nankai Economic Studies
关键词
投资冲击
劳动就业
新凯恩斯主义模型
反事实模拟
Investment Shocks
Labor Employment
New-Keynesian Models
Counterfactual Simulation
