混频数据、投资冲击与中国宏观经济波动

Mixed-frequency Data,Investment Shocks and Business Cycles in China

使用中国数据估计DSGE模型时,由于缺乏季度的支出法消费、投资数据,一般使用月度的社会消费品零售额、固定资产投资数据加总作为替代。本文利用这一数据对DSGE模型进行贝叶斯估计,发现参数估计结果会出现系统性偏差;而使用年度的支出法消费、投资数据进行估计,模型的样本外预测绩效总体更优。基于这一结果,将年度频率的支出法投资、消费数据与季度频率的GDP、货币、通胀数据相结合,同时又结合投资品价格的年度(1992—1997年)、半年度(1998—2002年)和季度(2003—2016年)数据,对模型重新估计并进行方差分解。结果表明:中国产出波动的最主要解释因素是与投资相关的冲击,其次为货币政策冲击、持久性技术冲击和外生需求冲击。

This paper shows that the retail sales of consumer goods （RSCG） and fixed-asset investment （FAI） usually used in the estimation of Chinese dynamic stochastic general equilibrium （DSGE） models contain misleading information, and suggests replacing them with annual household consumption and fixed capital formation in gross domestic product （GDP） using the expenditure approach. Due to a lack of quarterly data, the usual practice in the estimation of Chinese DSGE models is aggregating monthly RSCG and FAI data to obtain quarterly data on consumption and investment. However, there are important differences between these series and the corresponding model variables by definition. The inconsistency between model variables and observed data may lead to incorrect results in parameter estimation. Chang et al. （2016） interpolate annual household consumption expenditure and fixed capital formation in expenditure GDP using RSCG and FAI as interpolaters to obtain usable quarterly data. However, the seasonal information of the interpolated series retained from RSCG and FAI may be misleading. This paper instead proposes the usage of mixed-frequency data in a DSGE model estimation, that is, replacing the quarterly data of RSCG and FAI with the annual data of household consumption expenditure and fixed capital formation, and combining annual data with quarterly data. The solution to the DSGE model can be cast in state-space form and estimated via the Kalman filter. The low frequency series are considered high frequency series with missing observations. Thus, the Kalman filter implemented in Dynare can deal with missing va｜ues easily. Apart from consumption and investment, the time series used for the model estimation include GDP, GDP deflator and money aggregates （M2）. Based on the Bayesian technique, this paper estimates a DSGE model using mixed-and single-frequency （quarterly） data, respectively. Comparing the estimation results, there are significant discrepancies between the mixed-and single- t^equeney estimates. In particular, compared with single-frequency estimates, the investment adjustment costs parameter of /he mixed-frequency estimates is larger, while the standard deviation of permanent technology shocks is lower, indicating a smaller role fnr permanent tecbnology shocks in driving business cycles. To assess the relative performance of ahernative datasets, this paper considers the out-of-sample foreeasting performance of the DSGE model. The predicted variables include GDP and inflation. The model is re-estimated every quarter and forecast fi^rward for eight periods. The RMSE indicators are calculated from the predictive value and actual data. Based on the RMSE for GDP, the model estimated with mixed-frequency data outperforms that of quarterly data. For inflation, the model estimated with mixed-frequency data performs better in the long term but worse in the short term. This paper estimates the CEE/SW model using data sampled at different frequencies. The variance decomposition shows that the main factors explaining China＇s output volatility are investment shocks, followed by monetary shocks, persistent teehnology shocks and external demand shocks. These four factors can explain more than 80% of the volatility of GDP, while the investment shocks alone can account for more than 30% of GDP variability. The contribution of investment shocks comes mainly from investment-specific technology shocks, measured by the relative price of investment. This result differs from that of Justiniano et al. （2010, 2011 ） , who find that shocks to marginal investment efficiency are the key drivers of business cycle fluctuations in U.S. output. However, like their analysis, the theoretical analysis presented here shows that investment shocks may be interpreted as a proxy for the overall health of the financial system.