题目：
Forecasting the Chinese Macroeconomy Based on a Large Factor Model with Monthly and Quarterly Data
主讲人：张秦 国际商学院助理教授
澳大利亚麦考瑞大学博士，浙江工商大学国际商学院助理教授。主要研究领域为应用宏观经济学和应用计量经济学等，在Empirical Economics等SSCI期刊发表多篇论文。
时间：2021年6月30日（周三）15:00-16:00
地点：综合楼741
摘要：
This paper considers forecasting Chinese macroeconomic variables using large-scale factor models with mixed-frequency data and missing observations component. The factor models are particular compatible with potential data contamination, rapid institutional and structural change, which are prevalent in China. We perform a horse-race among a large set of traditional models and large dimensional approximate factor models. We forecast two measures of inflation and three measures of real activity, with forecast horizons ranging from 1 to 12 months. Using 251 monthly variables and 34 quarterly variables over the December 2001 to June 2018 period, we find statistical evidence that mixed-frequency factor models, especially mixed-frequency factor-augmented vector autoregressive models, generated superior forecasts to the univariate and multi-variate models for price series, nominal investment, and nominal consumption, except for the CPI inflation rate and nominal consumption at one month ahead. In the global financial crisis period, full-panel factor models perform well for price series and railway cargo at 6-, 9- and 12-month-ahead but for investment and consumption only at 1-month-ahead. Therefore, the results of this paper provide clear guidance and important implications for academics, practitioners and the public who are interested in macroeconomic forecasting in China.