大维变量选择、混频因子模型与新冠肺炎疫情冲击下的GDP现时预测

Variable Selection, Factor-MIDAS and GDP Nowcasting during Recession and Recovery Period of Covid-19

新冠肺炎疫情不仅对我国宏观经济造成了巨大冲击,也为准确预测我国宏观经济未来走势带来挑战。本文从新冠肺炎疫情冲击出发,将模型置信集检验与U-MIDAS模型组合,设计了一种在混频情形下利用预测变量的异质性波动从大维数据集中选取对GDP具有稳定预测效果变量的方法。通过利用选取出的稳定性变量构建多种形式的混频目标因子模型并与其他类型的混频因子模型对比,全面评估了不同模型在疫情前后对GDP进行高频现时预测的效果。研究发现,在疫情冲击前的平稳时期,利用覆盖范围较广的变量构建双因子MIDAS模型预测效果最优;利用稳定性变量构建的单因子U-MIDAS模型同样具有良好的预测效果。当经济从冲击中持续恢复时,利用部分稳定性变量构建的双因子U-MIDAS模型在捕捉到GDP的核心变化后率先对其连续做出准确的现时预测。经济稳定时,对预测变量设定较长的滞后阶数会提升预测效果;在冲击后的恢复期中则应减少滞后阶数,避免变量在冲击中出现的异常值对预测产生负面影响。本文也为当经济受到巨大外生冲击或处于冲击后的恢复期时其他宏观经济指标的预测提供了有价值的参考。

The large shock to China’s macroeconomy caused by Covid-19 crisis has imposed tremendous challenges on nowcasting China’s Gross Domestic Product(GDP) accurately. In this paper, from the perspective of the Covid-19 crisis, we combine Model Confidence Set and U-MIDAS to propose a new method to select variables in the setting of mixed frequency data. This method allows us to select variables with stable predictive performance when used to forecast GDP via exploiting variables’ heterogeneous response to the shock. Then, TFA-MIDAS models with different specifications are built with those stable variables and we compare these models with other types of(T)FA-MIDAS models in the aspect of accuracy when used to forecast GDP before and after the outbreak of the epidemic. Firstly, when macroeconomy ran smoothly before the epidemic, two-factor MIDAS models with variables covering a wide range of macroeconomy have excellent predictive power;single-factor U-MIDAS models with stable variables also perform well. Secondly, when the economy continued to recover from the shock, the two-factor U-MIDAS model with some certain stable variables can continuously nowcast GDP in a much more accurate way by capturing the core dynamics of GDP. Thirdly, setting long lag orders for the predictive variables when the economic situation is stable can improve the forecast performance;while during the recovery period, shorter lag orders are preferred in order to avoid the negative impact of those outliers that occurred in the crisis on forecasting. The method proposed in this paper aims to provide some enlightenment to macroeconomic forecasting during recession and recovery period of huge exogenous shocks.