Nowcasting industrial production using linear and non-linear models of electricity demand
提出利用电力市场数据的线性与非线性模型（包括马尔可夫转换模型）来即时预测工业生产，发现该模型在经济动荡时期（如新冠疫情）显著提升预测效果，尤其能识别两种波动状态。
This article proposes different modelling approaches which exploit electricity market data to nowcast industrial production. Our models include linear, mixed-data sampling (MIDAS), Markov-Switching (MS) and MS-MIDAS regressions. Comparisons against autoregressive approaches and other commonly used macroeconomic predictors show that electricity market data combined with an MS model significantly improve nowcasting performance, especially during turbulent economic states, such as those generated by the recent COVID-19 pandemic. The most promising results are provided by an MS model which identifies two volatility regimes. These results confirm that electricity market data provide timely and easy-to-access information for nowcasting macroeconomic variables, especially when it is most valuable, i.e. during times of crisis and uncertainty.