Modeling, forecasting, and nowcasting U.S. CO2 emissions using many macroeconomic predictors
提出一个结构增强动态因子模型，利用大量宏观经济时间序列数据预测美国二氧化碳排放，发现工业生产指数是最佳解释变量，并即时预测2019年人均排放下降约2.6%。
We propose a structural augmented dynamic factor model for U.S. CO 2 emissions. Variable selection techniques applied to a large set of annual macroeconomic time series indicate that CO 2 emissions are best explained by industrial production indices covering manufacturing and residential utilities. We employ a dynamic factor structure to explain, forecast, and nowcast the industrial production indices and thus, by way of the structural equation, emissions. We show that our model has good in-sample properties and out-of-sample performance in comparison with univariate and multivariate competitor models. Based on data through September 2019, our model nowcasts a reduction of about 2.6% in U.S. per capita CO 2 emissions in 2019 compared to 2018 as the result of a reduction in industrial production in residential utilities.