Modeling, forecasting, and nowcasting U.S. CO2 emissions using many macroeconomic predictors

We propose a structural augmented dynamic factor model for U.S. CO2 emissions. Variable selection techniques applied to a large set of annual macroeconomic time series indicate that CO2 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 CO2 emissions in 2019 compared to 2018 as the result of a reduction in industrial production in residential utilities. •Structural augmented dynamic factor model for relation of U.S. CO2 emissions and large macroeconomic data set.•Variable selection for contemporaneous explanation of emissions favors Industrial Production (IP) and Industrial Production: Residential Utilities indices.•Factors from a large data set have predictive power for IP indices.•Model has good in-sample fit, forecast and nowcast power.