Eric Hillebrand

Modeling, Forecasting, and Nowcasting U.S. CO2 Emissions Using Many Macroeconomic Predictors

Publikation: Working paperForskning


  • rp19_21

    Forlagets udgivne version, 810 KB, PDF-dokument

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 sectors. 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. CO2 emissions in 2019 compared to 2018 as the result of a reduction in industrial production in residential utilities.
Antal sider38
StatusUdgivet - 27 nov. 2019
SerietitelCREATES Research Papers


  • CO2 emissions, Macroeconomic variables, Dynamic factor model, Variable selection, Forecasting, Nowcasting

Se relationer på Aarhus Universitet Citationsformater


Ingen data tilgængelig

ID: 172617566