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

Mikkel Bennedsen, Eric Hillebrand () and Siem Jan Koopman

Energy Economics, 2021, vol. 96, issue C

Abstract: 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.

Keywords: CO2 emissions; Macroeconomic variables; Dynamic factor model; Variable selection; Forecasting; Nowcasting (search for similar items in EconPapers)
JEL-codes: C01 C13 C32 C51 C52 C53 C55 C82 Q43 Q47 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (15)

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Working Paper: Modeling, Forecasting, and Nowcasting U.S. CO2 Emissions Using Many Macroeconomic Predictors (2019)
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Persistent link: https://EconPapers.repec.org/RePEc:eee:eneeco:v:96:y:2021:i:c:s0140988321000232

DOI: 10.1016/j.eneco.2021.105118

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