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

Mikkel Bennedsen (), Eric Hillebrand () and Siem Jan Koopman
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Mikkel Bennedsen: Aarhus University and CREATES, Postal: Department of Economics and Business Economics, Aarhus University and CREATES, Fuglesangs Allé 4, building 2621, 9, 8210 Aarhus V, Denmark

CREATES Research Papers from Department of Economics and Business Economics, Aarhus University

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 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.

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)
Pages: 38
Date: 2019-11-27
New Economics Papers: this item is included in nep-ene, nep-env, nep-ets, nep-for and nep-ore
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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Journal Article: Modeling, forecasting, and nowcasting U.S. CO2 emissions using many macroeconomic predictors (2021)
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Persistent link: https://EconPapers.repec.org/RePEc:aah:create:2019-21

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