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

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Modeling, forecasting, and nowcasting U.S. CO2 emissions using many macroeconomic predictors. / Bennedsen, Mikkel; Hillebrand, Eric; Koopman, Siem Jan.

In: Energy Economics, Vol. 96, 105118, 04.2021.

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@article{d7c442d0278a413bb64823b27a734a69,
title = "Modeling, forecasting, and nowcasting U.S. CO2 emissions using many macroeconomic predictors",
abstract = "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. ",
keywords = "CO emissions, Dynamic factor model, Forecasting, Macroeconomic variables, Nowcasting, Variable selection",
author = "Mikkel Bennedsen and Eric Hillebrand and Koopman, {Siem Jan}",
year = "2021",
month = apr,
doi = "10.1016/j.eneco.2021.105118",
language = "English",
volume = "96",
journal = "Energy Economics",
issn = "0140-9883",
publisher = "Elsevier BV",

}

RIS

TY - JOUR

T1 - Modeling, forecasting, and nowcasting U.S. CO2 emissions using many macroeconomic predictors

AU - Bennedsen, Mikkel

AU - Hillebrand, Eric

AU - Koopman, Siem Jan

PY - 2021/4

Y1 - 2021/4

N2 - 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.

AB - 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.

KW - CO emissions

KW - Dynamic factor model

KW - Forecasting

KW - Macroeconomic variables

KW - Nowcasting

KW - Variable selection

UR - http://www.scopus.com/inward/record.url?scp=85101170880&partnerID=8YFLogxK

U2 - 10.1016/j.eneco.2021.105118

DO - 10.1016/j.eneco.2021.105118

M3 - Journal article

VL - 96

JO - Energy Economics

JF - Energy Economics

SN - 0140-9883

M1 - 105118

ER -