Robust Estimation and Forecasting of Climate Change Using Score-Driven Ice-Age Models
We use data on the following climate variables for the period of the last 798 thousand years: global ice volume (Icet), atmospheric carbon dioxide level (CO2,t), and Antarctic land surface temperature (Tempt). Those variables are cyclical and are driven by the following strongly exogenous orbital va...
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ftmdpi:oai:mdpi.com:/2225-1146/10/1/9/ 2023-08-20T04:02:11+02:00 Robust Estimation and Forecasting of Climate Change Using Score-Driven Ice-Age Models Szabolcs Blazsek Alvaro Escribano 2022-02-16 application/pdf https://doi.org/10.3390/econometrics10010009 EN eng Multidisciplinary Digital Publishing Institute https://dx.doi.org/10.3390/econometrics10010009 https://creativecommons.org/licenses/by/4.0/ Econometrics; Volume 10; Issue 1; Pages: 9 climate change ice-ages and inter-glacial periods global ice volume atmospheric CO 2 Antarctic land surface temperature dynamic conditional score generalized autoregressive score Text 2022 ftmdpi https://doi.org/10.3390/econometrics10010009 2023-08-01T04:10:28Z We use data on the following climate variables for the period of the last 798 thousand years: global ice volume (Icet), atmospheric carbon dioxide level (CO2,t), and Antarctic land surface temperature (Tempt). Those variables are cyclical and are driven by the following strongly exogenous orbital variables: eccentricity of the Earth’s orbit, obliquity, and precession of the equinox. We introduce score-driven ice-age models which use robust filters of the conditional mean and variance, generalizing the updating mechanism and solving the misspecification of a recent climate–econometric model (benchmark ice-age model). The score-driven models control for omitted exogenous variables and extreme events, using more general dynamic structures and heteroskedasticity. We find that the score-driven models improve the performance of the benchmark ice-age model. We provide out-of-sample forecasts of the climate variables for the last 100 thousand years. We show that during the last 10–15 thousand years of the forecasting period, for which humanity influenced the Earth’s climate, (i) the forecasts of Icet are above the observed Icet, (ii) the forecasts of CO2,t level are below the observed CO2,t, and (iii) the forecasts of Tempt are below the observed Tempt. The forecasts for the benchmark ice-age model are reinforced by the score-driven models. Text Antarc* Antarctic MDPI Open Access Publishing Antarctic Econometrics 10 1 9 |
institution |
Open Polar |
collection |
MDPI Open Access Publishing |
op_collection_id |
ftmdpi |
language |
English |
topic |
climate change ice-ages and inter-glacial periods global ice volume atmospheric CO 2 Antarctic land surface temperature dynamic conditional score generalized autoregressive score |
spellingShingle |
climate change ice-ages and inter-glacial periods global ice volume atmospheric CO 2 Antarctic land surface temperature dynamic conditional score generalized autoregressive score Szabolcs Blazsek Alvaro Escribano Robust Estimation and Forecasting of Climate Change Using Score-Driven Ice-Age Models |
topic_facet |
climate change ice-ages and inter-glacial periods global ice volume atmospheric CO 2 Antarctic land surface temperature dynamic conditional score generalized autoregressive score |
description |
We use data on the following climate variables for the period of the last 798 thousand years: global ice volume (Icet), atmospheric carbon dioxide level (CO2,t), and Antarctic land surface temperature (Tempt). Those variables are cyclical and are driven by the following strongly exogenous orbital variables: eccentricity of the Earth’s orbit, obliquity, and precession of the equinox. We introduce score-driven ice-age models which use robust filters of the conditional mean and variance, generalizing the updating mechanism and solving the misspecification of a recent climate–econometric model (benchmark ice-age model). The score-driven models control for omitted exogenous variables and extreme events, using more general dynamic structures and heteroskedasticity. We find that the score-driven models improve the performance of the benchmark ice-age model. We provide out-of-sample forecasts of the climate variables for the last 100 thousand years. We show that during the last 10–15 thousand years of the forecasting period, for which humanity influenced the Earth’s climate, (i) the forecasts of Icet are above the observed Icet, (ii) the forecasts of CO2,t level are below the observed CO2,t, and (iii) the forecasts of Tempt are below the observed Tempt. The forecasts for the benchmark ice-age model are reinforced by the score-driven models. |
format |
Text |
author |
Szabolcs Blazsek Alvaro Escribano |
author_facet |
Szabolcs Blazsek Alvaro Escribano |
author_sort |
Szabolcs Blazsek |
title |
Robust Estimation and Forecasting of Climate Change Using Score-Driven Ice-Age Models |
title_short |
Robust Estimation and Forecasting of Climate Change Using Score-Driven Ice-Age Models |
title_full |
Robust Estimation and Forecasting of Climate Change Using Score-Driven Ice-Age Models |
title_fullStr |
Robust Estimation and Forecasting of Climate Change Using Score-Driven Ice-Age Models |
title_full_unstemmed |
Robust Estimation and Forecasting of Climate Change Using Score-Driven Ice-Age Models |
title_sort |
robust estimation and forecasting of climate change using score-driven ice-age models |
publisher |
Multidisciplinary Digital Publishing Institute |
publishDate |
2022 |
url |
https://doi.org/10.3390/econometrics10010009 |
geographic |
Antarctic |
geographic_facet |
Antarctic |
genre |
Antarc* Antarctic |
genre_facet |
Antarc* Antarctic |
op_source |
Econometrics; Volume 10; Issue 1; Pages: 9 |
op_relation |
https://dx.doi.org/10.3390/econometrics10010009 |
op_rights |
https://creativecommons.org/licenses/by/4.0/ |
op_doi |
https://doi.org/10.3390/econometrics10010009 |
container_title |
Econometrics |
container_volume |
10 |
container_issue |
1 |
container_start_page |
9 |
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1774712554709319680 |