基于分数驱动的冰河时代模型对气候变化的稳健估计和预测

IF 1.1 Q3 ECONOMICS
Szabolcs Blazsek, A. Escribano
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引用次数: 3

摘要

我们使用了过去79.8万年期间的以下气候变量数据:全球冰量(Icet)、大气二氧化碳水平(CO2,t)和南极地表温度(Tempt)。这些变量是周期性的,并由以下强烈的外生轨道变量驱动:地球轨道的离心率、倾角和春分的岁差。我们引入了分数驱动的冰期模型,该模型使用条件均值和方差的鲁棒滤波器,推广了更新机制,并解决了最近的气候计量模型(基准冰期模型)的错误规范。分数驱动模型使用更一般的动态结构和异方差来控制遗漏的外生变量和极端事件。我们发现分数驱动模型提高了基准冰期模型的性能。我们提供了过去10万年气候变量的样本外预测。我们表明,在人类影响地球气候的最后1 - 1.5万年的预测期内,(i) Icet的预测高于观测到的Icet, (ii) CO2,t水平的预测低于观测到的CO2,t,和(iii) Tempt的预测低于观测到的Tempt。对基准冰期模型的预测得到分数驱动模型的加强。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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 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.
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来源期刊
Econometrics
Econometrics Economics, Econometrics and Finance-Economics and Econometrics
CiteScore
2.40
自引率
20.00%
发文量
30
审稿时长
11 weeks
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