Robust estimation and forecasting of climate change using score-driven ice-age models

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ScScore-driven models applied to finance and economics have attracted significant attention in the last decade. In this paper, we apply those models to climate data. We study the robustness of a recent climate econometric model, named ice-age model, and we extend that model by using score-driven filters in the measurement and transition equations. The climate variables considered are Antarctic ice volume Icet, atmospheric carbon dioxide level CO2,t, and land surface temperature Tempt, which during the history of the Earth were driven by exogenous variables. The influence of humanity on climate started approximately 10-15 thousand years ago, and it has significantly increased since then. We forecast the climate variables for the last 100 thousand years, by using data for the period of 798 thousand years ago to 101 thousand years ago for which humanity did not influence the Earth’s climate. For the last 10-15 thousand years of the forecasting period, we find that: (i) the forecasts of Icet are above the observed Icet, (ii) the forecasts of the CO2,t level are below the observed CO2,t, and (iii) the forecasts of Tempt are below the observed Tempt. Our results are robust, and they disentangle the effects of humanity and orbital variables.
Climate Change, Ice-Ages and Inter-Glacial Periods, Atmospheric Co2 and Land Surface Temperature, Dynamic Conditional Score Models, Generalized Autoregressive Score Models
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