Modeling and forecasting the long memory of Cyclical Trends in paleoclimate data
识别并估计地球温度、冰量和CO2的古气候数据中的相关周期，利用周期协整分析连接这些周期与地球偏心率和倾角，构建包含周期成分的预测模型，准确预测转折点。
This paper identifies and estimates the relevant cycles in paleoclimate data of earth temperature, ice volume and C O 2 . Cyclical cointegration analysis is used to connect these cycles to the earth eccentricity and obliquity and to see that the earth surface temperature and ice volume are closely connected. These findings are used to build a forecasting model including the cyclical component as well as the relevant earth and climate variables which outperforms models ignoring the cyclical behaviour of the data. Especially the turning points can be predicted accurately using the proposed approach. Out of sample forecasts for the turning points of earth temperature, ice volume and C O 2 are derived.