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Time series modelling of global mean temperature for managerial decision-making

机译：用于管理决策的全球平均温度的时间序列建模

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Climate change has important implications for business and economic activity. Effective management of climate change impacts will depend on the availability of accurate and cost-effective forecasts. This paper uses univariate time series techniques to model the properties of a global mean temperature dataset in order to develop a parsimonious forecasting model for managerial decision-making over the short-term horizon. Although the model is estimated on global temperature data, the methodology could also be applied to temperature data at more localised levels. The statistical techniques include seasonal and non-seasonal unit root testing with and without structural breaks, as well as ARIMA and GARCH modelling. A forecasting evaluation shows that the chosen model performs well against rival models. The estimation results confirm the findings of a number of previous studies, namely that global mean temperatures increased significantly throughout the 20th century. The use of GARCH modelling also shows the presence of volatility clustering in the temperature data, and a positive association between volatility and global mean temperature.
机译：气候变化对商业和经济活动具有重要意义。对气候变化影响的有效管理将取决于准确和具成本效益的预测的可用性。本文使用单变量时间序列技术对全球平均温度数据集的属性进行建模，以开发用于短期内管理决策的简约预测模型。尽管该模型是根据全球温度数据估算的，但该方法也可以应用于局部水平的温度数据。统计技术包括具有和不具有结构破坏的季节性和非季节性单位根测试，以及ARIMA和GARCH建模。预测评估表明，所选模型相对于竞争对手模型表现良好。估算结果证实了许多先前研究的结果，即全球平均温度在整个20世纪都显着增加。 GARCH建模的使用还表明温度数据中存在波动性聚类，并且波动性与全球平均温度之间存在正相关关系。

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