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Please use this identifier to cite or link to this item: http://hdl.handle.net/10373/315

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Title: Time series modelling of global mean temperature for managerial decision-making
Authors: Romilly, Peter
Affiliation: University of Abertay Dundee. Dundee Business School
Keywords: Climate change
Forecasting
Unit root tests
ARIMA and GARCH models
Issue Date: Jul-2005
Publisher: Elsevier Ltd
Type: Journal Article
Refereed: peer-reviewed
Rights: Published version (c)Elsevier Ltd, available from DOI: 10.1016/j.jenvman.2005.01.008
Citation: Romily, P. 2005. Time series modelling of global mean temperature for managerial decision-making. Journal of Environmental Management. 76(1):pp.61-70. [Online] Available from DOI: 10.1016/j.jenvman.2005.01.008
Abstract: 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.
URI: http://hdl.handle.net/10373/315
ISSN: 0301-4797
Appears in Collections:Dundee Business School Collection

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