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

Peter Romilly

Research output: Contribution to journalArticle

  • 25 Citations

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.
Original languageEnglish
Pages (from-to)61-70
Number of pages10
JournalJournal of Environmental Management
Volume76
Issue number1
DOIs
StatePublished - Jul 2005
Externally publishedYes

Fingerprint

Decision Making
Temperature
temperature
modeling
Volatilization
Climate Change
decision making
time series
climate change
Climate change
Time series
Decision making
Cluster Analysis
Economics
Costs and Cost Analysis
Datasets
economic activity
methodology
cost
Association reactions

Cite this

Romilly, Peter / Time series modelling of global mean temperature for managerial decision-making.

In: Journal of Environmental Management, Vol. 76, No. 1, 07.2005, p. 61-70.

Research output: Contribution to journalArticle

@article{e9fe6da6a8954a1bae304d68c1b70f0c,
title = "Time series modelling of global mean temperature for managerial decision-making",
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.",
author = "Peter Romilly",
year = "2005",
month = "7",
doi = "10.1016/j.jenvman.2005.01.008",
volume = "76",
pages = "61--70",
journal = "Journal of Environmental Management",
issn = "0301-4797",
publisher = "Academic Press Inc.",
number = "1",

}

Time series modelling of global mean temperature for managerial decision-making. / Romilly, Peter.

In: Journal of Environmental Management, Vol. 76, No. 1, 07.2005, p. 61-70.

Research output: Contribution to journalArticle

TY - JOUR

T1 - Time series modelling of global mean temperature for managerial decision-making

AU - Romilly,Peter

PY - 2005/7

Y1 - 2005/7

N2 - 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.

AB - 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.

U2 - 10.1016/j.jenvman.2005.01.008

DO - 10.1016/j.jenvman.2005.01.008

M3 - Article

VL - 76

SP - 61

EP - 70

JO - Journal of Environmental Management

T2 - Journal of Environmental Management

JF - Journal of Environmental Management

SN - 0301-4797

IS - 1

ER -