Sidetrack: A Seasonal Autoregressive Model for the Central England Temperature Series

by Graham Giller February 01, 2012 23:10

I recently wrote up, and submitted to the Social Science Research Network, a study of the Central England Temperature series that I did a couple of years ago and recently updated. You can access the paper at the SSRN here: A Seasonal Autoregressive Model for the Central England Temperature Series.

What I do in this analysis is build a seasonal autoregressive model for what is the longest time series of directly measured temperatures available for analysis. This model includes a linear trend component. I fit the model for both the pre-Industrial Revolution Period (1659 to 1849) and the Post-Industrial Revolution period (1949 to 1999). In the former period no trend is apparent, in the latter period I find a warming trend of 1 °C/century. This result has a borderline statistical significance (less than 3σ). I use both models to forecast the temperatures for the 21st. Century and find that the older, trend free, model does a slightly better job out-of-sample than the newer model with the trend.


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About the Author

Graham Giller - Headshot GRAHAM GILLER
Dr. Giller holds a doctorate from Oxford University in experimental elementary particle physics. His field of research was statistical astronomy using high energy cosmic rays. After leaving Oxford, he worked in the Process Driven Trading Group at Morgan Stanley, as a strategy researcher and portfolio manager. He then ran a CTA/CPO firm which concentrated on trading eurodollar futures using statistical models. From 2004, he has managed a private family investment office. In 2009, he joined a California based hedge fund startup, concentrating on high frequency alpha and volatility forecasting. My updated resume is on LinkedIn.



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