Spurious Correlations in Time Series Data: A Note

Jamal Munshi

Sonoma State University

August 22, 2016

Unrelated time series data can show spurious correlations by virtue of a shared drift in the long term trend. The spuriousness of such correlations is demonstrated with examples. The SP500 stock market index, GDP at current prices for the USA, and the number of homicides in England and Wales in the sample period 1968 to 2002 are used for this demonstration. Detrended analysis shows the expected result that at an annual time scale the GDP and SP500 series are related and that neither of these time series is related to the homicide series. Correlations between the source data and those between cumulative values show spurious correlations of the two financial time series with the homicide series. These results have implications for empirical evidence that attributes changes in temperature and carbon dioxide levels in the surface-atmosphere system to fossil fuel emissions.

Number of Pages in PDF File: 10

Keywords: climate change, global warming, carbon dioxide, fossil fuel emissions, carbon emissions, applied statistics, spurious correlations, cumulative values, spurious correlations between cumulative values, causation and correlation

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Date posted: August 24, 2016 ; Last revised: October 13, 2016

Suggested Citation

Munshi, Jamal, Spurious Correlations in Time Series Data: A Note (August 22, 2016). Available at SSRN: https://ssrn.com/abstract=2827927 or http://dx.doi.org/10.2139/ssrn.2827927

Contact Information

Jamal Munshi (Contact Author)
Sonoma State University ( email )
1801 East Cotati Avenue
Rohnert Park, CA 94928
United States
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