Abstract:
In a recent paper, Gupta et al., (2015), analyzed whether sunspot numbers cause global temperatures
based on monthly data covering the period 1880:1-2013:9. The authors find that standard time
domain Granger causality test fails to reject the null hypothesis that sunspot numbers does not cause
global temperatures for both full and sub-samples, namely 1880:1-1936:2, 1936:3-1986:11 and
1986:12-2013:9 (identified based on tests of structural breaks). However, frequency domain
causality test detects predictability for the full-sample at short (2 to 2.6 months) cycle lengths, but
not the sub-samples. But since, full-sample causality cannot be relied upon due to structural breaks,
Gupta et al., (2015) concludes that the evidence of causality running from sunspot numbers to global
temperatures is weak and inconclusive. Given the importance of the issue of global warming, our
current paper aims to revisit this issue of whether sunspot numbers cause global temperatures, using
the same data set and sub-samples used by Gupta et al., (2015), based on an nonparametric Singular
Spectrum Analysis (SSA)-based causality test. Based on this test, we however, show that sunspot
numbers have predictive ability for global temperatures for the three sub-samples, over and above
the full-sample. Thus, generally speaking, our non-parametric SSA-based causality test outperformed
both time domain and frequency domain causality tests and highlighted that sunspot numbers have
always been important in predicting global temperatures.
