improves upon standard tests for cross-section correlation, such as Breusch and Pagan (1980) for example, as it requires less restrictive assumptions to be satisfied in the data and allows to precisely identify the series in the panel that determine the rejection of the null hypothesis of no cross-section correlation. Ng (2006)’s methodology produces a split in the sample of all pairwise correlation coefficients (55 correlations in our sample of 11 commodity price returns) into two subsets of small and large absolute correlations. Building on the literature about spacings and adopting breakpoint analysis techniques, the procedure makes use of a spacings variance ratio test statistic (SVR) to test the null hypothesis of no correlation within groups of correlation coefficients.<sup>8</sup> We assess the cross-section unconditional correlation of the 11 commodity price returns over the full sample ( _T_ = 520 monthly observations) and over the four subsamples described in Section 2 ( _T_ = 117 monthly observations over S1, _T_ = 92 monthly observations over S2, _T_ = 238 monthly observations over S3 and _T_ = 73 monthly observations over S4). As reported in Table 2, we reject the null hypothesis of no cross-section correlation at the 10% level in most time periods and for most values of _q_ , a parameter that determines the length of the spacings needed to run the test. The only exception is the 1979M11-1987M06 period, for which we are unable to reject the null of no cross-section correlation in the entire panel. It is worth noting that, for the same 1979M111987M06 period, the extent of overall cross-section correlation is small: 12 pairs of price returns in the large correlation subset exhibit significant levels of (positive) correlation, the remaining 43 in the small correlation subset are, instead, not statistically different from zero. The results of the testing procedure also suggest a high level of generally positive co-movement (i.e., positive correlation) of price returns concerning large groups of commodity pairs in all time periods. The extent of positive co-movement increases over time. The number of positive correlation coefficients is 41 in the first period and becomes 54 in the last period. More specifically, we observe 37 positive correlation coefficients (out of 48 statistically significant correlations) in the 1970M021979M10 large correlation set, 12 positive correlation coefficients (out of 12 statistically significant correlations) in the 1979M11-1987M06 large correlation set and 23 positive correlation coefficients (out of 28 statistically significant correlations) in the 1987M07-2007M04 large correlation set. In the 2007M05-2013M05 period, the large correlation set includes 48 positive correlation coefficients (out of 48 statistically significant correlations).

Table 6 in Appendix B provides additional information about the cross-section correlation of these commodities. We sort the pairs of commodities in the large absolute correlation sets over full sample and subsamples in a descending order. Overall, the price returns of related commodities (i.e., commodities in the same category) tend to be strongly positively correlated. We observe strong and positive correlations within cereal crops (specifically maize, sorghum, barley and wheat), between cereal crops and soybean oil and within energy commodities (oil, coal and gas). Related commodities are likely affected by similar supply and demand factors since they are close substitutes in demand, have similar input costs and share common market information. Such a positive correlation between related products is typically labeled as “category based” co-movement, given

> 8We provide further methodological details in Appendix A.2

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