José Iparraguirre writes:

There’s a letter in the latest issue of The Economist (July 31st) signed by Sir Richard Branson (Virgin), Michael Masters (Masters Capital Management) and David Frenk (Better Markets) about an “>OECD report on speculation and the prices of commodities, which includes the following: “The report uses a Granger causality test to measure the relationship between the level of commodities futures contracts held by swap dealers, and the prices of those commodities. Granger tests, however, are of dubious applicability to extremely volatile variables like commodities prices.”

The report says:

Granger causality is a standard statistical technique for determining whether one time series is useful in forecasting another. It is important to bear in mind that the term causality is used in a statistical sense, and not in a philosophical one of structural causation. More precisely a variable A is said to Granger cause B if knowing the time paths of B and A together improve the forecast of B based on its own time path, thus providing a measure of incremental predictability. In our case the time series of interest are market measures of returns, implied volatility, and realized volatility, or variable B. . . . Simply put, Granger’s test asks the question: Can past values of trader positions be used to predict either market returns or volatility?

This seems clear enough, but the authors muddy the water later on by writing:

There is a positive contemporaneous association between changes in net positions held by index traders and price changes (returns) in the CBOT wheat market . . . this contemporaneous analysis cannot distinguish between the increase in index traders’ positions and other correlated shifts in fundamentals: correlation does not imply causation. [Italics added by me.]

This seems to miss the point. Granger causality, as defined above, is a measure of correlation, or of partial correlation. It’s just a correlation between things that are not happening at the same time. The distinction here is in what’s being correlated. The phrase “correlation does not imply causation” does not belong here at all! (Unless I’m missing something, which is always possible.)

I have nothing to say on the particulars, as I have no particular expertise in this area. But in general, I’d prefer if researchers in this sort of problem were to try to estimate the effects of interest (for example, the amount of additional information present in some forecast) rather than setting up a series of hypothesis tests. The trouble with tests is that when they reject, it often tells us nothing more than that the sample size is large. And when they fail to reject, if often tells us nothing more than that the sample size is small. In neither case is the test anything like a direct response to the substantive question of interest.