Jarque Bera Test statistic What exactly does the Jarque-Bera test statistic represent?
It seems like the p-value is of more relevance when interpreting the data.
I would assume that as the JB statistic tests for normality, a value of 0 would mean the data is perfectly normal. Does this mean that all we look for in a JB test statistic is its proximity to 0?
 A: Firstly note that failure to reject a null doesn't mean the null is true -- so just because a goodness of fit test came out perfectly consistent with a normal, that doesn't imply that the data were drawn from a normal distribution. 
However, in the case of this particular test the connection is even more tenuous -- even if the population skewness and kurtosis were the same as for a normal, it still doesn't imply normality. There's an infinity of distributions that have skewness 0 and kurtosis 3. A number of examples can be found on site here with a bit of searching.
The closer the Jarque-Bera test statistic* is to zero, the closer the sample skewness and kurtosis are to the values 0 and 3. Rejection indicates inconsistency with those values (and hence with normality), failure to reject doesn't imply normality.
You could perhaps interpret it as a weighted sum-of-squared deviations from the expected cumulants under normality. (At least that would be the interpretation in very large samples - the values are asymptotic, but the approach of the joint distribution to the asymptotic distribution comes in quite slowly; the distribution shows clear signs of dependence even for samples of size 300 for example)
*(that name for the test is popular among econometricians but it is wrongly named, since the statistic was proposed, used and discussed decades before they came along).  
