As with other areas of empirical finance, the econometric analysis of energy and commodity markets involves applying a suite of appropriate econometric tests to a range of appropriate time series, cross-sectional or panel data. Such empirical studies, by their very nature, suffer from the well-established problem of data snooping bias, whereby there is a non-negligible likelihood that statistically significant results may be identified by random chance alone rather than as a result of any underlying statistical relationships. White (2000) describes data snooping bias as resulting from a given set of data being used more than once for purposes of inference or model selection, whereby any statistically significant results are due to chance rather than to any merit inherent in the methodology. In the statistical and econometric literature, this phenomenon is more commonly referred to as the multiple comparisons problem that results from multiple hypothesis testing. Although the problem is well established in the literature, much of the empirical finance work to date (across all market classes, including energy and commodities) either ignores or is unaware of the problem.
Rather than present a comprehensive literature review, the interested reader is instead directed to the work of Romano et al. (2010), who provide a detailed exposition of the issues ...