This article reviews some recent advances in testing for serial correlation, provides Stata code for implementation and illustrates its application to market risk forecast evaluation. The classical and widely used Portamenteau tests and their data-driven versions are the focus of this article. These tests are simple to implement for two reasons: First, the researcher does not need to specify the order of the autocorrelation tested, since the test automatically chooses this number; second, its asymptotic null distribution is chi-square with one degree of freedom, so there is no need of using a bootstrap procedure to estimate the critical values. We illustrate the wide applicability of the methodology with applications to forecast evaluation for market risk measures, such as Value-at-Risk and Expected Shortfall.
st0504; dbptest; rtau; autocorrelation; consistency; power; akaike's information criterion; schwarz's bayesian information criterion; market risk