Equivalence Between Out-of-Sample Forecast Comparisons and Wald Statistics ∗

By P. R. Hansen, Allan G. Timmermann. 2012

47 citations recorded by the source corpus.

We establish the equivalence between a commonly used out-of-sample test of equal predictive accuracy and the difference between two Wald statistics. This equivalence greatly simplifies the computational burden of calculating recursive out-of-sample tests and evaluating their critical values. Our results shed new light on many aspects of the test and establishes certain weaknesses associated with using out-of-sample forecast comparison tests to conduct inference about nested regression models.

Subfields: Management Science and Operations Research

View Paper: https://www.semanticscholar.org/paper/8330f1f4cf5983f872741fd0c7402e58bb3f234e

Citations in the corpus

• Comparing Predictive Accuracy, Twenty Years Later: A Personal Perspective on the Use and Abuse of Diebold–Mariano Tests (2015, unknown): “Hansen and Timmermann (2013) made this point very clearly in the nested case, showing precisely how the leading recursive pseudo-out-of-sample procedure of McCracken (2007) produces costly power reduction relative to the full-sample Wald statistic while simultaneously producing no offsetting…”
• Effects of Index-Fund Investing on Commodity Futures Prices (2014, unknown): “Hansen and Timmermann (2013) demonstrate that the popular practice of calculating an “out-of-sample” MSE of a set of recursive regressions that end at all points between some sample dates T0 and T is asymptotically equivalent to looking at the difference between two simple Wald tests, the first…”