Advances in Forecast Evaluation

By Todd E. Clark, Michael W. McCracken. 2011

179 citations recorded by the source corpus.

This paper surveys recent developments in the evaluation of point forecasts. Taking West’s (2006) survey as a starting point, we briefly cover the state of the literature as of the time of West’s writing. We then focus on recent developments, including advancements in the evaluation of forecasts at the population level (based on true, unknown model coefficients), the evaluation of forecasts in the finite sample (based on estimated model coefficients), and the evaluation of conditional versus unconditional forecasts. We present original results in a few subject areas: the optimization of power in determining the split of a sample into in-sample and out-of-sample portions; whether the accuracy of inference in evaluation of multistep forecasts can be improved with the judicious choice of HAC estimator (it can); and the extension of West’s (1996) theory results for population-level, unconditional forecast evaluation to the case of conditional forecast evaluation.

Subfields: Management Science and Operations Research

View Paper: https://www.semanticscholar.org/paper/e73fd7a85d123bff15e3db951d96ffa887c69ede

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In Management Science and Operations Research

Citations in the corpus

Out-of-Sample Forecast Tests Robust to the Choice of Window Size (2011, background): “E (Lt+h( )) = 0: (12) In order to test (12), one simply tests whether b Lt+h has zero mean by a standard Wald test in a regression of b Lt+h onto a constant (i.”
Using Entropic Tilting to Combine BVAR Forecasts With External Nowcasts (2014, methodology): “Based on simulation evidence in Clark and McCracken (2013), in computing the variance estimator which enters the test statistic, we employ a rectangular kernel truncated at lag h − 1 and incorporate the finite sample correction due to Harvey, Leybourne, and Newbold (1997).”
Bayesian VARs : specification choices and forecast accuracy (2011, unknown): “Following the recommendation of Clark and McCracken (2011c), to reduce the chances of spurious rejections at longer forecast horizons, we compute the Diebold–Mariano test with the Harvey et al. (1997) adjustment of the variance that enters the test statistic.”
Multi-Horizon Forecast Comparison (2019, background): “Importantly, both uniform and average multi-horizon SPA, as well as their respective tests, are defined in such a way that they reduce to the standard Diebold and Mariano (1995) test when only considering a single horizon. In addition to the pairwise tests, we propose a multi-horizon version of the ”