Working Paper 11 - 20 September 2011 Advances in Forecast Evaluation

Working papers of the Federal Reserve Bank of Cleveland are preliminary materials circulated to stimulate discussion and critical comment on research in progress. They may not have been subject to the formal editorial review accorded offi cial Federal Reserve Bank of Cleveland publications. The views stated herein are those of the authors and are not necessarily those of the Federal Reserve Bank of Cleveland or of the Board of Governors of the Federal Reserve System. This paper surveys recent developments in the evaluation of point forecasts. Taking West's (2006) survey as a starting point, we briefl y 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 coeffi cients), the evaluation of forecasts in the fi nite sample (based on estimated model coeffi cients), 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. The views expressed herein are solely those of the authors and do not necessarily refl ect the views of the Federal Reserve Bank of Cleveland, the Federal Reserve Bank of St. Louis, the Federal Reserve System, or any of its staff.