Forecasting GDP with many predictors in a small open economy: forecast or information pooling?

ABSTRACT: This study compares two distinct approaches, pooling forecasts from single indicator MIDAS models versus pooling information from indicators into factor MIDAS models, for short-term Singapore GDP growth forecasting with a large ragged-edge mixed frequency dataset. We consider various popular weighting schemes in the literature when conducting forecast pooling. As for factor extraction, both the conventional dynamic factor model and the three-pass regression filter approach are considered. We investigate the relative predictive performance of all methods in a pseudo-out-of-sample forecasting exercise from 2007Q4 to 2020Q3. In the stable growth non-crisis period, no substantial difference in predictive performance is found across forecast models. In comparison, we find information pooling te

SUBMITTER: Chow HK 

PROVIDER: S-EPMC9826763 | biostudies-literature | 2023 Jan

REPOSITORIES: biostudies-literature