出版社：Organisation for Economic Co-operation and Development (OECD)
摘要：This paper compares the short-term forecasting performance of state-of-the-art large-scale dynamic factor models (DFMs) and the small-scale bridge models routinely used at the OECD. Pseudo-real time out-of-sample forecasts for France, Germany, Italy, Japan, United Kingdom and the United States during and after the Great Recession (2008-2014) suggest that large-scale DFMs are not systematically more accurate than small-scale bridge models, especially at short forecast horizons. Moreover, DFM parameters appear to be highly unstable during the Great Recession (2008-2009), making forecast revisions between successive vintages difficult to explain as revisions cannot be fully attributed to news on specific groups of indicators. The implication for OECD forecasting practice is that there would be no gain from switching from the current small-scale bridge models to large-scale DFMs.
关键词：bridge models; big data; nowcasting; dynamic factor models
