This paper analyzes the effects of IMF member countries participation in the IMF’s Data Standards Initiatives (DSI) on the statistical quality of WEO forecasts. Results show that WEO forecasts for SDDS subscribers are in general better than for GDDS participants and those member countries than do not participate in the DSIs. Policy implications are that the DSI positively affect the statistical quality of forecasts and by extension improve the necessary conditions for multilateral surveillance and the provision of member countries with high quality policy advice.
Jonas Dovern, Mr. Ulrich Fritsche, Mr. Prakash Loungani, and Ms. Natalia T. Tamirisa
We examine the behavior of forecasts for real GDP growth using a large panel of individual forecasts from 30 advanced and emerging economies during 1989–2010. Our main findings are as follows. First, our evidence does not support the validity of the sticky information model (Mankiw and Reis, 2002) for describing the dynamics of professional growth forecasts. Instead, the empirical evidence is more in line with implications of "noisy" information models (Woodford, 2002; Sims, 2003). Second, we find that information rigidities are more pronounced in emerging economies than advanced economies. Third, there is evidence of nonlinearities in forecast smoothing. It is less pronounced in the tails of the distribution of individual forecast revisions than in the central part of the distribution.
This paper simulates out-of-sample inflation forecasting for Germany, the UK, and the US. In contrast to other studies, we use output gaps estimated with unrevised real-time GDP data. This exercise assumes an information set similar to that available to a policymaker at a given point in time since GDP data is subject to sometimes substantial revisions. In addition to using real-time datasets for the UK and the US, we employ a dataset for real-time German GDP data not used before. We find that Phillips curves based on ex post output gaps generally improve the accuracy of inflation forecasts compared to an AR(1) forecast but that real-time output gaps often do not help forecasting inflation. This raises the question how operationally useful certain output gap estimates are for forecasting inflation.