We develop a framework to nowcast (and forecast) economic variables with machine learning techniques. We explain how machine learning methods can address common shortcomings of traditional OLS-based models and use several machine learning models to predict real output growth with lower forecast...

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,...

We document information rigidity in forecasts for real GDP growth in 46 countries over the past two decades. We investigate: (i) if rigidities are lower around turning points in the economy, such as in times of recessions and crises; (ii) if rigidities differ across countries, particularly...

This paper investigates the performance of the IMF WEO growth forecast revisions across different horizons and country groups. We find that: (i) growth revisions in horizons closer to the actual are generally larger, more volatile, and more negative; (ii) on average, growth revisions are in the...

This paper is the first attempt to directly explore the long-run nonlinear relationship between the shadow economy and level of development. Using a dataset of 158 countries over the period from 1996 to 2015, our results reveal a robust U-shaped relationship between the shadow economy size and...