The Great Recession and the subsequent period of subdued GDP growth in most advanced economies have highlighted the need for macroeconomic forecasters to account for sudden and deep recessions, periods of higher macroeconomic volatility, and fluctuations in trend GDP growth. In this paper, we...

This paper assesses the OECD’s projections for GDP growth and inflation during the global financial crisis and recovery, focussing on lessons that can be learned. The projections repeatedly over-estimated growth, failing to anticipate the extent of the slowdown and later the weak pace of the...

The present paper develops Adaptive Trees, a new machine learning approach specifically designed for economic forecasting. Economic forecasting is made difficult by economic complexity, which implies non-linearities (multiple interactions and discontinuities) and unknown structural changes (the...

This paper estimates potential output losses from the global financial crisis by comparing recent OECD published projections with a counter-factual assuming a continuation of pre-crisis productivity trends and a trend employment rate which is sensitive to demographic trends. Among the 19 OECD...

This paper describes the methodology used in the OECD Economics Department to produce historical estimates and short-run projections of potential output. These estimates are used mainly in the OECD Economic Outlook, in country surveys and as starting point for long-run scenarios. Total-economy...

This paper extends the OECD Economics Department’s suite of short-term indicator models for quarterly GDP growth, which currently cover only the G7 countries, to the BRIICS countries. Reflecting the relative scarcity of high-quality macroeconomic time series, the paper adopts a small-scale...

This paper introduces the OECD Weekly Tracker of economic activity for 46 OECD and G20 countries using Google Trends search data. The Tracker performs well in pseudo-real time simulations including around the COVID-19 crisis. The underlying model adds to the previous Google Trends literature in...