Working Paper: CEPR ID: DP15403
Authors: Christiane Baumeister; Pierre Guerin
Abstract: This paper evaluates the predictive content of a set of alternative monthly indicators of global economic activity for nowcasting and forecasting quarterly world GDP using mixed-frequency models. We find that a recently proposed indicator that covers multiple dimensions of the global economy consistently produces substantial improvements in forecast accuracy, while other monthly measures have more mixed success. This global economic conditions indicator contains valuable information also for assessing the current and future state of the economy for a set of individual countries and groups of countries. We use this indicator to track the evolution of the nowcasts for the US, the OECD area, and the world economy during the coronavirus pandemic and quantify the main factors driving the nowcasts.
Keywords: MIDAS models; Global economic conditions; World GDP growth; Nowcasting; Forecasting; Mixed frequency
JEL Codes: C22; C52; E37
Edges that are evidenced by causal inference methods are in orange, and the rest are in light blue.
| Cause | Effect |
|---|---|
| global economic conditions indicator (GECon) (F01) | quarterly world GDP growth (O50) |
| global economic conditions indicator (GECon) (F01) | forecast accuracy (C53) |
| GECon captures industrial activity and consumer confidence (E20) | quarterly world GDP growth (O50) |
| GECon is particularly effective during COVID-19 (D58) | quarterly world GDP growth (O50) |
| GECon's predictive power varies by economic context (E17) | quarterly world GDP growth (O50) |