Why can’t professional macroeconomic forecasters predict recessions?
The professional forecasters’ inability to anticipate macroeconomic recessions is well documented. The literature has found that aggregate or consensus forecasts are too optimistic before downturns and too pessimistic before recoveries. This paper explores whether this result also holds at the indiv...
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| Tipo de recurso: | artículo |
| Estado: | Versión borrador |
| Fecha de publicación: | 2018 |
| País: | España |
| Institución: | Universidad Politécnica de Cartagena(UPCT) |
| Repositorio: | Repositorio Digital UPCT |
| OAI Identifier: | oai:repositorio.upct.es:10317/7398 |
| Acceso en línea: | http://hdl.handle.net/10317/7398 http://seville2019.econworld.org/papers/Lopez_Perez_Why.pdf |
| Access Level: | acceso abierto |
| Palabra clave: | Funcas Forecast errors Professional Forecasters Expected GDP growth Forecast revisions Economía Aplicada 53 Ciencias Económicas |
| Sumario: | The professional forecasters’ inability to anticipate macroeconomic recessions is well documented. The literature has found that aggregate or consensus forecasts are too optimistic before downturns and too pessimistic before recoveries. This paper explores whether this result also holds at the individual level or is the result of an aggregation bias. Using a Spanish survey of professional forecasters conducted by Funcas, I find that individual forecasters are indeed too optimistic before recessions. The reason is not that they become inattentive when the economy is in good shape. Instead, they put too much weight on the most recent developments when producing their forecasts. The analysis of their forecast revisions reveals that better-than-expected data makes some forecasters to revise their forecasts upwards too much. These revisions raise the consensus forecast and trigger a herd behaviour by other forecasters, who also revise up their forecasts. Both factors lead to subsequent negative forecast errors, especially when a recession occurs. Consequently, professional forecasters could improve their forecasting performance by reacting less aggressively to the latest data releases and by avoiding inefficient herding. |
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