The ECB Survey of Professional Forecasters (SPF) - A Review After Eight Years' Experience
By C. Bowles, R. Friz, V. Genre, G. Kenny, Aidan Meyler, Tuomas Rautanen. 2007
170 citations recorded by the source corpus.
View Paper: https://www.semanticscholar.org/paper/c5988741e4383992df2751d04e1c758c7506a4be
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
- V. Genre
- G. Kenny
- Aidan Meyler
- Xuguang Simon Sheng
- K. Lahiri
- Allan G. Timmermann
- Thomas S. Wallsten
- Yaron Shlomi
Citations in the corpus
- Accuracy, Unbiasedness and Efficiency of Professional Macroeconomic Forecasts: An empirical Comparison for the G7 (2011, unknown): “Notable exceptions are Harvey, Leybourne, and Newbold (2001), who analyze a set of selected individual forecasts for the UK from the survey data set provided by Consensus Economics; Gallo, Granger, and Jeon (2002), who analyze the evolution of macroeconomic forecasts for the US, the UK, and Japan; B”
- Combining expert forecasts: Can anything beat the simple average? (2013, methodology): “The SPF forecasts are described in detail in previous studies such as Bowles et al. (2007) and Garcia (2003).”
- Inattentive professional forecasters (2013, unknown): “To illustrate what lies behind this formal rejection, Table (6) reports the set of target moments in the SPF data and the same set of moments as generated by the model for different values of the parameters.”
- Anchoring of Inflation Expectations in the Euro Area: Recent Evidence Based on Survey Data (2016, unknown): “The ECB SPF is described in detail in Bowles et al. (2007).”
- Evolution of Forecast Disagreement in a Bayesian Learning Model (2008, background): “Bowles et al. (2007). Second, the within-agent variation in forecasts, however, starts with a relatively very low value at the 24-month horizon and remains almost the same at horizons 24 to 16 months.”
- Efficiently encoding and modeling subjective probability distributions for quantitative variables. (2016, background): “As another example, both the European Central Bank (ECB; e.g., Bowles et al., 2007; Garcia, 2003) and the Federal Reserve Bank of Philadelphia (Croushore, 1993) regularly survey experts to obtain their probabilistic forecasts of various economic indicator variables.”