Combining inflation density forecasts

By Christian Kascha, Francesco Ravazzolo. 2010

137 citations recorded by the source corpus.

Subfields: Demography, Economics and Econometrics, Management Science and Operations Research

View Paper: https://www.semanticscholar.org/paper/943cb0e45e08fa7cc3df85029a1b1937ca6a62d7

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Demography

1. K. Wallis
2. K. Aastveit
3. James Mitchell
4. H. V. Dijk
5. Tilmann Gneiting
6. Francesco Ravazzolo

In Economics and Econometrics

1. Tilmann Gneiting
2. Francesco Ravazzolo
3. Roopesh Ranjan
4. K. Wallis
5. James Mitchell
6. K. Aastveit
7. H. V. Dijk
8. Andrew J. Patton

In Management Science and Operations Research

1. Francesco Ravazzolo
2. Roopesh Ranjan
3. Tilmann Gneiting
4. K. Wallis
5. James Mitchell
6. K. Aastveit
7. H. V. Dijk
8. Aki Vehtari

Citations in the corpus

• Nowcasting GDP in Real Time: A Density Combination Approach (2014, unknown): “This distribution can accommodate both skewness and kurtosis and be multimodal (see Kascha and Ravazzolo 2010).”
• Combining Predictive Distributions (2011, background): “…of choice, with the pioneering work of Winkler (1968) and Zarnowitz (1969), and recent papers by Mitchell and Hall (2005), Wallis (2005), Hall and Mitchell (2007), Jore, Mitchell and Vahey (2010), Kascha and Ravazzolo (2010) and Garratt et al. (2011) being examples in the case of density forecasts.”
• Quantile aggregation of density forecasts (2014, unknown): “Kascha and Ravazzolo (2010) provide an empirical comparison of the linear versus the logarithmic opinion pool of several forecasting models of inflation.”
• Advances in Forecasting Under Instability (2012, unknown): “…densities are mis-speci ed.46 Potentially interesting alternative avenues for future research may include non-linear (logarithmic) combinations (e.g Kascha and Ravazzolo, 2010, although they don t focus on density forecasting and instabilities) and maximizing the forecasting performance not of the…”
• Combining forecasts – forty years later (2011, background): “It seems to be agreed to base the weights on past forecast performance as measured by the average logarithmic score, possibly allowing for time variation in the weights (Hall and Mitchell, 2007; Geweke and Amisano, 2009; Jore et al., 2010; Kascha and Ravazzolo, 2010).”
• The Evolution of Forecast Density Combinations in Economics (2018, methodology): “Second to the linear opinion pool, the logarithmic opinion pool is the most widely used density forecast combination method in economics; e.g. see Kascha and Ravazzolo (2010) and Wallis (2011).”