Combining forecast densities from VARs with uncertain instabilities

By A. Jore, James Mitchell, Shaun P. Vahey. 2010

188 citations recorded by the source corpus.

Subfields: Economics and Econometrics, Management Science and Operations Research

View Paper: https://www.semanticscholar.org/paper/a45d6bf15c761cb72d90bc59aff663856ab4bee1

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In Management Science and Operations Research

Citations in the corpus

Bayesian Nonparametric Calibration and Combination of Predictive Distributions (2015, methodology): “The model weights in the linear pooling are estimated by using the recursive log score, see, for example, Jore, Mitchell, and Vahey (2010).”
Evaluating the Calibration of Multi-Step-Ahead Density Forecasts Using Raw Moments (2015, methodology): “5 Finally, in several applications like those by Clements (2004), Mitchell and Hall (2005), Jore et al. (2010), Bache et al. (2011), and Aastveit et al. (2011) one finds a variant of the test by Berkowitz (2001) adapted to the case of serially correlated INTs. Instead of testing for zero mean, unit ”
Forecast combinations: An over 50-year review (2022, methodology): “Kang et al. (2021) utilized a group of features only measuring the diversity across the candidate forecasts to construct a forecast combination model mapping the diversity matrix to the forecast errors.”
Nowcasting GDP in Real Time: A Density Combination Approach (2014, unknown): “While Jore et al. (2010) seem to find some evidence of gains in adaptive log score weights for density combination forecasts, this re20”
Real-Time Density Forecasts From Bayesian Vector Autoregressions With Stochastic Volatility (2011, methodology): “Although Jore, Mitchell, and Vahey (2010) provided an historical evaluation of density forecasts, their volatility models are limited to discrete break specifications.”
The Evolution of Forecast Density Combinations in Economics (2018, background): “Subsequent research, much but not all with a central banking focus and including Jore et al. (2010), Bache et al. (2011) and Bache et al. (2010), extended the forecast density combinations to consider a large number of models.”
Generalised Density Forecast Combinations (2015, unknown): “…econometric applications as a means of improving forecast ‘accuracy’, as measured by a scoring rule (see Gneiting & Raftery (2007)), especially in the face of uncertain instabilities and uncertainty about the preferred model; e.g., see Jore et al. (2010), Geweke & Amisano (2012) and Rossi (2013).”
Combining inflation density forecasts (2010, methodology): “Formally, ωt+h,t,i = 1/N for all t, h, i. Recursive log score weights (RLSW): A weighting scheme based on the out-of-sample performance of density forecasts are recursive log score weights as proposed in e.g. Jore et al. (2008).”
Short-Term GDP Forecasting With a Mixed-Frequency Dynamic Factor Model With Stochastic Volatility (2016, unknown): “In particular Jore et al. (2010) show that changes in the underlying data generating process can severely hinder the accuracy of density forecasts produced with time invariant models.”
Combining density forecasts using focused scoring rules (2017, methodology): “The second scheme, adopted from Jore, Mitchell, and Vahey (2010), assigns weights to the various individual density forecasts based on their relative average scores.”