Combining forecast densities from VARs with uncertain instabilities

Jore, Anne Sofie; Mitchell, James; Vahey, Shaun P.

doi:10.1002/jae.1162

Cited by 167 publications

(149 citation statements)

References 40 publications

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“…Among others, Hall and Mitchell [2007], Jore et al [2010] and Geweke and Amisano [2010b] discuss the use of the log score as a ranking device on the forecast ability of different models. The log score is easy to evaluate and can be used to detect misspecification by studying how model weights change over different vintages.…”

Section: Combinations Of Multivariate Predictive Densitiesmentioning

confidence: 99%

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Time-varying combinations of predictive densities using nonlinear filtering

Billio¹,

Casarin²,

Ravazzolo

et al. 2013

Journal of Econometrics

138

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Section: Combinations Of Multivariate Predictive Densitiesmentioning

confidence: 99%

“…The first one is a Bayesian model averaging (BMA) approach similar to Jore et al [2010] and Hoogerheide et al [2010]. Following the notation in the previous section, model predictions are combined by:…”

Section: Gdp Growth and Pce Inflationmentioning

confidence: 99%

Time-varying combinations of predictive densities using nonlinear filtering

Billio¹,

Casarin²,

Ravazzolo

et al. 2013

Journal of Econometrics

138

View full text Add to dashboard Cite

“…It is possible to calculate MSEs based on the means of the distributions, but it is more natural to take advantage of the full distributions, see e.g. Jore et al (2010) and Amisano and Geweke (2009). Then the question of evaluating densities arises.…”

Section: Deriving the Weightsmentioning

confidence: 99%

“…In the first step, we group models into different model classes. The nowcasts from all individual models within a model class are combined using the logarithmic score (log score) to compute the weights, see among others Jore et al (2010). This yields a combined predictive density nowcast for each of the three model classes.…”

Section: Introductionmentioning

confidence: 99%

Nowcasting GDP in Real-Time: A Density Combination Approach

Aastveit

Gerdrup²,

Jore³

et al. 2011

SSRN Journal

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In this paper we use U.S. real-time vintage data and produce combined density nowcasts for quarterly GDP growth from a system of three commonly used model classes. The density nowcasts are combined in two steps. First, a wide selection of individual models within each model class are combined separately. Then, the nowcasts from the three model classes are combined into a single predictive density. We update the density nowcast for every new data release throughout the quarter, and highlight the importance of new information for the evaluation period 1990Q2-2010Q3. Our results show that the logarithmic score of the predictive densities for U.S. GDP increase almost monotonically as new information arrives during the quarter. While the best performing model class is changing during the quarter, the density nowcasts from our combination framework is always performing well both in terms of logarithmic scores and calibration tests. The density combination approach is superior to a simple model selection strategy and also performs better in terms of point forecast evaluation than standard point forecast combinations.JEL-codes: C32, C52, C53, E37, E52.

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“…Jore, Mitchell and Vahey (2009) [JMV] demonstrate that a recursive weight density combination strategy, using the logarithmic scores of the component forecast densities, produces well-calibrated ensemble densities.…”

Section: Introductionmentioning

confidence: 99%

Real-Time Inflation Forecast Densities from Ensemble Phillips Curves

et al. 2010

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A popular macroeconomic forecasting strategy takes combinations across many models to hedge against model instabilities of unknown timing; see (among others) Stock and Watson (2004) and Clark and McCracken (2009). In this paper, we examine the effectiveness of recursive-weight and equal-weight combination strategies for density forecasting using a time-varying Phillips curve relationship between inflation and the output gap. The densities reflect the uncertainty across a large number of models using many statistical measures of the output gap, allowing for a single structural break of unknown timing. We use real-time data for the US, Australia, New Zealand and Norway. Our main finding is that the recursive-weight strategy performs well across the real-time data sets, consistently giving well-calibrated forecast densities. The equal-weight strategy generates poorly-calibrated forecast densities for the US and Australian samples. There is little difference between the two strategies for our New Zealand and Norwegian data. We also find that the ensemble modeling approach performs more consistently with real-time data than with revised data in all four countries.

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Combining forecast densities from VARs with uncertain instabilities

Cited by 167 publications

References 40 publications

Time-varying combinations of predictive densities using nonlinear filtering

Time-varying combinations of predictive densities using nonlinear filtering

Nowcasting GDP in Real-Time: A Density Combination Approach

Real-Time Inflation Forecast Densities from Ensemble Phillips Curves

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