Variance and Skewness in Density Predictions : A World GDP Growth Forecast Assessment
The paper introduces a Bayesian cross-entropy forecast (BCEF) procedure to assess the variance and skewness in density forecasting. The methodology decomposes the variance and skewness of the predictive distribution accounting for the shares of selected risk factors. The method assigns probabil...
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| Format: | Working Paper |
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World Bank, Washington, DC
2020
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| Online Access: | http://documents.worldbank.org/curated/en/802551502718519493/Variance-and-Skewness-in-Density-Predictions-A-World-GDP-Growth-Forecast-Assessment http://hdl.handle.net/10986/33116 |
| Summary: | The paper introduces a Bayesian cross-entropy forecast
(BCEF) procedure to assess the variance and skewness in
density forecasting. The methodology decomposes the variance
and skewness of the predictive distribution accounting
for the shares of selected risk factors. The method assigns
probability distributions to baseline-projections of an
economic or social random variable—for example, gross
domestic product growth, inflation, population growth,
poverty headcount, among others—combining ex-post
and ex-ante market information. The generated asymmetric
density forecasts use information derived from surveys on
expectations and implied statistics of predictive models.
The BCEF procedure is applied to produce world GDP
growth forecasts for three-year horizons using information
spanning the period of October 2005–August 2015.
The scores indicate that the BCEF density forecasts are
more accurate and reliable than some naïve—symmetric
and normal distributed confidence interval—predictions,
illustrating the value-added of the introduced methodology. |
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