Assessing Forecast Uncertainty : An Information Bayesian Approach
Regardless of the field, forecasts are widely used and yet assessments of the embedded uncertainty—the magnitude of the downside and upside risks of the prediction itself—are often missing. Particularly in policy-making and investment, accounting f...
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| Language: | English en_US |
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World Bank, Washington, DC
2017
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| Online Access: | http://documents.worldbank.org/curated/en/802551502718519493/Assessing-forecast-uncertainty-an-information-Bayesian-approach http://hdl.handle.net/10986/27972 |
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okr-10986-279722021-05-25T10:54:43Z Assessing Forecast Uncertainty : An Information Bayesian Approach Mendez-Ramos, Fabian BAYESIAN ENTROPY DENSITY FORECASTS SCORING RULES CONTINUOUS RANKED PROBABILITY SCORE ECONOMIC GROWTH VOLATILITY RISKS VARIANCE SKEWNESS DECOMPOSITION UNCERTAINTY Regardless of the field, forecasts are widely used and yet assessments of the embedded uncertainty—the magnitude of the downside and upside risks of the prediction itself—are often missing. Particularly in policy-making and investment, accounting for these risks around baseline predictions is of outstanding importance for making better and more informed decisions. This paper introduces a procedure to assess risks associated with a random phenomenon. The methodology assigns probability distributions to baseline-projections of an economic or social random variable—for example gross domestic product growth, inflation, population growth, poverty headcount, and so forth—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 methodology also decomposes the variance and skewness of the predictive distribution accounting for the shares of selected risk factors. The procedure relies on a Bayesian information-theoretical approach, which allows the inclusion of judgment and forecaster expertise. For reliability purposes and transparency, the paper also evaluates the constructed density forecasts assigning a score. The continuous ranked probability score is used to assess the prediction accuracy of elicited density forecasts. The selected score incentivizes the forecaster to provide its true and best predictive distribution. An empirical application to forecast world gross domestic product growth is used to test the Bayesian entropy methodology. Predictive variance and skewness of world gross domestic product growth are associated with ex-ante information of four risk factors: term spreads, absolute deviations of headline inflation targets, energy prices, and the Standard and Poor's 500 index prices. The Bayesian entropy technique is benchmarked with naïve-generated density forecasts that utilize information from historical forecast errors. The results show that the Bayesian density forecasts outperform the naïve-generated benchmark predictions, illustrating the value added of the introduced methodology. 2017-08-24T21:30:31Z 2017-08-24T21:30:31Z 2017-08 Working Paper http://documents.worldbank.org/curated/en/802551502718519493/Assessing-forecast-uncertainty-an-information-Bayesian-approach http://hdl.handle.net/10986/27972 English en_US Policy Research Working Paper;No. 8165 CC BY 3.0 IGO http://creativecommons.org/licenses/by/3.0/igo World Bank World Bank, Washington, DC Publications & Research Publications & Research :: Policy Research Working Paper |
| repository_type |
Digital Repository |
| institution_category |
Foreign Institution |
| institution |
Digital Repositories |
| building |
World Bank Open Knowledge Repository |
| collection |
World Bank |
| language |
English en_US |
| topic |
BAYESIAN ENTROPY DENSITY FORECASTS SCORING RULES CONTINUOUS RANKED PROBABILITY SCORE ECONOMIC GROWTH VOLATILITY RISKS VARIANCE SKEWNESS DECOMPOSITION UNCERTAINTY |
| spellingShingle |
BAYESIAN ENTROPY DENSITY FORECASTS SCORING RULES CONTINUOUS RANKED PROBABILITY SCORE ECONOMIC GROWTH VOLATILITY RISKS VARIANCE SKEWNESS DECOMPOSITION UNCERTAINTY Mendez-Ramos, Fabian Assessing Forecast Uncertainty : An Information Bayesian Approach |
| relation |
Policy Research Working Paper;No. 8165 |
| description |
Regardless of the field, forecasts are
widely used and yet assessments of the embedded
uncertainty—the magnitude of the downside and upside risks
of the prediction itself—are often missing. Particularly in
policy-making and investment, accounting for these risks
around baseline predictions is of outstanding importance for
making better and more informed decisions. This paper
introduces a procedure to assess risks associated with a
random phenomenon. The methodology assigns probability
distributions to baseline-projections of an economic or
social random variable—for example gross domestic product
growth, inflation, population growth, poverty headcount, and
so forth—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 methodology also decomposes the
variance and skewness of the predictive distribution
accounting for the shares of selected risk factors. The
procedure relies on a Bayesian information-theoretical
approach, which allows the inclusion of judgment and
forecaster expertise. For reliability purposes and
transparency, the paper also evaluates the constructed
density forecasts assigning a score. The continuous ranked
probability score is used to assess the prediction accuracy
of elicited density forecasts. The selected score
incentivizes the forecaster to provide its true and best
predictive distribution. An empirical application to
forecast world gross domestic product growth is used to test
the Bayesian entropy methodology. Predictive variance and
skewness of world gross domestic product growth are
associated with ex-ante information of four risk factors:
term spreads, absolute deviations of headline inflation
targets, energy prices, and the Standard and Poor's 500
index prices. The Bayesian entropy technique is benchmarked
with naïve-generated density forecasts that utilize
information from historical forecast errors. The results
show that the Bayesian density forecasts outperform the
naïve-generated benchmark predictions, illustrating the
value added of the introduced methodology. |
| format |
Working Paper |
| author |
Mendez-Ramos, Fabian |
| author_facet |
Mendez-Ramos, Fabian |
| author_sort |
Mendez-Ramos, Fabian |
| title |
Assessing Forecast Uncertainty : An Information Bayesian Approach |
| title_short |
Assessing Forecast Uncertainty : An Information Bayesian Approach |
| title_full |
Assessing Forecast Uncertainty : An Information Bayesian Approach |
| title_fullStr |
Assessing Forecast Uncertainty : An Information Bayesian Approach |
| title_full_unstemmed |
Assessing Forecast Uncertainty : An Information Bayesian Approach |
| title_sort |
assessing forecast uncertainty : an information bayesian approach |
| publisher |
World Bank, Washington, DC |
| publishDate |
2017 |
| url |
http://documents.worldbank.org/curated/en/802551502718519493/Assessing-forecast-uncertainty-an-information-Bayesian-approach http://hdl.handle.net/10986/27972 |
| _version_ |
1764466265853788160 |