Forecasting Czech GDP using Bayesian dynamic model averaging EL Classification
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F26482789%3A_____%2F18%3AN0000003" target="_blank" >RIV/26482789:_____/18:N0000003 - isvavai.cz</a>
Result on the web
<a href="https://iises.net/international-journal-of-economic-sciences/publication-detail-1721" target="_blank" >https://iises.net/international-journal-of-economic-sciences/publication-detail-1721</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.20472/ES.2018.7.1.004" target="_blank" >10.20472/ES.2018.7.1.004</a>
Alternative languages
Result language
angličtina
Original language name
Forecasting Czech GDP using Bayesian dynamic model averaging EL Classification
Original language description
Forecasting future path of macroeconomic aggregates has become crucial for monetary and fiscal policymakers. Using Czech data, the aim of this paper is to demonstrate the benefits of the Bayesian dynamic averaging and Bayesian Vector Autoregressive Models (BVAR) in forecasting real GDP growth. Estimation of richly parameterized VARs often leads to unstable estimates and inaccurate forecasts in models with many variables. Bayesian inference and proper choice of informative priors offers an effective solution to this problem by shrinking the variance of model parameters. Bayesian dynamic model averaging (DMA) then makes it possible to account for model uncertainty by combining predictive abilities of many competing VAR models considered by a researcher. Since forecasting performance of individual models may vary over time, the DMA can adapt their weights in dynamic and optimal way. It is shown that the application of DMA leads to substantial forecasting gains in forecasting Czech real GDP.
Czech name
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Czech description
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Classification
Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
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OECD FORD branch
50206 - Finance
Result continuities
Project
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Continuities
N - Vyzkumna aktivita podporovana z neverejnych zdroju
Others
Publication year
2018
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
International Journal of Economic Sciences
ISSN
1804-9796
e-ISSN
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Volume of the periodical
7/2018
Issue of the periodical within the volume
1
Country of publishing house
CZ - CZECH REPUBLIC
Number of pages
17
Pages from-to
65-81
UT code for WoS article
000432932500004
EID of the result in the Scopus database
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