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@article{4962, author = {Jeřábek, Tomáš and Šperková, Radka}, article_location = {Praha}, article_number = {4}, doi = {http://dx.doi.org/10.11118/actaun201563041269}, keywords = {predictive likelihood; density forecasts; Bayesian averaging; Bayesian VAR model}, language = {eng}, issn = {1211-8516}, journal = {Acta Univ Agric Silvic Mendel Brun}, note = {AR 2016/2017 - odesláno do RIV. (duben 2017)}, title = {A Predictive Likelihood Approach to Bayesian Averaging}, volume = {63}, year = {2015} }
TY - JOUR ID - 4962 AU - Jeřábek, Tomáš - Šperková, Radka PY - 2015 TI - A Predictive Likelihood Approach to Bayesian Averaging JF - Acta Univ Agric Silvic Mendel Brun VL - 63 IS - 4 SP - 1269-1276 EP - 1269-1276 PB - Mendel University Press SN - 12118516 N1 - AR 2016/2017 - odesláno do RIV. (duben 2017) KW - predictive likelihood KW - density forecasts KW - Bayesian averaging KW - Bayesian VAR model N2 - Multivariate time series forecasting is applied in a wide range of economic activities related to regional competitiveness and is the basis of risk management analysis. In this paper we combine multivariate density forecasts. The performance of models is identified using historical dates including domestic economy and foreign economy, which is represented by countries of the Eurozone. Because forecast accuracy of observed models are different, the weighting scheme based on the predictive likelihood, the trace of past MSE matrix, model ranks are used to combine the models. The equal-weight scheme is used as a simple combination scheme. The results show that optimally combined densities are comparable to the best individual models. ER -
JEŘÁBEK, Tomáš and Radka ŠPERKOVÁ. A Predictive Likelihood Approach to Bayesian Averaging. \textit{Acta Univ Agric Silvic Mendel Brun}. Praha: Mendel University Press, 2015, vol.~63, No~4, p.~1269-1276. ISSN~1211-8516. Available from: https://dx.doi.org/10.11118/actaun201563041269.
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