J
2015
A Predictive Likelihood Approach to Bayesian Averaging
JEŘÁBEK, Tomáš and Radka ŠPERKOVÁ
Basic information
Original name
A Predictive Likelihood Approach to Bayesian Averaging
Authors
JEŘÁBEK, Tomáš (203 Czech Republic, guarantor, belonging to the institution) and Radka ŠPERKOVÁ (203 Czech Republic)
Edition
Acta Univ Agric Silvic Mendel Brun, Praha, Mendel University Press, 2015, 1211-8516
Other information
Type of outcome
Článek v odborném periodiku
Field of Study
50200 5.2 Economics and Business
Country of publisher
Czech Republic
Confidentiality degree
není předmětem státního či obchodního tajemství
RIV identification code
RIV/04274644:_____/15:#0000041
Organization unit
University of Finance and Administration
Keywords (in Czech)
predictive likelihood; density forecasts; Bayesian averaging; Bayesian VAR model
Keywords in English
predictive likelihood; density forecasts; Bayesian averaging; Bayesian VAR model
V originále
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.
In Czech
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.
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