JEŘÁBEK, Tomáš a Radka ŠPERKOVÁ. A Predictive Likelihood Approach to Bayesian Averaging. Acta Univ Agric Silvic Mendel Brun. Praha: Mendel University Press, roč. 63, č. 4, s. 1269-1276. ISSN 1211-8516. doi:10.11118/actaun201563041269. 2015.
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Základní údaje
Originální název A Predictive Likelihood Approach to Bayesian Averaging
Autoři JEŘÁBEK, Tomáš (203 Česká republika, garant, domácí) a Radka ŠPERKOVÁ (203 Česká republika).
Vydání Acta Univ Agric Silvic Mendel Brun, Praha, Mendel University Press, 2015, 1211-8516.
Další údaje
Originální jazyk angličtina
Typ výsledku Článek v odborném periodiku
Obor 50200 5.2 Economics and Business
Stát vydavatele Česká republika
Utajení není předmětem státního či obchodního tajemství
Kód RIV RIV/04274644:_____/15:#0000041
Organizační jednotka Vysoká škola finanční a správní
Doi http://dx.doi.org/10.11118/actaun201563041269
Klíčová slova česky predictive likelihood; density forecasts; Bayesian averaging; Bayesian VAR model
Klíčová slova anglicky predictive likelihood; density forecasts; Bayesian averaging; Bayesian VAR model
Štítky AR 2016-2017, RIV_ne, SCOPUS, xJ3
Příznaky Recenzováno
Změnil Změnil: Mgr. Tomáš Jeřábek, Ph.D., MBA, učo 29123. Změněno: 8. 6. 2018 10:01.
Anotace
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.
Anotace česky
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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