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

Language

English

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

DOI

http://dx.doi.org/10.11118/actaun201563041269

Keywords (in Czech)

predictive likelihood; density forecasts; Bayesian averaging; Bayesian VAR model

Keywords in English

predictive likelihood; density forecasts; Bayesian averaging; Bayesian VAR model

Tags

AR 2016-2017, RIV_ne, SCOPUS, xJ3

Tags

Reviewed
Změněno: 8/6/2018 10:01, Mgr. Tomáš Jeřábek, Ph.D., MBA

Abstract

ORIG CZ

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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