scholarly journals Bayesian Forecast Combination for VAR Models

Author(s):  
Michael Andersson ◽  
Sune Karlsson
2020 ◽  
Vol 15 (04) ◽  
pp. 2050016
Author(s):  
PHILIP HANS FRANSES

In this paper, it is proposed to combine the forecasts using a simple Bayesian forecast combination algorithm. The algorithm is applied to forecasts from three non-nested diffusion models for S shaped processes like virus diffusion. An illustration to daily data on first-wave cumulative Covid-19 cases in the Netherlands shows the ease of use of the algorithm and the accuracy of the newly combined forecasts.


2020 ◽  
Author(s):  
Philip Hans Franses

AbstractThere are various diffusion models for S shaped processes like virus diffusion and these models are typically not nested. In this note it is proposed to combine the forecasts using a simple Bayesian forecast combination algorithm. An illustration to daily data on cumulative Covid-19 cases in the Netherlands shows the ease of use of the algorithm and the accuracy of the thus combined forecasts.


2019 ◽  
Vol 59 ◽  
pp. 278-298 ◽  
Author(s):  
Kuo-Hsuan Chin ◽  
Xue Li

2017 ◽  
pp. 88-110 ◽  
Author(s):  
S. Drobyshevsky ◽  
P. Trunin ◽  
A. Bozhechkova ◽  
E. Gorunov ◽  
D. Petrova

The article investigates the Bank of Russia information policy using a new approach to measuring information effects on Russian data, including the analysis of the tonality of news reports, as well as internet users’ queries on Google. The efficiency of regulator’s information signals is studied using EGARCH-, VAR- models, as well as nonparametric tests. The authors conclude that the regulator communicates effectively in terms of the predictability of interest rate policy, the degree to which information signals affect the money and foreign exchange markets.


2007 ◽  
Vol 9 (2) ◽  
pp. 39-54 ◽  
Author(s):  
Victor de la Pena ◽  
Ricardo Rivera ◽  
Jesus Ruiz-Mata

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