scholarly journals BAYESIAN FORECAST COMBINATION IN VAR-DSGE MODELS

2017 ◽  
Author(s):  
Kuo-Hsuan Chin ◽  
Xue Li
Keyword(s):  
Dsge Models ◽  
Forecast Combination ◽  
Bayesian Forecast

Bayesian forecast combination in VAR-DSGE models

2019 ◽  
Vol 59 ◽  
pp. 278-298 ◽  
Author(s):  
Kuo-Hsuan Chin ◽  
Xue Li
Keyword(s):  
Dsge Models ◽  
Forecast Combination ◽  
Bayesian Forecast

scholarly journals SIMPLE BAYESIAN FORECAST COMBINATION

2020 ◽  
Vol 15 (04) ◽  
pp. 2050016
Author(s):  
PHILIP HANS FRANSES
Keyword(s):  
The Netherlands ◽  
Diffusion Models ◽  
Ease Of Use ◽  
Forecast Combination ◽  
Bayesian Forecast ◽  
Daily Data ◽  
Combined Forecasts

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.

scholarly journals Forecasting virus diffusion with simple Bayesian forecast combination

2020 ◽  
Author(s):  
Philip Hans Franses
Keyword(s):  
The Netherlands ◽  
Diffusion Models ◽  
Ease Of Use ◽  
Forecast Combination ◽  
Bayesian Forecast ◽  
Daily Data ◽  
Combined Forecasts

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.

Bayesian Estimation of DSGE Models

2015 ◽  
Author(s):  
Edward P. Herbst ◽  
Frank Schorfheide
Keyword(s):  
Monte Carlo ◽  
Sequential Monte Carlo ◽  
Likelihood Function ◽  
Academic Research ◽  
Dynamic Stochastic General Equilibrium ◽  
Computational Techniques ◽  
Dsge Models ◽  
Monte Carlo Techniques ◽  
Dynamic Stochastic ◽  
Theoretical Foundations

Dynamic stochastic general equilibrium (DSGE) models have become one of the workhorses of modern macroeconomics and are extensively used for academic research as well as forecasting and policy analysis at central banks. This book introduces readers to state-of-the-art computational techniques used in the Bayesian analysis of DSGE models. The book covers Markov chain Monte Carlo techniques for linearized DSGE models, novel sequential Monte Carlo methods that can be used for parameter inference, and the estimation of nonlinear DSGE models based on particle filter approximations of the likelihood function. The theoretical foundations of the algorithms are discussed in depth, and detailed empirical applications and numerical illustrations are provided. The book also gives invaluable advice on how to tailor these algorithms to specific applications and assess the accuracy and reliability of the computations. The book is essential reading for graduate students, academic researchers, and practitioners at policy institutions.

Estimating DSGE Models with Long Memory Dynamics

2008 ◽  
Author(s):  
Gianluca Moretti ◽  
Giulio Nicoletti
Keyword(s):  
Long Memory ◽  
Dsge Models
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