Chapter 15. Forecasting with Bayesian Vector Autoregression

This chapter reviews Bayesian methods for inference and forecasting with VAR models. Bayesian inference and, by extension, forecasting depends on numerical methods for simulating from the posterior distribution of the parameters and special attention is given to the implementation of the simulation algorithm.

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Year of publication:	2013
Authors:	Karlsson, Sune
Published in:	Handbook of economic forecasting ; Volume 2. - Amsterdam : Elsevier North Holland, ISBN 978-0-444-62732-2. - 2013, p. 791-897
Subject:	Markov chain Monte Carlo \| Structural VAR \| Cointegration \| Conditional forecasts \| Time-varying parameters \| Stochastic volatility \| Model selection \| Large VAR \| VAR-Modell \| VAR model \| Prognoseverfahren \| Forecasting model \| Monte-Carlo-Simulation \| Monte Carlo simulation \| Markov-Kette \| Markov chain \| Theorie \| Theory \| Zeitreihenanalyse \| Time series analysis \| Kointegration \| Bayes-Statistik \| Bayesian inference \| Schätzung \| Estimation \| Wirtschaftsprognose \| Economic forecast \| Volatilität \| Volatility

Type of publication:	Article
Language:	English
Other identifiers:	10.1016/B978-0-444-62731-5.00015-4 [DOI]
Source:	ECONIS - Online Catalogue of the ZBW

Persistent link: https://ebvufind01.dmz1.zbw.eu/10014025233