Forecasting with Bayesian Vector Autoregressions

Prepared for the Handbook of Economic Forecasting, vol 2 <p> 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 spe- cial attention is given to the implementation of the simulation algorithm.

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Year of publication:	2012-08-04
Authors:	Karlsson, Sune
Institutions:	Handelshögskolan, Örebro Universitet
Subject:	Markov chain Monte Carlo \| Structural VAR \| Cointegration \| Condi- tional forecasts \| Time-varying parameters \| Stochastic volatility \| Model selection \| Large VAR

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Extent:	application/pdf
Series:	Working Papers.
Type of publication:	Book / Working Paper
Notes:	The text is part of a series Working Papers Number 2012:12 105 pages
Classification:	C11 - Bayesian Analysis ; C32 - Time-Series Models ; C53 - Forecasting and Other Model Applications
Source:	RePEc - Research Papers in Economics

Persistent link: https://www.econbiz.de/10011019076