Forecasting with Bayesian Vector Autoregressions

Prepared for the Handbook of Economic Forecasting, vol 2 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
Authors:	Karlsson, Sune
Publisher:	Örebro : Örebro University School of Business
Subject:	Markov chain Monte Carlo \| Structural VAR \| Cointegration \| Condi- tional forecasts \| Time-varying parameters \| Stochastic volatility \| Model selection \| Large VAR

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Series:	Working Paper ; 12/2012
Type of publication:	Book / Working Paper
Type of publication (narrower categories):	Working Paper
Language:	English
Other identifiers:	hdl:10419/244486 [Handle] RePEc:hhs:oruesi:2012_012 [RePEc]
Classification:	C11 - Bayesian Analysis ; C32 - Time-Series Models ; C53 - Forecasting and Other Model Applications
Source:	EconStor

Persistent link: https://www.econbiz.de/10012654382