Forecasting with Bayesian Vector Autoregression
Sune Karlsson, Department of Statistics, Örebro University School of Business, Örebro University
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.
Markov chain Monte Carlo; Structural VAR; Cointegration; Conditional forecasts; Time-varying parameters; Stochastic volatility; Model selection; Large VAR
Vector autoregressions (VARs) have become the workhorse model for macroeconomic forecasting. ...