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Forecasting with Bayesian Vector Autoregression

In: Handbook of Economic Forecasting

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  • Karlsson, Sune

Abstract

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.

Suggested Citation

  • Karlsson, Sune, 2013. "Forecasting with Bayesian Vector Autoregression," Handbook of Economic Forecasting, in: G. Elliott & C. Granger & A. Timmermann (ed.), Handbook of Economic Forecasting, edition 1, volume 2, chapter 0, pages 791-897, Elsevier.
    • Handle: RePEc:eee:ecofch:2-791
    DOI: 10.1016/B978-0-444-62731-5.00015-4
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    More about this item

    Keywords

    Markov chain Monte Carlo; Structural VAR; Cointegration; Conditional forecasts; Time-varying parameters; Stochastic volatility; Model selection; Large VAR;
    All these keywords.

    JEL classification:

    • C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General
    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods

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