Bayesian Prediction with a Cointegrated Vector Autoregression
AbstractA complete procedure for calculating the joint predictive distribution of future observations based on the cointegrated vector autoregression is presented. The large degree of uncertainty in the choise of the cointegration vectors is incorporated into the analysis through a prior distribution on the cointegration vectors which allows the forecaster to realistically express his beliefs. This prior leads to a form of model averaging where the predictions from the models based on the different cointegration vectors are weighted together in an optimal way. The ideas of Litterman (1980) are adapted for the prior on the short run dynamics with a resulting prior which only depends on a few hyperparameters and is therefore easily specified. A straight forward numerical evaluation of the predictive distribution based on Gibbs sampling is proposed. The prediction procedure is applied to a seven variable system with focus on forecasting the Swedish inflation.
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Bibliographic InfoPaper provided by Sveriges Riksbank (Central Bank of Sweden) in its series Working Paper Series with number 97.
Length: 23 pages
Date of creation: 01 Oct 1999
Date of revision:
Publication status: Published in International Journal of Forecasting, 2001, pages 585-605.
Bayesian; Cointegration; Inflation forecasting; Model averaging; Predictive density;
Find related papers by JEL classification:
- E50 - Macroeconomics and Monetary Economics - - Monetary Policy, Central Banking, and the Supply of Money and Credit - - - General
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- Jansson, Per & Vredin, Anders, 2001. "Forecast-based Monetary Policy in Sweden 1992-1998: A View from Within," Working Paper Series 120, Sveriges Riksbank (Central Bank of Sweden).
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