Forecasting with High‐Dimensional Panel VARs

Gary Koop and Dimitris Korobilis

Oxford Bulletin of Economics and Statistics, 2019, vol. 81, issue 5, 937-959

Abstract: This paper develops methods for estimating and forecasting in Bayesian panel vector autoregressions of large dimensions with time‐varying parameters and stochastic volatility. We exploit a hierarchical prior that takes into account possible pooling restrictions involving both VAR coefficients and the error covariance matrix, and propose a Bayesian dynamic learning procedure that controls for various sources of model uncertainty. We tackle computational concerns by means of a simulation‐free algorithm that relies on analytical approximations to the posterior. We use our methods to forecast inflation rates in the eurozone and show that these forecasts are superior to alternative methods for large vector autoregressions.

Date: 2019
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Citations: View citations in EconPapers (10)

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https://doi.org/10.1111/obes.12303

Related works:
Working Paper: Forecasting with High-Dimensional Panel VARs (2018)
Working Paper: Forecasting with High-Dimensional Panel VARs (2018)
Working Paper: Forecasting with High-Dimensional Panel VARs (2018)
Working Paper: Forecasting With High Dimensional Panel VARs (2015)
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Persistent link: https://EconPapers.repec.org/RePEc:bla:obuest:v:81:y:2019:i:5:p:937-959

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