Forecasting with High-Dimensional Panel VARs

Gary Koop and Dimitris Korobilis

Working Paper series from Rimini Centre for Economic Analysis

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 an analytical approximation of the posterior distribution. We use our methods to forecast inflation rates in the eurozone and show that forecasts from our flexible specification are superior to alternative methods for large vector autoregressions.

Keywords: Panel VAR; inflation forecasting; Bayesian; time-varying parameter model (search for similar items in EconPapers)
Date: 2018-05
New Economics Papers: this item is included in nep-ecm, nep-ets, nep-for and nep-ore
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (6)

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http://rcea.org/RePEc/pdf/wp18-20.pdf

Related works:
Journal Article: Forecasting with High‐Dimensional Panel VARs (2019)
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:rim:rimwps:18-20

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