Forecasting With High Dimensional Panel VARs

Working Papers from Business School - Economics, University of Glasgow

Abstract: In this paper, we develop econometric methods for estimating large Bayesian timevarying parameter panel vector autoregressions (TVP-PVARs) and use these methods to forecast inflation for euro area countries. Large TVP-PVARs contain huge numbers of parameters which can lead to over-parameterization and computational concerns. To overcome these concerns, we use hierarchical priors which reduce the dimension of the parameter vector and allow for dynamic model averaging or selection over TVP-PVARs of different dimension and different priors. We use forgetting factor methods which greatly reduce the computational burden. Our empirical application shows substantial forecast improvements over plausible alternatives.

Keywords: Panel VAR; inflation forecasting; Bayesian; time-varying parameter model (search for similar items in EconPapers)
Date: 2015-11
New Economics Papers: this item is included in nep-ecm, nep-ets and nep-for
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Citations: View citations in EconPapers (17)

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http://www.gla.ac.uk/media/media_433934_en.pdf (application/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 (2018)
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Persistent link: https://EconPapers.repec.org/RePEc:gla:glaewp:2015_25

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