Korobilis, D (2016) Prior selection for panel vector autoregressions. Computational Statistics and Data Analysis, 101 (C). pp. 110-120. DOI https://doi.org/10.1016/j.csda.2016.02.011
Korobilis, D (2016) Prior selection for panel vector autoregressions. Computational Statistics and Data Analysis, 101 (C). pp. 110-120. DOI https://doi.org/10.1016/j.csda.2016.02.011
Korobilis, D (2016) Prior selection for panel vector autoregressions. Computational Statistics and Data Analysis, 101 (C). pp. 110-120. DOI https://doi.org/10.1016/j.csda.2016.02.011
Abstract
Bayesian shrinkage priors have been very popular in estimating vector autoregressions (VARs) of possibly large dimensions. Many of these priors are not appropriate for multi-country settings, as they cannot account for the type of restrictions typically met in panel vector autoregressions (PVARs). With this in mind, new parametric and semi-parametric priors for PVARs are proposed, which perform valuable shrinkage in large dimensions and also allow for soft clustering of variables or countries which are homogeneous. The implication of these new priors for modelling interdependencies and heterogeneities among different countries in a panel VAR setting, is discussed. Monte Carlo evidence and an empirical forecasting exercise show clear and important gains from the new priors compared to existing popular priors for VARs and PVARs.
Item Type: | Article |
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Uncontrolled Keywords: | Bayesian model selection; Shrinkage; Spike and slab priors; Forecasting; Large vector autoregression |
Subjects: | H Social Sciences > HB Economic Theory |
Divisions: | Faculty of Social Sciences Faculty of Social Sciences > Essex Business School |
SWORD Depositor: | Unnamed user with email elements@essex.ac.uk |
Depositing User: | Unnamed user with email elements@essex.ac.uk |
Date Deposited: | 23 Nov 2016 12:42 |
Last Modified: | 16 May 2024 17:46 |
URI: | http://repository.essex.ac.uk/id/eprint/17944 |
Available files
Filename: MIXLASSO_PVAR.pdf