DP4033 | Panel Index VAR Models: Specification, Estimation, Testing and Leading Indicators

Publication Date

23/08/2003

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Abstract

This Paper proposes a method to conduct inference in panel VAR models with cross-unit interdependencies and time variations in the coefficients. The set-up used is Bayesian, and Markov chain Monte Carlo (MCMC) methods are used to estimate the posterior distribution of the features of interest. The model is re-parameterized to resemble an observable index model and specification searches are discussed. The approach can be used to construct multi-unit forecasts, leading indicators and to conduct policy analysis in multi-unit set-ups. The methodology is employed to construct leading indicators for inflation and GDP growth in the euro area.