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ESTIMATING MULTICOUNTRY VAR MODELS *
Author(s) -
Canova Fabio,
Ciccarelli Matteo
Publication year - 2009
Publication title -
international economic review
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.658
H-Index - 86
eISSN - 1468-2354
pISSN - 0020-6598
DOI - 10.1111/j.1468-2354.2009.00554.x
Subject(s) - markov chain monte carlo , autoregressive model , bayesian probability , econometrics , bayesian vector autoregression , vector autoregression , computer science , univariate , impulse response , monte carlo method , curse of dimensionality , markov chain , mathematics , statistics , multivariate statistics , machine learning , artificial intelligence , mathematical analysis
This article presents a method to estimate the coefficients, to test specification hypotheses, and to conduct policy exercises in multicountry Vector Autoregressive (VAR) models with cross‐unit interdependencies, unit‐specific dynamics, and time variations in the coefficients. The framework of analysis is Bayesian: A prior flexibly reduces the dimensionality of the model and puts structure on the time variations, Markov chain Monte Carlo (MCMC) methods are used to obtain posterior distributions, and marginal likelihoods to check the fit of various specifications. Impulse responses and conditional forecasts are obtained with the output of an MCMC routine. The transmission of certain shocks across countries is analyzed.