English, Article, Journal or magazine article edition: Estimating Multi-country VAR models Fabio Canova; Matteo Ciccarelli

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Bookmark: http://trove.nla.gov.au/version/191797
Physical Description
  • preprint
Language
  • English

Edition details

Title
  • Estimating Multi-country VAR models
Author
  • Fabio Canova
  • Matteo Ciccarelli
Physical Description
  • preprint
Notes
  • This paper describes a methodology to estimate the coefficients, to test specification hypotheses and to conduct policy exercises in multi-country 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; 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 MCMC routine. The transmission of certain shocks across G7 countries is analyzed. JEL Classification: C3; C5; E5.
  • RePEc:ecb:ecbwps:20060603
  • This paper describes a methodology to estimate the coefficients, to test specification hypotheses and to conduct policy exercises in multi-country 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; 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 MCMC routine. The transmission of certain shocks across countries is analyzed.
  • RePEc:upf:upfgen:920
  • This paper presents a method to estimate the coefficients, to test specification hypotheses and to conduct policy exercises in multi-country 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; 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 a MCMC routine. The transmission of certain shocks across countries is analyzed.
  • Multi-country VAR, Markov Chain Monte Carlo methods, Flexible priors, International transmission
  • RePEc:prt:dpaper:7_2007
Language
  • English
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