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Bayesian Analysis of ARMA models using Noninformative Priors

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  • Kleibergen, F.R.
  • Hoek, H.

Abstract

Parameters in AutoRegressive Moving Average (ARMA) models are locally nonidentified, due to the problem of root cancellation. Parameters can be constructed which represent this identification problem. We argue that ARMA parameters should be analyzed conditional on these identifying parameters. Priors exploiting this feature result in regular posteriors, while priors which neglect it result in posteriori favor of nonidentified parameter values. By considering the implicit AR representation of an ARMA model a prior with the desired proporties is obtained. The implicit AR representation also allows to construct easily implemented algorithms to analyze ARMA parameters. As a byproduct, posteriors odds ratios can be computed to compare (nonnested) parsimonious ARMA models. The procedures are applied to two datasets, the (extended) Nelson-Plosser data and monthly observations of US 3-month and 10 year interest rates. For approximately 50% of the series in these two datasets an ARMA model is favored above an AR model.

Suggested Citation

  • Kleibergen, F.R. & Hoek, H., 1995. "Bayesian Analysis of ARMA models using Noninformative Priors," Econometric Institute Research Papers EI 9553-/B, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
    • Handle: RePEc:ems:eureir:1363
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    Cited by:

    1. Kleibergen, F.R. & Paap, R., 1996. "Priors, Posterior Odds and Lagrange Multiplier Statistics in Bayesian Analyses of Cointegration," Econometric Institute Research Papers EI 9668-/A, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.

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