Bayesian Model Selection and Forecasting in Noncausal Autoregressive Models
Arto Luoma and
MPRA Paper from University Library of Munich, Germany
In this paper, we propose a Bayesian estimation and prediction procedure for noncausal autoregressive (AR) models. Specifically, we derive the joint posterior density of the past and future errors and the parameters, which gives posterior predictive densities as a byproduct. We show that the posterior model probability provides a convenient model selection criterion and yields information on the probabilities of the alternative causal and noncausal specifications. This is particularly useful in assessing economic theories that imply either causal or purely noncausal dynamics. As an empirical application, we consider U.S. inflation dynamics. A purely noncausal AR model gets the strongest support, but there is also substantial evidence in favor of other noncausal AR models allowing for dependence on past inflation. Thus, although U.S. inflation dynamics seem to be dominated by expectations, the backward-looking component is not completely missing. Finally, the noncausal specifications seem to yield inflation forecasts which are superior to those from alternative models especially at longer forecast horizons.
Keywords: Noncausality; Autoregression; Bayesian model selection; Forecasting (search for similar items in EconPapers)
JEL-codes: C11 C22 C52 E31 (search for similar items in EconPapers)
New Economics Papers: this item is included in nep-cba, nep-ecm, nep-ets, nep-for and nep-ore
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Journal Article: BAYESIAN MODEL SELECTION AND FORECASTING IN NONCAUSAL AUTOREGRESSIVE MODELS (2012)
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Persistent link: https://EconPapers.repec.org/RePEc:pra:mprapa:23646
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