Markku Lanne
;
Arto Luoma
;
Jani Luoto
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bayesian model selection and forecasting in noncausal autoregressive models (replication data)

In this paper, we propose a Bayesian estimation and forecasting procedure for noncausal autoregressive (AR) models. Specifically, we derive the joint posterior density of the past and future errors and the parameters, yielding predictive densities as a by-product. We show that the posterior model probabilities provide a convenient model selection criterion in discriminating between alternative causal and noncausal specifications. As an empirical application, we consider US inflation. The posterior probability of noncausality is found to be high-over 98%. Furthermore, the purely noncausal specifications yield more accurate inflation forecasts than alternative causal and noncausal AR models.

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Suggested Citation

Lanne, Markku; Luoma, Arto; Luoto, Jani (2010): BAYESIAN MODEL SELECTION AND FORECASTING IN NONCAUSAL AUTOREGRESSIVE MODELS (replication data). Version: 1. Journal of Applied Econometrics. Dataset. https://jda-test.zbw.eu/dataset/bayesian-model-selection-and-forecasting-in-noncausal-autoregressive-models?activity_id=51093da9-7894-4bbf-b1a9-d31c7b15da8d