Optimal Forecasting of Noncausal Autoregressive Time Series
Jani Luoto and
Pentti Saikkonen ()
MPRA Paper from University Library of Munich, Germany
In this paper, we propose a simulation-based method for computing point and density forecasts for univariate noncausal and non-Gaussian autoregressive processes. Numerical methods are needed to forecast such time series because the prediction problem is generally nonlinear and no analytic solution is therefore available. According to a limited simulation experiment, the use of a correct noncausal model can lead to substantial gains in forecast accuracy over the corresponding causal model. An empirical application to U.S. inflation demonstrates the importance of allowing for noncausality in improving point and density forecasts.
Keywords: Noncausal autoregression; density forecast; inflation (search for similar items in EconPapers)
JEL-codes: C22 C53 C63 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: Optimal forecasting of noncausal autoregressive time series (2012)
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Persistent link: https://EconPapers.repec.org/RePEc:pra:mprapa:23648
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