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Optimal forecasting of noncausal autoregressive time series

Markku Lanne, Jani Luoto and Pentti Saikkonen ()

International Journal of Forecasting, 2012, vol. 28, issue 3, 623-631

Abstract: 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 for forecasting such time series because the prediction problem is generally nonlinear and therefore no analytic solution is 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 US inflation demonstrates the importance of allowing for noncausality in improving point and density forecasts.

Keywords: Noncausal time series; Non-Gaussian time series; Point forecast; Density forecast; Inflation (search for similar items in EconPapers)
Date: 2012
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Working Paper: Optimal Forecasting of Noncausal Autoregressive Time Series (2010) Downloads
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DOI: 10.1016/j.ijforecast.2011.08.003

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Handle: RePEc:eee:intfor:v:28:y:2012:i:3:p:623-631