Filtering and Prediction in Noncausal Processes

Christian Gourieroux and Joann Jasiak

No 2014-15, Working Papers from Center for Research in Economics and Statistics

Abstract: This paper revisits the filtering and prediction in noncausal and mixed autoregressive processes and provides a simple alternative set of methods that are valid for processes with infinite variances. The prediction method provides complete predictive densities and prediction intervals at any finite horizon H, for univariate and multivariate processes. It is based on an unobserved component representation of noncausal processes. The filtering procedure for the unobserved components is provided along with a simple back-forecasting estimator for the parameters of noncausal and mixed models and a simulation algorithm for noncausal and mixed autoregressive processes. The approach is illustrated by simulations

Keywords: Noncausal Process; Nonlinear Prediction; Filtering; Look-Ahead Estimator; Speculative Bubble; Technical Analysis (search for similar items in EconPapers)
JEL-codes: C14 G23 G32 (search for similar items in EconPapers)
Pages: 49
Date: 2014-04
New Economics Papers: this item is included in nep-ecm, nep-ets, nep-for and nep-ore
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Citations: View citations in EconPapers (1)

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