Optimal Forecasting of Noncausal Autoregressive Time Series
By Markku Lanne, Jani Luoto, Pentti Saikkonen. 2012
64 citations recorded by the source corpus.
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.
Subfields: Economics and Econometrics, Management Science and Operations Research
View Paper: https://www.semanticscholar.org/paper/7dfc726eba3c189abf56b88682b5b5d4a717458d
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In Economics and Econometrics
- Christian Gouriéroux
- Joann Jasiak
- Pierre Perron
- Sébastien-Yves Laurent
- Zhongjun Qu
- John M. Maheu
- Xin Jin
- Luc Bauwens
In Management Science and Operations Research
Citations in the corpus
- Filtering, Prediction and Simulation Methods for Noncausal Processes (2016, methodology): “37: 405–430 (2016) Copyright © 2015 Wiley Publishing Ltd wileyonlinelibrary.com/journal/jtsa DOI: 10.1111/jtsa.12165 (Lanne et al., 2012), or the estimation of long sequences "TC1; : : : ; "TCM , considered as the additional unknown ‘parameters’ in a Bayesian framework (Lanne et al., 2012).”
- NONCAUSAL VECTOR AUTOREGRESSION (2013, unknown): “(12) Clearly, τ (λ) ≥ 1 with equality if and only if t is Gaussian.”
- Noncausal Autoregressions for Economic Time Series (2010, unknown): “Note, however, that in noncausal autoregressive models the prediction problem is, in general, nonlinear (see Rosenblatt (2000, Corollary 5.4.2, and Lanne et al. (2010)) and, therefore, Et( t+j) cannot be computed in a simple fashion (see Lanne et al. (2010)).”