Voisin, Elisa and Hecq, Alain (2019): Forecasting bubbles with mixed causal-noncausal autoregressive models.
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Abstract
This paper investigates one-step ahead density forecasts of mixed causal-noncausal models. It analyses and compares two data-driven approaches. The paper focuses on explosive episodes and therefore on predicting turning points of bubbles. Guidance in using these approximation methods are presented with the suggestion of using both of the approaches as they jointly carry more information. The analysis is illustrated with an application on Nickel prices.
Item Type: | MPRA Paper |
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Original Title: | Forecasting bubbles with mixed causal-noncausal autoregressive models |
Language: | English |
Keywords: | Noncausal models, forecasting, predictive densities, bubbles, simulations-based forecasts |
Subjects: | C - Mathematical and Quantitative Methods > C2 - Single Equation Models ; Single Variables > C22 - Time-Series Models ; Dynamic Quantile Regressions ; Dynamic Treatment Effect Models ; Diffusion Processes C - Mathematical and Quantitative Methods > C5 - Econometric Modeling > C53 - Forecasting and Prediction Methods ; Simulation Methods C - Mathematical and Quantitative Methods > C5 - Econometric Modeling > C58 - Financial Econometrics |
Item ID: | 96350 |
Depositing User: | Elisa Voisin |
Date Deposited: | 08 Oct 2019 09:04 |
Last Modified: | 28 May 2024 07:40 |
References: | Andrews, B., Davis, R., and Breidt, J. (2006). Maximum likelihood estimation for all-pass time series models.Journal of Multivariate Analysis,97(7), 1638–1659. Bec, F., Bohn Nielsen, H., and Saïdi, S. (2019). Mixed causal noncausal autoregressions: Bimodality issues in estimation and unit root testing(Tech. Rep.). Fries, S. (2018). Conditional moments of anticipative α-stable markov processes. arXiv preprint arXiv:1805.05397. Fries, S., and Zakoïan, J.-M. (2019). Mixed causal-noncausal AR processes and the modelling of explosive bubbles.Econometric Theory, 1–37. Gouriéroux, C., Hencic, A., and Jasiak, J. (2018). Forecast performance in noncausal MAR(1, 1) processes. Gouriéroux, C., and Jasiak, J. (2016). Filtering, prediction and simulation methods for noncausal processes. Journal of Time Series Analysis,37(3), 405–430. Gouriéroux, C., and Jasiak, J. (2018). Misspecification of noncausal order in autoregressive processes. Journal of Econometrics,205(1), 226–248. Gouriéroux, C., Jasiak, J., and Monfort, A. (2016). Stationary bubble equilibria in rational expectation models. CREST Working Paper. Paris, France: Centre de Recherche en Economie et Statistique. Gouriéroux, C., and Zakoïan, J.-M. (2013). Explosive bubble modelling by noncausal process. CREST. Paris, France: Centre de Recherche en Economie et Statistique. Gouriéroux, C., and Zakoïan, J.-M. (2017). Local explosion modelling by non-causal process.Journal of the Royal Statistical Society: Series B(Statistical Methodology),79(3), 737–756. Hecq, A., Lieb, L., and Telg, S. (2016). Identification of mixed causal-noncausal models in finite samples.Annals of Economics and Statistics/Annales d’ ́Economie et de Statistique(123/124), 307–331. Hecq, A., Lieb, L., and Telg, S. (2017). Simulation, estimation and selection of mixed causal-noncausal autoregressive models: The MARX package. Hecq, A., Telg, S., and Lieb, L. (2017). Do seasonal adjustments induce noncausal dynamics in inflation rates? Econometrics,5(4), 48. Hencic, A., and Gouriéroux, C. (2015). Noncausal autoregressive model inapplication to bitcoin/ exchange rates. In Econometrics of risk (pp.17–40). Springer. Karapanagiotidis, P. (2014). Dynamic modeling of commodity futures prices. MPRA Paper 56805, University Library of Munich, Germany. Lanne, M., Luoto, J., and Saikkonen, P. (2012). Optimal forecasting of non-causal autoregressive time series. International Journal of Forecasting,28(3), 623–631. Lanne, M., Nyberg, H., and Saarinen, E. (2012). Does noncausality help in forecasting economic time series? Economics Bulletin. Lanne, M., and Saikkonen, P. (2011). Noncausal autoregressions for economic time series. Journal of Time Series Econometrics,3(3). Lof, M., and Nyberg, H. (2017). Noncausality and the commodity currency hypothesis. Energy Economics,65, 424–433. |
URI: | https://mpra.ub.uni-muenchen.de/id/eprint/96350 |
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Forecasting bubbles with mixed causal-noncausal autoregressive models. (deposited 15 Mar 2019 16:41)
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