Nonlinear Fore(Back)Casting and Innovation Filtering for Causal–Noncausal VAR Models.
We show that the mixed causal–noncausal vector autoregressive (VAR) processes satisfy the Markov property in both calendar and reverse time. Based on that property, we introduce closed-form formulas of forward and backward predictive densities for point and interval forecasting and backcasting out-o...
| Publicado en: | Journal of Financial Econometrics Vol. 24; no. 2; pp. 1 - 25 |
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| Autores principales: | , |
| Formato: | Artículo |
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Oxford University Press / USA
2026
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ssf&AN=192849870&site=ehost-live header: @attributes: shortDbName: ssf uiTerm: 192849870 longDbName: Social Sciences Full Text (H.W. Wilson) uiTag: AN controlInfo: bkinfo: jinfo: jid: 14798409 T2Y jtl: Journal of Financial Econometrics issn: 14798409 maglogo: N pubinfo: dt: 2026 vid: 24 iid: 2 pid: 622 pub: Oxford University Press / USA artinfo: ui: 192849870 10.1093/jjfinec/nbag005 ppf: 1 ppct: 24 formats: tig: atl: Nonlinear Fore(Back)Casting and Innovation Filtering for Causal–Noncausal VAR Models. aug: au: Gourieroux, Christian Jasiak, Joann affil: University of Toronto, 150 St. George Street, Toronto, Ontario, M5S 3G7, Canada York University, 4700 Keele Street, Toronto, Ontario, M3J 1P3, Canada su: Forecasting Vector autoregression model Markov processes Distribution (Probability theory) Impulse response Time series analysis Autoregressive models sug: subj: Forecasting Vector autoregression model Markov processes Distribution (Probability theory) Impulse response Time series analysis Autoregressive models keyword: backcasting bubble C14 C53 copyrightHolder:Oxford University Press copyrightYear:2026 G17 generalized covariance (GCov) estimator inLanguage:en mixed causal–noncausal process nonlinear innovations oil price predictive density publisher:Oxford University Press sameAs:https://dx.doi.org/10.1093/jjfinec/nbag005 backcasting bubble C14 C53 copyrightHolder:Oxford University Press copyrightYear:2026 G17 generalized covariance (GCov) estimator inLanguage:en mixed causal–noncausal process nonlinear innovations oil price predictive density publisher:Oxford University Press sameAs:https://dx.doi.org/10.1093/jjfinec/nbag005 ab: We show that the mixed causal–noncausal vector autoregressive (VAR) processes satisfy the Markov property in both calendar and reverse time. Based on that property, we introduce closed-form formulas of forward and backward predictive densities for point and interval forecasting and backcasting out-of-sample. The backcasting formula is used for adjusting the forecast interval to obtain a desired coverage level when the tail quantiles are difficult to estimate. A confidence set for the prediction interval is introduced for assessing the uncertainty due to estimation. We also define new nonlinear past-dependent innovations of mixed causal–noncausal VAR models for impulse response function analysis. Our approach is illustrated by simulations and an application to the joint analysis of oil prices and real gross domestic product (GDP) growth rates. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: N holdings: @attributes: islocal: N |
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