Noncausal Vector Autoregression
Markku Lanne and
Pentti Saikkonen ()
MPRA Paper from University Library of Munich, Germany
In this paper, we propose a new noncausal vector autoregressive (VAR) model for non-Gaussian time series. The assumption of non-Gaussianity is needed for reasons of identifiability. Assuming that the error distribution belongs to a fairly general class of elliptical distributions, we develop an asymptotic theory of maximum likelihood estimation and statistical inference. We argue that allowing for noncausality is of particular importance in economic applications which currently use only conventional causal VAR models. Indeed, if noncausality is incorrectly ignored, the use of a causal VAR model may yield suboptimal forecasts and misleading economic interpretations. Therefore, we propose a procedure for discriminating between causality and noncausality. The methods are illustrated with an application to interest rate data.
Keywords: Vector autoregression; noncausal time series; non-Gaussian time series (search for similar items in EconPapers)
JEL-codes: C32 C52 E43 (search for similar items in EconPapers)
New Economics Papers: this item is included in nep-ecm, nep-ets and nep-ore
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https://mpra.ub.uni-muenchen.de/23717/1/MPRA_paper_23717.pdf original version (application/pdf)
Journal Article: NONCAUSAL VECTOR AUTOREGRESSION (2013)
Working Paper: Noncausal vector autoregression (2009)
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Persistent link: https://EconPapers.repec.org/RePEc:pra:mprapa:23717
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