NONCAUSAL VECTOR AUTOREGRESSION
Markku Lanne and
Pentti Saikkonen ()
Econometric Theory, 2013, vol. 29, issue 3, 447-481
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 that 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.
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Working Paper: Noncausal Vector Autoregression (2010)
Working Paper: Noncausal vector autoregression (2009)
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Persistent link: https://EconPapers.repec.org/RePEc:cup:etheor:v:29:y:2013:i:03:p:447-481_00
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