Department of Economics and Business Economics

Noncausal vector autoregression

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  • M. Lanne
  • P. Saikkonen, University of Helsinki
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.
Original languageEnglish
JournalEconometric Theory
Volume29
Issue3
Pages (from-to)447-481
Number of pages35
ISSN0266-4666
DOIs
Publication statusPublished - 1 Jun 2013

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