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Noncausal Vector Autoregression

  • Lanne, Markku
  • Saikkonen, Pentti

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.

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Paper provided by University Library of Munich, Germany in its series MPRA Paper with number 23717.

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Date of creation: Apr 2010
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Handle: RePEc:pra:mprapa:23717
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  24. Lanne, Markku & Luoto, Jani & Saikkonen, Pentti, 2012. "Optimal forecasting of noncausal autoregressive time series," International Journal of Forecasting, Elsevier, vol. 28(3), pages 623-631.
  25. Francq, Christian & Roy, Roch & Zakoian, Jean-Michel, 2005. "Diagnostic Checking in ARMA Models With Uncorrelated Errors," Journal of the American Statistical Association, American Statistical Association, vol. 100, pages 532-544, June.
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  28. Eric M. Leeper & Todd B. Walker & Shu-Chun Susan Yang, 2008. "Fiscal Foresight: Analytics and Econometrics," Caepr Working Papers 2008-013, Center for Applied Economics and Policy Research, Economics Department, Indiana University Bloomington.
  29. Susan Yang, Shu-Chun, 2005. "Quantifying tax effects under policy foresight," Journal of Monetary Economics, Elsevier, vol. 52(8), pages 1557-1568, November.
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