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Noncausal vector autoregression

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  • Lanne, Markku

    ()
    (Department of Economics, and HECER, University of Helsinki)

  • Saikkonen, Pentti

    ()
    (Department of Mathematics and Statistics, and HECER, University of Helsinki)

Abstract

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 importance in empirical economic research, which currently uses 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. This is emphasized in the paper by noting that noncausality is closely related to the notion of nonfundamentalness, under which structural economic shocks cannot be recovered from an estimated causal VAR model. As detecting nonfundamentalness is therefore of great importance, we propose a procedure for discriminating between causality and noncausality that can be seen as a test of nonfundamentalness. The methods are illustrated with applications to fiscal foresight and the term structure of interest rates.

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Bibliographic Info

Paper provided by Bank of Finland in its series Research Discussion Papers with number 18/2009.

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Length: 63 pages
Date of creation: 12 Aug 2009
Date of revision:
Handle: RePEc:hhs:bofrdp:2009_018

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Postal: Bank of Finland, P.O. Box 160, FI-00101 Helsinki, Finland
Web page: http://www.suomenpankki.fi/en/
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Related research

Keywords: elliptic distribution; fiscal foresight; maximum likelihood estimation; noncausal; nonfundamentalness; non-Gaussian; term structure of interest rates;

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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.
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Citations

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Cited by:
  1. Lanne, Markku & Luoto, Jani & Saikkonen, Pentti, 2010. "Optimal Forecasting of Noncausal Autoregressive Time Series," MPRA Paper 23648, University Library of Munich, Germany.
  2. Lof, Matthijs, 2011. "GMM estimation with noncausal instruments under rational expectations," MPRA Paper 35536, University Library of Munich, Germany.
  3. Lanne, Markku & Saikkonen, Pentti, 2010. "Noncausal autoregressions for economic time series," MPRA Paper 32943, University Library of Munich, Germany.
  4. Paul Beaudry & Franck Portier, 2013. "News Driven Business Cycles: Insights and Challenges," NBER Working Papers 19411, National Bureau of Economic Research, Inc.
  5. Nyberg, Henri & Saikkonen, Pentti, 2014. "Forecasting with a noncausal VAR model," Computational Statistics & Data Analysis, Elsevier, vol. 76(C), pages 536-555.
  6. Saikkonen, Pentti & Sandberg , Rickard, 2013. "Testing for a unit root in noncausal autoregressive models," Research Discussion Papers 26/2013, Bank of Finland.
  7. Lof Matthijs, 2013. "Noncausality and asset pricing," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 17(2), pages 211-220, April.
  8. Markku Lanne & Jani Luoto, 2014. "Noncausal Bayesian Vector Autoregression," CREATES Research Papers 2014-07, School of Economics and Management, University of Aarhus.

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