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Forecasting with a noncausal VAR model

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  • Nyberg, Henri
  • Saikkonen, Pentti

Abstract

Simulation-based forecasting methods for a non-Gaussian noncausal vector autoregressive (VAR) model are proposed. In noncausal autoregressions the assumption of non-Gaussianity is needed for reasons of identifiability. Unlike in conventional causal autoregressions the prediction problem in noncausal autoregressions is generally nonlinear, implying that its analytical solution is unfeasible and, therefore, simulation or numerical methods are required in computing forecasts. It turns out that different special cases of the model call for different simulation procedures. Monte Carlo simulations demonstrate that gains in forecasting accuracy are achieved by using the correct noncausal VAR model instead of its conventional causal counterpart. In an empirical application, a noncausal VAR model comprised of U.S. inflation and marginal cost turns out superior to the best-fitting conventional causal VAR model in forecasting inflation.

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Bibliographic Info

Article provided by Elsevier in its journal Computational Statistics & Data Analysis.

Volume (Year): 76 (2014)
Issue (Month): C ()
Pages: 536-555

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Handle: RePEc:eee:csdana:v:76:y:2014:i:c:p:536-555

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Web page: http://www.elsevier.com/locate/csda

Related research

Keywords: Noncausal vector autoregression; Forecasting; Simulation; Importance sampling; Inflation;

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References

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  1. Deborah Gefang & Gary Koop & Simon M. Potter, 2009. "The Dynamics of UK and US Inflation Expectations," Working Paper Series 14_09, The Rimini Centre for Economic Analysis, revised Jan 2009.
  2. Lanne Markku & Saikkonen Pentti, 2011. "Noncausal Autoregressions for Economic Time Series," Journal of Time Series Econometrics, De Gruyter, vol. 3(3), pages 1-32, October.
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  13. Lanne, Markku & Luoto, Jani, 2013. "Autoregression-based estimation of the new Keynesian Phillips curve," Journal of Economic Dynamics and Control, Elsevier, vol. 37(3), pages 561-570.
  14. Henri Nyberg & Markku Lanne & Erkka Saarinen, 2012. "Does noncausality help in forecasting economic time series?," Economics Bulletin, AccessEcon, vol. 32(4), pages 2849-2859.
  15. Canova, Fabio, 2007. "G-7 Inflation Forecasts: Random Walk, Phillips Curve Or What Else?," Macroeconomic Dynamics, Cambridge University Press, vol. 11(01), pages 1-30, February.
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Cited by:
  1. 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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