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Does noncausality help in forecasting economic time series?

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  • Henri Nyberg

    ()
    (University of Helsinki)

  • Markku Lanne

    ()
    (University of Helsinki)

  • Erkka Saarinen

    ()
    (University of Helsinki)

Abstract

In this paper, we compare the forecasting performance of univariate noncausal and conventional causal autoregressive models for a comprehensive data set consisting of 170 monthly U.S. macroeconomic and financial time series. The noncausal models consistently outperform the causal models. For a collection of quarterly time series, the improvement in forecast accuracy due to allowing for noncausality is found even greater.

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File URL: http://www.accessecon.com/Pubs/EB/2012/Volume32/EB-12-V32-I4-P274.pdf
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Bibliographic Info

Article provided by AccessEcon in its journal Economics Bulletin.

Volume (Year): 32 (2012)
Issue (Month): 4 ()
Pages: 2849-2859

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Handle: RePEc:ebl:ecbull:eb-12-00360

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Related research

Keywords: Noncausal autoregression; forecast comparison; macroeconomic variables; financial variables;

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  1. Lanne, Markku & Luoma, Arto & Luoto, Jani, 2009. "Bayesian Model Selection and Forecasting in Noncausal Autoregressive Models," MPRA Paper 23646, University Library of Munich, Germany.
  2. Marcellino, Massimiliano & Stock, James H & Watson, Mark W, 2005. "A Comparison of Direct and Iterated Multistep AR Methods for Forecasting Macroeconomic Time Series," CEPR Discussion Papers 4976, C.E.P.R. Discussion Papers.
  3. Lanne, Markku & Nyberg, Henri & Saarinen, Erkka, 2011. "Forecasting U.S. Macroeconomic and Financial Time Series with Noncausal and Causal AR Models: A Comparison," MPRA Paper 30254, University Library of Munich, Germany.
  4. Lanne, Markku & Luoto, Jani & Saikkonen, Pentti, 2010. "Optimal Forecasting of Noncausal Autoregressive Time Series," MPRA Paper 23648, University Library of Munich, Germany.
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