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Nonlinear Autoregresssive Leading Indicator Models of Output in G-7 Countries

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  • Heather M. Anderson

    ()

  • George Athanasopoulos
  • Farshid Vahid

    ()

Abstract

This paper studies linear and nonlinear autoregressive leading indicator models of business cycles in G7 countries. The models use the spread between short-term and long-term interest rates as leading indicators for GDP, and their success in capturing business cycles is gauged by non-parametric shape tests, and their ability to predict the probability of recession. We find that bivariate nonlinear models of output and the interest rate spread can successfully capture the shape of the business cycle in cases where linear models fail. Also, our nonlinear leading indicator models for USA, Canada and the UK outperform other models of GDP with respect to predicting the probability of recession.

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Bibliographic Info

Paper provided by Monash University, Department of Econometrics and Business Statistics in its series Monash Econometrics and Business Statistics Working Papers with number 20/02.

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Length: 38 pages
Date of creation: Dec 2002
Date of revision:
Handle: RePEc:msh:ebswps:2002-20

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Keywords: Business Cycles; Leading Indicators; Model Evaluation; Nonlinear Models; Yield Spreads.;

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References

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Cited by:
  1. Boonsoo Koo & Myung Hwan Seo, 2013. "Structural-break models under mis-specification: implications for forecasting," Monash Econometrics and Business Statistics Working Papers 11/13, Monash University, Department of Econometrics and Business Statistics.
  2. Galvão, Ana Beatriz, 2013. "Changes in predictive ability with mixed frequency data," International Journal of Forecasting, Elsevier, vol. 29(3), pages 395-410.
  3. Markku Lanne & Henri Nyberg, 2014. "Generalized Forecast Error Variance Decomposition for Linear and Nonlinear Multivariate Models," CREATES Research Papers 2014-17, School of Economics and Management, University of Aarhus.

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