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Nonlinear autoregressive leading indicator models of output in G-7 countries

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  • George Athanasopoulos

    (Department of Econometrics and Business Statistics, Monash University, Clayton, Australia)

  • Heather M. Anderson

    (School of Economics, Australian National University, Canberra, Australia)

  • Farshid Vahid

    (School of Economics, Australian National University, Canberra, Australia)

Abstract

This paper studies linear and nonlinear autoregressive leading indicator models of business cycles in G-7 countries. Our models use the spread between short-term and long-term interest rates as leading indicators for GDP. We examine data admissibility by determining whether these models have the ability to produce time series with classical cycles that resemble the observed classical cycles in the data, and then we ask whether this data admissibility lends itself to better predictions of the probability of recession. Copyright © 2007 John Wiley & Sons, Ltd.

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Bibliographic Info

Article provided by John Wiley & Sons, Ltd. in its journal Journal of Applied Econometrics.

Volume (Year): 22 (2007)
Issue (Month): 1 ()
Pages: 63-87

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Handle: RePEc:jae:japmet:v:22:y:2007:i:1:p:63-87

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Cited by:
  1. 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.
  2. Boonsoo Koo & Myung Hwan Seo, 2013. "Structural-break models under mis-specification: implications for forecasting," Monash Econometrics and Business Statistics Working Papers 8/13, Monash University, Department of Econometrics and Business Statistics.
  3. Ana Beatriz Galv�o, 2007. "Changes in Predictive Ability with Mixed Frequency Data," Working Papers 595, Queen Mary, University of London, School of Economics and Finance.

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