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Modelling Business Cycle Nonlinearity in Conditional Mean and Conditional Variance: Some International and Sectoral Evidence

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  • Peel, D A
  • Speight, A E H

Abstract

This paper tests for the presence of output mean and variance nonlinearities in international industrial production and U.K. and U.S. sectoral production growth rates using ARMA-GQARCH, bilinear (BL) and joint BL-GQARCH models. ARMA-GQARCH models confirm the presence of asymmetric variance effects in Italian, U.K. and U.S. industrial production and in all sectors other than U.S. nondurables, and such that the conditional variance of output is increased following negative shocks. BL models are identified for German, Italian and U.S. industrial production and U.S. manufacturing, while BL-GQARCH models of joint nonlinearity in both conditional mean and conditional variance are also found to hold for U.S. industrial production and manufacturing. Moreover, with the exception of Italy, all BL and BL-QARCH models provide superior out-of-sample mean forecasts relative to forecasts from both naive models and models of the ARMA-GQARCH class. Copyright 1998 by The London School of Economics and Political Science

Suggested Citation

  • Peel, D A & Speight, A E H, 1998. "Modelling Business Cycle Nonlinearity in Conditional Mean and Conditional Variance: Some International and Sectoral Evidence," Economica, London School of Economics and Political Science, vol. 65(258), pages 211-229, May.
    • Handle: RePEc:bla:econom:v:65:y:1998:i:258:p:211-29
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    Cited by:

    1. Elena Andreou & Alessandra Pelloni & Marianne Sensier, 2008. "Is Volatility Good for Growth? Evidence from the G7," Economics Discussion Paper Series 0804, Economics, The University of Manchester.
    2. Luis Eduardo Arango Thomas, 1998. "Some univariate time series properties of output," Lecturas de Economía, Universidad de Antioquia, Departamento de Economía, issue 49, pages 7-46, Julio Dic.
    3. Sun, Yuying & Han, Ai & Hong, Yongmiao & Wang, Shouyang, 2018. "Threshold autoregressive models for interval-valued time series data," Journal of Econometrics, Elsevier, vol. 206(2), pages 414-446.
    4. Marian Vavra, 2012. "A Note on the Finite Sample Properties of the CLS Method of TAR Models," Birkbeck Working Papers in Economics and Finance 1206, Birkbeck, Department of Economics, Mathematics & Statistics.
    5. repec:rim:rimwps:37-08 is not listed on IDEAS
    6. Andrew McKenzie & Matthew Holt, 2002. "Market efficiency in agricultural futures markets," Applied Economics, Taylor & Francis Journals, vol. 34(12), pages 1519-1532.
    7. Taylor Mark P. & Davradakis Emmanuel, 2006. "Interest Rate Setting and Inflation Targeting: Evidence of a Nonlinear Taylor Rule for the United Kingdom," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 10(4), pages 1-20, December.
    8. E Andreou & A Pelloni & M Sensier, 2003. "The effect of nominal shock uncertainty on output growth," Centre for Growth and Business Cycle Research Discussion Paper Series 40, Economics, The University of Manchester.
    9. Galyna Grynkiv & Lars Stentoft, 2018. "Stationary Threshold Vector Autoregressive Models," JRFM, MDPI, vol. 11(3), pages 1-23, August.
    10. Taylor, Mark, 2003. "Is Official Exchange Rate Intervention Effective?," CEPR Discussion Papers 3758, C.E.P.R. Discussion Papers.

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