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

Author

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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. 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 if this data admissibility lends itself to better predictions of the probability of recession.

Suggested Citation

  • Heather M. Anderson & George Athanasopoulos & Farshid Vahid, 2006. "Nonlinear autoregressive leading indicator models of output in G-7 countries," CAMA Working Papers 2006-14, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University.
    • Handle: RePEc:een:camaaa:2006-14
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    File URL: https://cama.crawford.anu.edu.au/sites/default/files/publication/cama_crawford_anu_edu_au/2021-06/14_anderson_athanasopoulos_vahid_2006.pdf
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    1. is not listed on IDEAS
    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. Ubilava, David, 2019. "On The Relationship Between Financial Instability And Economic Performance: Stressing The Business Of Nonlinear Modeling," Macroeconomic Dynamics, Cambridge University Press, vol. 23(1), pages 80-100, January.
    4. Ben Cheikh, Nidhaleddine & Ben Naceur, Sami & Kanaan, Oussama & Rault, Christophe, 2021. "Investigating the asymmetric impact of oil prices on GCC stock markets," Economic Modelling, Elsevier, vol. 102(C).
    5. Koo, Bonsoo & Seo, Myung Hwan, 2015. "Structural-break models under mis-specification: Implications for forecasting," Journal of Econometrics, Elsevier, vol. 188(1), pages 166-181.
    6. John G Powell & Sirimon Treepongkaruna, 2012. "Recession fears as self-fulfilling prophecies? Influence on stock returns and output," Australian Journal of Management, Australian School of Business, vol. 37(2), pages 231-260, August.
    7. Markku Lanne & Henri Nyberg, 2016. "Generalized Forecast Error Variance Decomposition for Linear and Nonlinear Multivariate Models," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 78(4), pages 595-603, August.
    8. Moid U. Ahmad, 2015. "Does CRR and Repo Change Affect Corporate Output?," Jindal Journal of Business Research, , vol. 4(1-2), pages 115-125, June.
    9. Nidhaleddine Ben Cheikh & Younes Ben Zaied & Pascal Nguyen, 2018. "Nonlinear Exchange Rate Transmission in the Euro Area: A Multivariate Smooth Transition Regression Approach," Economics Bulletin, AccessEcon, vol. 38(3), pages 1590-1602.
    10. NIDHALEDDINE BEN CHEIKH & SAMI BEN NACEUR & OUSSAMA KANAAN & Christophe RAULT, 2019. "Oil Prices and GCC Stock Markets: New Evidence from Vector Smooth Transition Models," LEO Working Papers / DR LEO 2697, Orleans Economics Laboratory / Laboratoire d'Economie d'Orleans (LEO), University of Orleans.
    11. Maksim Isakin & Phuong V. Ngo, 2020. "Variance Decomposition Analysis for Nonlinear Economic Models," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 82(6), pages 1362-1374, December.
    12. Kheifets, Igor L., 2018. "Multivariate specification tests based on a dynamic Rosenblatt transform," Computational Statistics & Data Analysis, Elsevier, vol. 124(C), pages 1-14.
    13. Igor L. Kheifets & Pentti J. Saikkonen, 2020. "Stationarity and ergodicity of vector STAR models," Econometric Reviews, Taylor & Francis Journals, vol. 39(4), pages 407-414, April.
    14. Ralf Becker & Denise R. Osborn, 2012. "Weighted Smooth Transition Regressions," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 27(5), pages 795-811, August.

    More about this item

    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C23 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Models with Panel Data; Spatio-temporal Models
    • E17 - Macroeconomics and Monetary Economics - - General Aggregative Models - - - Forecasting and Simulation: Models and Applications
    • E37 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Forecasting and Simulation: Models and Applications

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