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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.

Suggested Citation

  • George Athanasopoulos & Heather M. Anderson & Farshid Vahid, 2007. "Nonlinear autoregressive leading indicator models of output in G-7 countries," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 22(1), pages 63-87.
  • Handle: RePEc:jae:japmet:v:22:y:2007:i:1:p:63-87
    DOI: 10.1002/jae.935
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    Cited by:

    1. Galvão, Ana Beatriz, 2013. "Changes in predictive ability with mixed frequency data," International Journal of Forecasting, Elsevier, vol. 29(3), pages 395-410.
    2. Ubilava, David, 2014. "On the Relationship between Financial Instability and Economic Performance: Stressing the Business of Nonlinear Modelling," 2014 Annual Meeting, July 27-29, 2014, Minneapolis, Minnesota 170222, Agricultural and Applied Economics Association.
    3. 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.
    4. 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.
    5. repec:eee:csdana:v:124:y:2018:i:c:p:1-14 is not listed on IDEAS
    6. 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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