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Threshold Autoregressions for Strongly Autocorrelated Time Series

Author

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  • Lanne, Markku
  • Saikkonen, Pentti

Abstract

In some cases the unit root or near unit root behavior of linear autoregressive models fitted to economic time series is not in accordance with the underlying economic theory. To accommodate this feature we consider a threshold autoregressive (TAR) process with the threshold effect only in the intercept term. Although these processes are stationary, their realizations switch between different regimes and can therefore closely resemble those of (near) integrated processes for sample sizes relevant in many economic applications. Estimation and inference of these TAR models are discussed, and a specification test for testing their stability is derived. Testing is based on the idea that if (near) integratedness is really caused by level shifts, the series purged of these shifts should be stable so that known stationarity tests can be applied to this series. Simulation results indicate that in certain cases these tests, like several linearity tests, can have low power. The proposed model is applied to interest rate data.

Suggested Citation

  • Lanne, Markku & Saikkonen, Pentti, 2002. "Threshold Autoregressions for Strongly Autocorrelated Time Series," Journal of Business & Economic Statistics, American Statistical Association, vol. 20(2), pages 282-289, April.
    • Handle: RePEc:bes:jnlbes:v:20:y:2002:i:2:p:282-89
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    Cited by:

    1. Theofanis Archontakis & Wolfgang Lemke, 2008. "Threshold Dynamics of Short‐term Interest Rates: Empirical Evidence and Implications for the Term Structure," Economic Notes, Banca Monte dei Paschi di Siena SpA, vol. 37(1), pages 75-117, February.
    2. Wolfgang Lemke & Theofanis Archontakis, 2008. "Bond pricing when the short-term interest rate follows a threshold process," Quantitative Finance, Taylor & Francis Journals, vol. 8(8), pages 811-822.
    3. Jarkko Jääskelä, 2007. "More Potent Monetary Policy? Insights from a Threshold Model," RBA Research Discussion Papers rdp2007-07, Reserve Bank of Australia.
    4. Christoph Berninger & Almond Stöcker & David Rügamer, 2022. "A Bayesian time‐varying autoregressive model for improved short‐term and long‐term prediction," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 41(1), pages 181-200, January.
    5. Kirstin Hubrich & Timo Teräsvirta, 2013. "Thresholds and Smooth Transitions in Vector Autoregressive Models," CREATES Research Papers 2013-18, Department of Economics and Business Economics, Aarhus University.
    6. Anders Bredahl Kock & Timo Teräsvirta, 2010. "Forecasting with nonlinear time series models," CREATES Research Papers 2010-01, Department of Economics and Business Economics, Aarhus University.
    7. Stanislav Anatolyev & Nikita Kobotaev, 2018. "Modeling and forecasting realized covariance matrices with accounting for leverage," Econometric Reviews, Taylor & Francis Journals, vol. 37(2), pages 114-139, February.
    8. Timo Teräsvirta, 2017. "Nonlinear models in macroeconometrics," CREATES Research Papers 2017-32, Department of Economics and Business Economics, Aarhus University.
    9. Christis Katsouris, 2023. "Structural Analysis of Vector Autoregressive Models," Papers 2312.06402, arXiv.org, revised Feb 2024.
    10. Terence D.Agbeyegbe & Elena Goldman, 2005. "Estimation of threshold time series models using efficient jump MCMC," Economics Working Paper Archive at Hunter College 406, Hunter College Department of Economics, revised 2005.
    11. Christoph Berninger & Almond Stocker & David Rugamer, 2020. "A Bayesian Time-Varying Autoregressive Model for Improved Short- and Long-Term Prediction," Papers 2006.05750, arXiv.org, revised Feb 2021.
    12. Clive G. Bowsher & Roland Meeks, 2008. "Stationarity and the term structure of interest rates: a characterisation of stationary and unit root yield curves," Working Papers 0811, Federal Reserve Bank of Dallas.
    13. Jack Strauss & Mark E. Wohar, 2007. "Domestic‐Foreign Interest Rate Differentials: Near Unit Roots and Symmetric Threshold Models," Southern Economic Journal, John Wiley & Sons, vol. 73(3), pages 814-829, January.

    More about this item

    JEL classification:

    • C12 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Hypothesis Testing: General
    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation

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