IDEAS home Printed from https://ideas.repec.org/p/fth/helsec/489.html
   My bibliography  Save this paper

Threshold Autoregression for Strongly Autocorrelated Time Series

Author

Listed:
  • Lanne, M.
  • Saikkonen, P.

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 process with the threshold effect only in the intercept term. Although these proceses are stationary, their realizations can closely resemble those of 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 integratedness is really caused by level shifts, the series pruged of these shifts should be stable so that known stationarity tests can be applied to this series. Simulation results indicate that in certain cases this test like several linearity tests can have low power. The proposed model is applied to interest rate data.

Suggested Citation

  • Lanne, M. & Saikkonen, P., 2000. "Threshold Autoregression for Strongly Autocorrelated Time Series," University of Helsinki, Department of Economics 489, Department of Economics.
  • Handle: RePEc:fth:helsec:489
    as

    Download full text from publisher

    To our knowledge, this item is not available for download. To find whether it is available, there are three options:
    1. Check below whether another version of this item is available online.
    2. Check on the provider's web page whether it is in fact available.
    3. Perform a search for a similarly titled item that would be available.

    Other versions of this item:

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    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. Jarkko Jääskelä, 2007. "More Potent Monetary Policy? Insights from a Threshold Model," RBA Research Discussion Papers rdp2007-07, Reserve Bank of Australia.
    3. 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.
    4. 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.
    5. Timo Teräsvirta, 2909. "Nonlinear models in macroeconometrics," CREATES Research Papers 2017-32, Department of Economics and Business Economics, Aarhus University.
    6. 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.
    7. 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.

    More about this item

    Keywords

    TESTS ; MODELS ; TIME SERIES;

    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

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:fth:helsec:489. See general information about how to correct material in RePEc.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Thomas Krichel). General contact details of provider: http://edirc.repec.org/data/valhefi.html .

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service hosted by the Research Division of the Federal Reserve Bank of St. Louis . RePEc uses bibliographic data supplied by the respective publishers.