IDEAS home Printed from https://ideas.repec.org/a/eee/econom/v189y2015i2p485-491.html
   My bibliography  Save this article

Threshold models in time series analysis—Some reflections

Author

Listed:
  • Tong, Howell

Abstract

In this paper, I reflect on the developments of the threshold model in time series analysis since its birth in 1978, with particular reference to econometrics.

Suggested Citation

  • Tong, Howell, 2015. "Threshold models in time series analysis—Some reflections," Journal of Econometrics, Elsevier, vol. 189(2), pages 485-491.
    • Handle: RePEc:eee:econom:v:189:y:2015:i:2:p:485-491
    DOI: 10.1016/j.jeconom.2015.03.039
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0304407615001177
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.jeconom.2015.03.039?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Ke Zhu & Wai Keung Li & Philip L. H. Yu, 2017. "Buffered Autoregressive Models With Conditional Heteroscedasticity: An Application to Exchange Rates," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 35(4), pages 528-542, October.
    2. Smith, J. Q. & Harrison, P. J. & Zeeman, E. C., 1981. "The analysis of some discontinuous decision processes," European Journal of Operational Research, Elsevier, vol. 7(1), pages 30-43, May.
    3. Dick van Dijk & Timo Terasvirta & Philip Hans Franses, 2002. "Smooth Transition Autoregressive Models — A Survey Of Recent Developments," Econometric Reviews, Taylor & Francis Journals, vol. 21(1), pages 1-47.
    4. K. S. Chan & H. Tong, 1986. "On Estimating Thresholds In Autoregressive Models," Journal of Time Series Analysis, Wiley Blackwell, vol. 7(3), pages 179-190, May.
    5. Dong Li, 2012. "A note on moving-average models with feedback," Journal of Time Series Analysis, Wiley Blackwell, vol. 33(6), pages 873-879, November.
    6. Alexander Aue & Lajos Horváth, 2013. "Structural breaks in time series," Journal of Time Series Analysis, Wiley Blackwell, vol. 34(1), pages 1-16, January.
    7. Herold Dehling & Aeneas Rooch & Murad S. Taqqu, 2013. "Non-Parametric Change-Point Tests for Long-Range Dependent Data," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 40(1), pages 153-173, March.
    8. Noelle I. Samia & Kung-Sik Chan & Nils Chr. Stenseth, 2007. "A generalized threshold mixed model for analyzing nonnormal nonlinear time series, with application to plague in Kazakhstan," Biometrika, Biometrika Trust, vol. 94(1), pages 101-118.
    9. Kapetanios, George & Mitchell, James & Shin, Yongcheol, 2014. "A nonlinear panel data model of cross-sectional dependence," Journal of Econometrics, Elsevier, vol. 179(2), pages 134-157.
    10. Terasvirta, Timo & Tjostheim, Dag & Granger, Clive W. J., 2010. "Modelling Nonlinear Economic Time Series," OUP Catalogue, Oxford University Press, number 9780199587155.
    11. Hamilton, James D, 1989. "A New Approach to the Economic Analysis of Nonstationary Time Series and the Business Cycle," Econometrica, Econometric Society, vol. 57(2), pages 357-384, March.
    12. Yao, Qiwei & Tong, Howell & Finkenstädt, Bärbel & Stenseth, Nils Chr, 2000. "Common structure in panels of short time series," LSE Research Online Documents on Economics 6325, London School of Economics and Political Science, LSE Library.
    13. White, Halbert, 1982. "Maximum Likelihood Estimation of Misspecified Models," Econometrica, Econometric Society, vol. 50(1), pages 1-25, January.
    14. Noelle I. Samia & Kung-Sik Chan, 2011. "Maximum likelihood estimation of a generalized threshold stochastic regression model," Biometrika, Biometrika Trust, vol. 98(2), pages 433-448.
    15. Leena Kalliovirta & Mika Meitz & Pentti Saikkonen, 2015. "A Gaussian Mixture Autoregressive Model for Univariate Time Series," Journal of Time Series Analysis, Wiley Blackwell, vol. 36(2), pages 247-266, March.
    16. Line Elvstrøm Ekner & Emil Nejstgaard, 2013. "Parameter Identification in the Logistic STAR Model," Discussion Papers 13-07, University of Copenhagen. Department of Economics.
    17. C. S. Wong & W. K. Li, 2000. "On a mixture autoregressive model," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 62(1), pages 95-115.
    18. Enders, Walter & Granger, Clive W J, 1998. "Unit-Root Tests and Asymmetric Adjustment with an Example Using the Term Structure of Interest Rates," Journal of Business & Economic Statistics, American Statistical Association, vol. 16(3), pages 304-311, July.
    19. K. S. Chan & H. Tong & N. Chr. Stenseth, 2004. "Testing for Common Structures in a Panel of Threshold Models," Biometrics, The International Biometric Society, vol. 60(1), pages 225-232, March.
    Full references (including those not matched with items on IDEAS)

    Citations

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


    Cited by:

    1. Andrzej Pisulewski, 2019. "The Dynamics of Unemployment in Poland from 1992 to 2017," Gospodarka Narodowa. The Polish Journal of Economics, Warsaw School of Economics, issue 1, pages 135-149.
    2. Lv, Wendai, 2018. "Does the OVX matter for volatility forecasting? Evidence from the crude oil market," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 492(C), pages 916-922.
    3. Glen Livingston Jr & Darfiana Nur, 2020. "Bayesian estimation and model selection of a multivariate smooth transition autoregressive model," Environmetrics, John Wiley & Sons, Ltd., vol. 31(6), September.
    4. Kaiji Motegi & Xiaojing Cai & Shigeyuki Hamori & Haifeng Xu, 2020. "Moving average threshold heterogeneous autoregressive (MAT‐HAR) models," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 39(7), pages 1035-1042, November.
    5. Antoine Lejay & Paolo Pigato, 2017. "A threshold model for local volatility: evidence of leverage and mean reversion effects on historical data," Working Papers hal-01669082, HAL.
    6. Srivastava, Dinesh Kumar & Bharadwaj, Muralikrishna & Kapur, Tarrung & Trehan, Ragini, 2021. "Examining sustainability of government debt in India: post Covid prospects," MPRA Paper 108342, University Library of Munich, Germany.
    7. Bruce E. Hansen, 1996. "Estimation of TAR Models," Boston College Working Papers in Economics 325., Boston College Department of Economics.
    8. Barrales-Ruiz, Jose & Mohammed, Mikidadu, 2021. "Financial regimes and oil prices," Resources Policy, Elsevier, vol. 74(C).
    9. Perera, Indeewara & Koul, Hira L., 2017. "Fitting a two phase threshold multiplicative error model," Journal of Econometrics, Elsevier, vol. 197(2), pages 348-367.
    10. Antoine Lejay & Paolo Pigato, 2019. "A Threshold Model For Local Volatility: Evidence Of Leverage And Mean Reversion Effects On Historical Data," International Journal of Theoretical and Applied Finance (IJTAF), World Scientific Publishing Co. Pte. Ltd., vol. 22(04), pages 1-24, June.
    11. Glen Livingston & Darfiana Nur, 2020. "Bayesian inference of smooth transition autoregressive (STAR)(k)–GARCH(l, m) models," Statistical Papers, Springer, vol. 61(6), pages 2449-2482, December.
    12. Darko B. Vuković & Moinak Maiti & Marko D. Petrović, 2023. "Tourism Employment and Economic Growth: Dynamic Panel Threshold Analysis," Mathematics, MDPI, vol. 11(5), pages 1-14, February.
    13. Bo Pieter Johannes Andree & Francisco Blasques & Eric Koomen, 2017. "Smooth Transition Spatial Autoregressive Models," Tinbergen Institute Discussion Papers 17-050/III, Tinbergen Institute.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Terasvirta, Timo, 2006. "Forecasting economic variables with nonlinear models," Handbook of Economic Forecasting, in: G. Elliott & C. Granger & A. Timmermann (ed.), Handbook of Economic Forecasting, edition 1, volume 1, chapter 8, pages 413-457, Elsevier.
    2. Lopes, Artur Silva & Zsurkis, Gabriel Florin, 2017. "Are linear models really unuseful to describe business cycle data?," Economics Discussion Papers 2017-5, Kiel Institute for the World Economy (IfW Kiel).
    3. Artur Silva Lopes & Gabriel Florin Zsurkis, 2019. "Are linear models really unuseful to describe business cycle data?," Applied Economics, Taylor & Francis Journals, vol. 51(22), pages 2355-2376, May.
    4. Franses,Philip Hans & Dijk,Dick van & Opschoor,Anne, 2014. "Time Series Models for Business and Economic Forecasting," Cambridge Books, Cambridge University Press, number 9780521520911, September.
    5. Timo Teräsvirta, 2017. "Nonlinear models in macroeconometrics," CREATES Research Papers 2017-32, Department of Economics and Business Economics, Aarhus University.
    6. Barbara Annicchiarico & Anna Rita Bennato & Emilio Zanetti Chini, 2014. "150 Years of Italian CO2 Emissions and Economic Growth," CEIS Research Paper 320, Tor Vergata University, CEIS, revised 31 Jul 2014.
    7. Galvão, Ana Beatriz, 2013. "Changes in predictive ability with mixed frequency data," International Journal of Forecasting, Elsevier, vol. 29(3), pages 395-410.
    8. Sibel Cengiz & Afsin Sahin, 2014. "Modelling nonlinear behavior of labor force participation rate by STAR: An application for Turkey," International Journal of Business and Economic Sciences Applied Research (IJBESAR), International Hellenic University (IHU), Kavala Campus, Greece (formerly Eastern Macedonia and Thrace Institute of Technology - EMaTTech), vol. 7(1), pages 113-127, April.
    9. Michael Wegener & Göran Kauermann, 2017. "Forecasting in nonlinear univariate time series using penalized splines," Statistical Papers, Springer, vol. 58(3), pages 557-576, September.
    10. Singh, Tarlok, 2014. "On the regime-switching and asymmetric dynamics of economic growth in the OECD countries," Research in Economics, Elsevier, vol. 68(2), pages 169-192.
    11. Kalliovirta, Leena & Meitz, Mika & Saikkonen, Pentti, 2016. "Gaussian mixture vector autoregression," Journal of Econometrics, Elsevier, vol. 192(2), pages 485-498.
    12. 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.
    13. Dueker, Michael J. & Sola, Martin & Spagnolo, Fabio, 2007. "Contemporaneous threshold autoregressive models: Estimation, testing and forecasting," Journal of Econometrics, Elsevier, vol. 141(2), pages 517-547, December.
    14. Demian Pouzo & Zacharias Psaradakis & Martin Sola, 2022. "Maximum Likelihood Estimation in Markov Regime‐Switching Models With Covariate‐Dependent Transition Probabilities," Econometrica, Econometric Society, vol. 90(4), pages 1681-1710, July.
    15. David Ubilava, 2014. "El Niño Southern Oscillation and the fishmeal–soya bean meal price ratio: regime-dependent dynamics revisited," European Review of Agricultural Economics, Oxford University Press and the European Agricultural and Applied Economics Publications Foundation, vol. 41(4), pages 583-604.
    16. Ferrara, Laurent & Marcellino, Massimiliano & Mogliani, Matteo, 2015. "Macroeconomic forecasting during the Great Recession: The return of non-linearity?," International Journal of Forecasting, Elsevier, vol. 31(3), pages 664-679.
    17. Eraslan, Sercan & Nöller, Marvin, 2020. "Recession probabilities falling from the STARs," Discussion Papers 08/2020, Deutsche Bundesbank.
    18. Anoop Chaturvedi & Shivam Jaiswal, 2020. "Bayesian Estimation and Unit Root Test for Logistic Smooth Transition Autoregressive Process," Journal of Quantitative Economics, Springer;The Indian Econometric Society (TIES), vol. 18(4), pages 733-745, December.
    19. LeBaron, Blake, 2003. "Non-Linear Time Series Models in Empirical Finance,: Philip Hans Franses and Dick van Dijk, Cambridge University Press, Cambridge, 2000, 296 pp., Paperback, ISBN 0-521-77965-0, $33, [UK pound]22.95, [," International Journal of Forecasting, Elsevier, vol. 19(4), pages 751-752.
    20. Franses,Philip Hans & Dijk,Dick van, 2000. "Non-Linear Time Series Models in Empirical Finance," Cambridge Books, Cambridge University Press, number 9780521779654.

    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:eee:econom:v:189:y:2015:i:2:p:485-491. See general information about how to correct material in RePEc.

    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.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with 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.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/jeconom .

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

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.