IDEAS home Printed from https://ideas.repec.org/a/bla/socsci/v103y2022i5p1273-1289.html
   My bibliography  Save this article

What goes up must come down: Theory and model specification of threshold dynamics

Author

Listed:
  • Hannah L. Paul
  • Andrew Q. Philips

Abstract

Objectives Despite the frequent use of time series models in the social sciences, they have often remained within the confines of assuming purely linear dynamic effects. We contend that many theories involve relationships that are inherently non‐linear. Methods We discuss several approaches to modeling a variety of these types of non‐linear autoregressive data‐generating processes, specifically threshold effects. Results We replicate and extend a recent analysis, and show evidence of threshold processes. Conclusion In doing so, we show that threshold models allow us to test richer, more complex theoretical implications about dynamic effects.

Suggested Citation

  • Hannah L. Paul & Andrew Q. Philips, 2022. "What goes up must come down: Theory and model specification of threshold dynamics," Social Science Quarterly, Southwestern Social Science Association, vol. 103(5), pages 1273-1289, September.
    • Handle: RePEc:bla:socsci:v:103:y:2022:i:5:p:1273-1289
    DOI: 10.1111/ssqu.13191
    as

    Download full text from publisher

    File URL: https://doi.org/10.1111/ssqu.13191
    Download Restriction: no
    File URL: https://libkey.io/10.1111/ssqu.13191?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
    ---><---

    References listed on IDEAS

    as
    1. Jong Hee Park, 2010. "Structural Change in U.S. Presidents' Use of Force," American Journal of Political Science, John Wiley & Sons, vol. 54(3), pages 766-782, July.
    2. Bruce E. Hansen, 2000. "Sample Splitting and Threshold Estimation," Econometrica, Econometric Society, vol. 68(3), pages 575-604, May.
    3. Granger, Clive W. J. & Terasvirta, Timo, 1993. "Modelling Non-Linear Economic Relationships," OUP Catalogue, Oxford University Press, number 9780198773207.
    4. Songlin Zhang & Haoqing Zheng & Hongyong Zhou & Qing Shao & Qun Wu, 2021. "Sustainable land urbanization, urban amenities, and population urbanization: Evidence from city‐level data in China," Social Science Quarterly, Southwestern Social Science Association, vol. 102(4), pages 1686-1698, July.
    5. Terasvirta, T & Anderson, H M, 1992. "Characterizing Nonlinearities in Business Cycles Using Smooth Transition Autoregressive Models," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 7(S), pages 119-136, Suppl. De.
    6. Tetsuya Matsubayashi, 2007. "Population Size, Local Autonomy, and Support for the Political System," Social Science Quarterly, Southwestern Social Science Association, vol. 88(3), pages 830-849, September.
    7. Cameron Wimpy & Guy D. Whitten, 2017. "What Is and What May Never Be: Economic Voting in Developing Democracies," Social Science Quarterly, Southwestern Social Science Association, vol. 98(3), pages 1099-1111, September.
    8. Daniel Kahneman & Amos Tversky, 2013. "Prospect Theory: An Analysis of Decision Under Risk," World Scientific Book Chapters, in: Leonard C MacLean & William T Ziemba (ed.), HANDBOOK OF THE FUNDAMENTALS OF FINANCIAL DECISION MAKING Part I, chapter 6, pages 99-127, World Scientific Publishing Co. Pte. Ltd..
    9. Jeremy R. Levine & Theodore S. Leenman & Carl Gershenson & David M. Hureau, 2018. "Political Places: Neighborhood Social Organization and the Ecology of Political Behaviors," Social Science Quarterly, Southwestern Social Science Association, vol. 99(1), pages 201-215, March.
    10. Stuart N. Soroka & Dominik A. Stecula & Christopher Wlezien, 2015. "It's (Change in) the (Future) Economy, Stupid: Economic Indicators, the Media, and Public Opinion," American Journal of Political Science, John Wiley & Sons, vol. 59(2), pages 457-474, February.
    11. Goldfeld, Stephen M. & Quandt, Richard E., 1973. "A Markov model for switching regressions," Journal of Econometrics, Elsevier, vol. 1(1), pages 3-15, March.
    12. Alexandra K. Murphy & Danielle Wallace, 2010. "Opportunities for Making Ends Meet and Upward Mobility: Differences in Organizational Deprivation Across Urban and Suburban Poor Neighborhoods," Social Science Quarterly, Southwestern Social Science Association, vol. 91(s1), pages 1164-1186.
    13. Gonzalo, Jesus & Pitarakis, Jean-Yves, 2002. "Estimation and model selection based inference in single and multiple threshold models," Journal of Econometrics, Elsevier, vol. 110(2), pages 319-352, October.
    14. 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.
    15. Suzanna De Boef & Luke Keele, 2008. "Taking Time Seriously," American Journal of Political Science, John Wiley & Sons, vol. 52(1), pages 184-200, January.
    16. Alexandra K. Murphy & Danielle Wallace, 2010. "Opportunities for Making Ends Meet and Upward Mobility: Differences in Organizational Deprivation Across Urban and Suburban Poor Neighborhoods," Social Science Quarterly, Southwestern Social Science Association, vol. 91(5), pages 1164-1186, December.
    17. Choi, Woon Gyu, 1999. "Asymmetric Monetary Effects on Interest Rates across Monetary Policy Stances," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 31(3), pages 386-416, August.
    18. Will Jennings & Peter John, 2009. "The Dynamics of Political Attention: Public Opinion and the Queen's Speech in the United Kingdom," American Journal of Political Science, John Wiley & Sons, vol. 53(4), pages 838-854, October.
    19. Metzger, Shawna K. & Jones, Benjamin T., 2016. "Surviving Phases: Introducing Multistate Survival Models," Political Analysis, Cambridge University Press, vol. 24(4), pages 457-477.
    Full references (including those not matched with items on IDEAS)

    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. 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.
    2. Martinez Oscar & Olmo Jose, 2012. "A Nonlinear Threshold Model for the Dependence of Extremes of Stationary Sequences," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 16(3), pages 1-39, September.
    3. Laurent Ferrara & Dominique Guégan, 2006. "Detection of the Industrial Business Cycle using SETAR Models," Journal of Business Cycle Measurement and Analysis, OECD Publishing, Centre for International Research on Economic Tendency Surveys, vol. 2005(3), pages 353-371.
    4. 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.
    5. Franses,Philip Hans & Dijk,Dick van, 2000. "Non-Linear Time Series Models in Empirical Finance," Cambridge Books, Cambridge University Press, number 9780521779654, September.
    6. Georgellis, Yannis & Gregoriou, Andros & Tsitsianis, Nikolaos, 2008. "Adaptation towards reference values: A non-linear perspective," Journal of Economic Behavior & Organization, Elsevier, vol. 67(3-4), pages 768-781, September.
    7. repec:wsr:wpaper:y:2010:i:057 is not listed on IDEAS
    8. 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.
    9. Álvaro Escribano & Oscar Jordá, 2001. "Testing nonlinearity: Decision rules for selecting between logistic and exponential STAR models," Spanish Economic Review, Springer;Spanish Economic Association, vol. 3(3), pages 193-209.
    10. Calmès, Christian & Théoret, Raymond, 2020. "Bank fee-based shocks and the U.S. business cycle," The North American Journal of Economics and Finance, Elsevier, vol. 51(C).
    11. Zuzanna Karolak, 2021. "Energy prices forecasting using nonlinear univariate models," Bank i Kredyt, Narodowy Bank Polski, vol. 52(6), pages 577-598.
    12. Sun, Yuying & Han, Ai & Hong, Yongmiao & Wang, Shouyang, 2018. "Threshold autoregressive models for interval-valued time series data," Journal of Econometrics, Elsevier, vol. 206(2), pages 414-446.
    13. Coskun, Esra Alp & Lau, Chi Keung Marco & Kahyaoglu, Hakan, 2020. "Uncertainty and herding behavior: evidence from cryptocurrencies," Research in International Business and Finance, Elsevier, vol. 54(C).
    14. Gregoriou, Andros & Kontonikas, Alexandros, 2009. "Modeling the behaviour of inflation deviations from the target," Economic Modelling, Elsevier, vol. 26(1), pages 90-95, January.
    15. Velasco, Sofia, 2024. "Asymmetries in the transmission of monetary policy shocks over the business cycle: a Bayesian Quantile Factor Augmented VAR," Working Paper Series 2983, European Central Bank.
    16. Simon Potter, 1999. "Nonlinear Time Series Modelling: An Introduction," Journal of Economic Surveys, Wiley Blackwell, vol. 13(5), pages 505-528, December.
    17. Kamel Helali, 2022. "Markov Switching-Vector AutoRegression Model Analysis of the Economic and Growth Cycles in Tunisia and Its Main European Partners," Journal of the Knowledge Economy, Springer;Portland International Center for Management of Engineering and Technology (PICMET), vol. 13(1), pages 656-686, March.
    18. Anna Florio, 2009. "Asymmetric Preferences For Interest Rate Variability And Non‐Linear Monetary Policy Inertia," Scottish Journal of Political Economy, Scottish Economic Society, vol. 56(5), pages 685-704, November.
    19. Clements, Michael P. & Franses, Philip Hans & Swanson, Norman R., 2004. "Forecasting economic and financial time-series with non-linear models," International Journal of Forecasting, Elsevier, vol. 20(2), pages 169-183.
    20. 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.
    21. Ubilava, David & Helmers, Claes Gustav, 2011. "The ENSO Impact on Predicting World Cocoa Prices," 2011 Annual Meeting, July 24-26, 2011, Pittsburgh, Pennsylvania 103528, Agricultural and Applied Economics Association.

    More about this item

    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:bla:socsci:v:103:y:2022:i:5:p:1273-1289. 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: Wiley Content Delivery (email available below). General contact details of provider: http://www.blackwellpublishing.com/journal.asp?ref=0038-4941 .

    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.