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THRET: Threshold Regression with Endogenous Threshold Variables

Author

Listed:
  • Andros Kourtellos
  • Thanasis Stengos
  • Chih Ming Tan

Abstract

This paper extends the simple threshold regression framework of Hansen (2000) and Caner and Hansen (2004) to allow for endogeneity of the threshold variable. We develop a concentrated two-stage least squares (C2SLS) estimator of the threshold parameter that is based on an inverse Mills ratio bias correction. Our method also allows for the endogeneity of the slope variables. We show that our estimator is consistent and investigate its performance using a Monte Carlo simulation that indicates the applicability of the method is finite samples. We also illustrate its usefulness with an empirical example from economic growth.

Suggested Citation

  • Andros Kourtellos & Thanasis Stengos & Chih Ming Tan, 2008. "THRET: Threshold Regression with Endogenous Threshold Variables," University of Cyprus Working Papers in Economics 3-2008, University of Cyprus Department of Economics.
  • Handle: RePEc:ucy:cypeua:3-2008
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    Cited by:

    1. Didier Yelognisse Alia & Romuald E. Kouadio Anago, 2014. "Foreign Aid Effectiveness in African Economies: Evidence from a Panel Threshold Framework," WIDER Working Paper Series wp-2014-015, World Institute for Development Economic Research (UNU-WIDER).
    2. Alia, Didier Yelognisse & Anago, Romuald E. Kouadio, 2014. "Foreign aid effectiveness in African economies: Evidence from a panel threshold framework," WIDER Working Paper Series 015, World Institute for Development Economic Research (UNU-WIDER).
    3. Gebauer, Stefan & Setzer, Ralph & Westphal, Andreas, 2018. "Corporate debt and investment: A firm-level analysis for stressed euro area countries," Journal of International Money and Finance, Elsevier, vol. 86(C), pages 112-130.
    4. George Kapetanios & James Mitchell & Yongcheol Shin, 2010. "A Nonlinear Panel Model of Cross-sectional Dependence," Working Papers 673, Queen Mary University of London, School of Economics and Finance.
    5. Gebauer, Stefan & Setzer, Ralph & Westphal, Andreas, 2017. "Corporate debt and investment: a firm analysis for stressed euro area countries," Working Paper Series 2101, European Central Bank.
    6. Markus Eberhardt & Francis Teal, 2011. "Econometrics For Grumblers: A New Look At The Literature On Cross‐Country Growth Empirics," Journal of Economic Surveys, Wiley Blackwell, vol. 25(1), pages 109-155, February.
    7. Otilia Boldea & Alastair R. Hall, 2013. "Testing structural stability in macroeconometric models," Chapters, in: Nigar Hashimzade & Michael A. Thornton (ed.), Handbook of Research Methods and Applications in Empirical Macroeconomics, chapter 9, pages 206-228, Edward Elgar Publishing.
    8. Lee, Hsiu-Yun, 2011. "Nonlinear exchange rate dynamics under stochastic official intervention," Economic Modelling, Elsevier, vol. 28(4), pages 1510-1518, July.
    9. Bose, Niloy & Murshid, Antu Panini & Wurm, Martin A., 2012. "The Growth Effects of Property Rights: The Role of Finance," World Development, Elsevier, vol. 40(9), pages 1784-1797.
    10. Jean-Louis Combes & Alexandru Minea & Pegdéwendé Nestor Sawadogo, 2019. "Does the composition of government expenditures matter for sovereign bond spreads' evolution in developing countries?," Working Papers halshs-02019063, HAL.
    11. repec:ipg:wpaper:2014-455 is not listed on IDEAS
    12. Farhani, Sahbi & Shahbaz, Muhammad, 2014. "What role of renewable and non-renewable electricity consumption and output is needed to initially mitigate CO2 emissions in MENA region?," Renewable and Sustainable Energy Reviews, Elsevier, vol. 40(C), pages 80-90.
    13. Thanassis Kazanas & Elias Tzavalis, 2011. "Unveiling the monetary policy rule in euro area," Working Papers 130, Bank of Greece.

    More about this item

    JEL classification:

    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation

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