Computational Economics

, Volume 46, Issue 2, pp 231–241 | Cite as

How to use SETAR models in gretl

  • Federico Lampis
  • Ignacio Díaz-Emparanza
  • Anindya Banerjee


This paper presents a means for the diffusion of the Self-Exciting Threshold Autoregressive (SETAR) model. Based on the Hansen (Econometrica 68(3):675–603, 2000) methodology, we implement a function in gretl with which estimate a SETAR model. The function is provided with a nice graphical user interface that enables the average user to estimate a SETAR model and make inference easily. The function and its use is presented by means of a case study. In addition we show more functionalities of gretl in order to perform a preliminary analysis of the data.


SETAR models Free and open-source software gretl 



The first author gratefully acknowledges financial support from Regione Autonoma della Sardegna through Programma Master and Back. Financial support from research project ECO2010-15332 from Ministerio de Ciencia e Innovacion, and from UPV/EHU Econometrics Research Group, Basque Government grant IT-642-13, is gratefully acknowledged by the second author.


  1. Adkins, L. (2010). Using gretl for principles of econometrics, 3rd edition version 1.313.Google Scholar
  2. Baiocchi, G., & Distaso, W. (2003). Gretl: Econometric software for the gnu generation. Journal of applied econometrics, 18, 105–110.CrossRefGoogle Scholar
  3. Chan, K. S. (1993). Consistency and limiting distribution of a least squares estimator of a threshold autoregressive model. The Annals of Statistics, 21, 520533.CrossRefGoogle Scholar
  4. Cottrell, A., & Lucchetti, R. (2011). Gretl users guide gnu regression, econometrics and time-series library. Google Scholar
  5. Fan, Y., & Yao, Q. (2003). Nonlinear Time Series: Nonparametric and parametric Methods. New York: Springer.CrossRefGoogle Scholar
  6. Hansen, B. (1996). Inference when a nuisance parameter is not identified under the null hypotesis. Econometrica, 64, 413–430.CrossRefGoogle Scholar
  7. Hansen, B. (1997). Inference in tar models. Studies in Nonlinear Dynamics and Econometrics, 1, 895–904.Google Scholar
  8. Hansen, B. (2000). Sample splitting and threshold estimation. Econometrica, 68(3), 575–604.CrossRefGoogle Scholar
  9. Smith, R., & Mixon, J. (2006). Teaching undergraduate econometrics with GRETL. Journal of Applied Econometrics, 21(7), 1103–1107.CrossRefGoogle Scholar
  10. Tong, H. (1983). Threshold models in non-linear time series analysis. Lecture Notes in Statistics, No.21. Heidelberg: Springer.Google Scholar
  11. Tong, H. (1990). Nonlinear time series, a dynamical system approach. London: Oxford University Press.Google Scholar
  12. Tong, H., & Lim, K. (1980). Threshold autoregression, limit cycles and cyclical data (with discussion). Journal of the Royal Statistical Society, B42, 245–292.Google Scholar
  13. Yalta, A., & Yalta, Y. (2007). Gretl 1.6.0 and its numerical accuracy. Journal of Applied Econometrics, 22, 849–854.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2014

Authors and Affiliations

  • Federico Lampis
    • 1
  • Ignacio Díaz-Emparanza
    • 2
  • Anindya Banerjee
    • 1
  1. 1.University of BirminghamBirminghamUK
  2. 2.University of the Basque Country - UPV/EHUBilbaoSpain

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