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PanelTM: an R package for two- and three-way dynamic panel threshold regression model

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  • F. Marta L. Di Lascio

    (Free University of Bozen-Bolzano, Italy)

  • Selene Perazzini

    (Free University of Bozen-Bolzano, Italy)

Abstract

This paper presents the R package PanelTM, which provides tools for estimating twoand three-way dynamic panel threshold regression models. Estimation is performed using a generalized method of moments approach based on first-difference transformations and instrumental variables as developed by Seo and Shin (2016) and applied in a threeway fashion by Di Lascio and Perazzini (2024, 2022). In addition to model estimation, PanelTM offers functionalities for change point detection, simulation and performance evaluation within panel structures with regime switches. The package is particularly suited to applications requiring the identification of structural breaks in complex panel data, with support for both exogenous and endogenous variables and for threshold heterogeneity across multiple dimensions.

Suggested Citation

  • F. Marta L. Di Lascio & Selene Perazzini, 2025. "PanelTM: an R package for two- and three-way dynamic panel threshold regression model," BEMPS - Bozen Economics & Management Paper Series BEMPS113, Faculty of Economics and Management at the Free University of Bozen.
    • Handle: RePEc:bzn:wpaper:bemps113
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    References listed on IDEAS

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    1. Bruce E. Hansen, 2000. "Sample Splitting and Threshold Estimation," Econometrica, Econometric Society, vol. 68(3), pages 575-604, May.
    2. Seo, Myung Hwan & Shin, Yongcheol, 2016. "Dynamic panels with threshold effect and endogeneity," Journal of Econometrics, Elsevier, vol. 195(2), pages 169-186.
    3. Laszlo Balazsi & Laszlo Matyas & Tom Wansbeek, 2018. "The estimation of multidimensional fixed effects panel data models," Econometric Reviews, Taylor & Francis Journals, vol. 37(3), pages 212-227, March.
    4. Hansen, Bruce E., 1999. "Threshold effects in non-dynamic panels: Estimation, testing, and inference," Journal of Econometrics, Elsevier, vol. 93(2), pages 345-368, December.
    5. Croissant, Yves & Millo, Giovanni, 2008. "Panel Data Econometrics in R: The plm Package," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 27(i02).
    6. Bates, Douglas & Mächler, Martin & Bolker, Ben & Walker, Steve, 2015. "Fitting Linear Mixed-Effects Models Using lme4," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 67(i01).
    7. Manuel Arellano & Stephen Bond, 1991. "Some Tests of Specification for Panel Data: Monte Carlo Evidence and an Application to Employment Equations," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 58(2), pages 277-297.
    8. Hsiao, Cheng & Zhang, Junwei, 2015. "IV, GMM or likelihood approach to estimate dynamic panel models when either N or T or both are large," Journal of Econometrics, Elsevier, vol. 187(1), pages 312-322.
    9. Blundell, Richard & Bond, Stephen, 1998. "Initial conditions and moment restrictions in dynamic panel data models," Journal of Econometrics, Elsevier, vol. 87(1), pages 115-143, August.
    10. Croissant, Yves & Millo, Giovanni, 2008. "Panel Data Econometrics in R: The plm Package," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 27(i02).
    11. Tony Lancaster, 2002. "Orthogonal Parameters and Panel Data," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 69(3), pages 647-666.
    12. Woosik Gong & Myung Hwan Seo, 2022. "Bootstraps for Dynamic Panel Threshold Models," Papers 2211.04027, arXiv.org, revised Sep 2024.
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    Keywords

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    JEL classification:

    • C87 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Econometric Software
    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
    • C23 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Models with Panel Data; Spatio-temporal Models
    • L66 - Industrial Organization - - Industry Studies: Manufacturing - - - Food; Beverages; Cosmetics; Tobacco

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