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Estimating long-run effects and the exponent of cross-sectional dependence: An update to xtdcce2

Author

Listed:
  • Jan Ditzen

    (Free University of Bozen-Bolzano)

Abstract

In this article, I describe several updates to xtdcce2 (Ditzen, 2018, Stata Journal 18: 585–617). First, I explain how to estimate long-run effects in models with cross-sectional dependence. I review three methods to estimate the long-run effects and discuss their implementation into Stata using xtdcce2. Two of the estimation methods build on Chudik et al. (2016, Advances in Econometrics: Vol. 36—Essays in Honor of Aman Ullah, 85–135): the cross-sectionally augmented distributed lag and the cross-sectionally augmented autoregressive distributed lag estimator. As a third alternative, I review an error-correction model in the presence of cross-sectional dependence. Second, I explain how to estimate the exponent of cross-sectional dependence using xtcse2 following Bailey, Kapetanios, and Pesaran (2016, Journal of Applied Econometrics 31: 929–960; 2019, Sankhyá 81: 46–102).

Suggested Citation

  • Jan Ditzen, 2021. "Estimating long-run effects and the exponent of cross-sectional dependence: An update to xtdcce2," Stata Journal, StataCorp LLC, vol. 21(3), pages 687-707, September.
    • Handle: RePEc:tsj:stataj:v:21:y:2021:i:3:p:687-707
    DOI: 10.1177/1536867X211045560
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    JEL classification:

    • C33 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Models with Panel Data; Spatio-temporal Models
    • C87 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Econometric Software

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