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

Jan Ditzen

Stata Journal, 2021, vol. 21, issue 3, 687-707

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).

Keywords: xtdcce2; xtcse2; xtcd2; parameter heterogeneity; dynamic panels; cross-section dependence; common-correlated effects; pooled mean-group estimator; mean-group estimator; error-correction model; ardl; long-run coefficients (search for similar items in EconPapers)
Date: 2021
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DOI: 10.1177/1536867X211045560

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