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xtdcce: Estimating Dynamic Common Correlated Effects in Stata

Author

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  • Jan Ditzen

    (Heriot-Watt University)

Abstract

This article introduces a new Stata command, xtdcce, to estimate a dynamic common correlated effects model with heterogeneous coefficients. The estimation procedure mainly follows Chudik and Pesaran (2015b), in addition the common correlated effects estimator (Pesaran, 2006), as well as the mean group (Pesaran and Smith, 1995) and the pooled mean group estimator (Shin et al., 1999) are supported. Coefficients are allowed to be heterogeneous or homogeneous. In addition instrumental variable regressions and unbalanced panels are supported. The Cross Sectional Dependence Test (CD Test) is automatically calculated and presented in the estimation output. Small sample time series bias can be corrected by jackknife correction or recursive mean adjustment. Examples for empirical applications of all estimation methods mentioned above are given

Suggested Citation

  • Jan Ditzen, 2016. "xtdcce: Estimating Dynamic Common Correlated Effects in Stata," SEEC Discussion Papers 1601, Spatial Economics and Econometrics Centre, Heriot Watt University.
  • Handle: RePEc:hwe:seecdp:1601
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    File URL: http://seec.hw.ac.uk/images/discussionpapers/SEEC_DiscussionPaper_No8.pdf
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    References listed on IDEAS

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    1. Christopher F Baum & Mark E. Schaffer & Steven Stillman, 2003. "Instrumental variables and GMM: Estimation and testing," Stata Journal, StataCorp LP, vol. 3(1), pages 1-31, March.
    2. M. Hashem Pesaran, 2006. "Estimation and Inference in Large Heterogeneous Panels with a Multifactor Error Structure," Econometrica, Econometric Society, vol. 74(4), pages 967-1012, July.
    3. De Hoyos, Rafael E. & Sarafidis, Vasilis, 2006. "Testing for cross-sectional dependence in panel-data models," Stata Journal, StataCorp LP, vol. 0(Number 4), pages 1-15.
    4. Robert C. Feenstra & Robert Inklaar & Marcel P. Timmer, 2015. "The Next Generation of the Penn World Table," American Economic Review, American Economic Association, vol. 105(10), pages 3150-3182, October.
    5. Kapetanios, G. & Pesaran, M. Hashem & Yamagata, T., 2011. "Panels with non-stationary multifactor error structures," Journal of Econometrics, Elsevier, vol. 160(2), pages 326-348, February.
    6. Jan Ditzen & Erich Gundlach, 2016. "A Monte Carlo study of the BE estimator for growth regressions," Empirical Economics, Springer, vol. 51(1), pages 31-55, August.
    7. Lee, Kevin & Pesaran, M Hashem & Smith, Ron, 1997. "Growth and Convergence in Multi-country Empirical Stochastic Solow Model," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 12(4), pages 357-392, July-Aug..
    8. Edward F. Blackburne III & Mark W. Frank, 2007. "Estimation of nonstationary heterogeneous panels," Stata Journal, StataCorp LP, vol. 7(2), pages 197-208, June.
    9. Gerdie Everaert & Tom De Groote, 2016. "Common Correlated Effects Estimation of Dynamic Panels with Cross-Sectional Dependence," Econometric Reviews, Taylor & Francis Journals, vol. 35(3), pages 428-463, March.
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    11. N. Gregory Mankiw & David Romer & David N. Weil, 1992. "A Contribution to the Empirics of Economic Growth," The Quarterly Journal of Economics, Oxford University Press, vol. 107(2), pages 407-437.
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    Cited by:

    1. Diallo, Ibrahima Amadou, 2017. "The role of human assets in economic growth: theory and empirics," MPRA Paper 80402, University Library of Munich, Germany.
    2. Kyle McNabb, 2016. "Tax structures and economic growth: New evidence from the Government Revenue Dataset," WIDER Working Paper Series 148, World Institute for Development Economic Research (UNU-WIDER).

    More about this item

    Keywords

    xtdcce; parameter heterogeneity; dynamic panels; cross section dependence; common correlated effects; pooled mean-group estimator; mean-group estimator; instrumental variables; ivreg2;

    JEL classification:

    • C21 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models
    • C23 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Models with Panel Data; Spatio-temporal Models
    • C26 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Instrumental Variables (IV) Estimation
    • C87 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Econometric Software

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