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Revisiting the Democracy-Growth Nexus:New Evidence from a Dynamic Common Correlated Effects Approach

Author

Listed:
  • Mohitosh Kejriwal
  • Haiqing Zhao

Abstract

This paper presents new evidence on the nexus between democracy and growth employing the dynamic common correlated e ects (DCCE) approach advanced by Chudik and Pesaran (2015) which is robust to both parameter heterogeneity and cross-section dependence.

Suggested Citation

  • Mohitosh Kejriwal & Haiqing Zhao, 2019. "Revisiting the Democracy-Growth Nexus:New Evidence from a Dynamic Common Correlated Effects Approach," Purdue University Economics Working Papers 1317, Purdue University, Department of Economics.
    • Handle: RePEc:pur:prukra:1317
    as

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    File URL: https://business.purdue.edu/research/working-papers-series/2019/1317.pdf
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    References listed on IDEAS

    as
    1. Chudik, Alexander & Pesaran, M. Hashem, 2015. "Common correlated effects estimation of heterogeneous dynamic panel data models with weakly exogenous regressors," Journal of Econometrics, Elsevier, vol. 188(2), pages 393-420.
    2. Castagnetti, Carolina & Rossi, Eduardo & Trapani, Lorenzo, 2015. "Inference on factor structures in heterogeneous panels," Journal of Econometrics, Elsevier, vol. 184(1), pages 145-157.
    3. M. Hashem Pesaran, 2015. "Testing Weak Cross-Sectional Dependence in Large Panels," Econometric Reviews, Taylor & Francis Journals, vol. 34(6-10), pages 1089-1117, December.
    4. Ando, Tomohiro & Bai, Jushan, 2015. "A simple new test for slope homogeneity in panel data models with interactive effects," Economics Letters, Elsevier, vol. 136(C), pages 112-117.
    Full references (including those not matched with items on IDEAS)

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    Cited by:

    1. Ryan H. Murphy & Colin O’Reilly, 2023. "Freedom through taxation: the effect of fiscal capacity on the rule of law," European Journal of Law and Economics, Springer, vol. 56(1), pages 69-90, August.

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    More about this item

    JEL classification:

    • C23 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Models with Panel Data; Spatio-temporal Models
    • C33 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Models with Panel Data; Spatio-temporal Models
    • C38 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Classification Methdos; Cluster Analysis; Principal Components; Factor Analysis

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