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Distributions of GDP across versions of the Penn World Tables: A functional data analysis approach

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  • Chen, Tao
  • DeJuan, Joseph
  • Tian, Renfang

Abstract

Data in the Penn World Tables (PWT) have been subject to a series of revisions since its first release in the early 1990s, and the amendments are substantial for many countries. This paper uses functional data analysis to examine the distribution functions of GDP across different versions of the PWT. We find no support for distribution equality hypothesis, indicating that GDP in different versions do not share a common underlying distribution. This suggests, at the least, a need to use caution in drawing conclusions from a particular PWT version, and conduct appropriate sensitivity analysis to check the robustness of results.

Suggested Citation

  • Chen, Tao & DeJuan, Joseph & Tian, Renfang, 2018. "Distributions of GDP across versions of the Penn World Tables: A functional data analysis approach," Economics Letters, Elsevier, vol. 170(C), pages 179-184.
    • Handle: RePEc:eee:ecolet:v:170:y:2018:i:c:p:179-184
    DOI: 10.1016/j.econlet.2018.05.038
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    References listed on IDEAS

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    1. Johnson, Simon & Larson, William & Papageorgiou, Chris & Subramanian, Arvind, 2013. "Is newer better? Penn World Table Revisions and their impact on growth estimates," Journal of Monetary Economics, Elsevier, vol. 60(2), pages 255-274.
    2. Natalia Ponomareva & Hajime Katayama, 2010. "Does the version of the Penn World Tables matter? An analysis of the relationship between growth and volatility," Canadian Journal of Economics, Canadian Economics Association, vol. 43(1), pages 152-179, February.
    3. Gerda Claeskens & Tatyana Krivobokova & Jean D. Opsomer, 2009. "Asymptotic properties of penalized spline estimators," Biometrika, Biometrika Trust, vol. 96(3), pages 529-544.
    4. Breton, Theodore R., 2012. "Penn World Table 7.0: Are the data flawed?," Economics Letters, Elsevier, vol. 117(1), pages 208-210.
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    Cited by:

    1. Wang, You & Gong, Xu, 2020. "Does financial development have a non-linear impact on energy consumption? Evidence from 30 provinces in China," Energy Economics, Elsevier, vol. 90(C).
    2. Tao Chen & Yixuan Li & Renfang Tian, 2023. "A Functional Data Approach for Continuous-Time Analysis Subject to Modeling Discrepancy under Infill Asymptotics," Mathematics, MDPI, vol. 11(20), pages 1-27, October.

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    More about this item

    Keywords

    Bootstrap; Data quality; Functional data analysis; GDP; Penn World Table;
    All these keywords.

    JEL classification:

    • C12 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Hypothesis Testing: General
    • C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Statistical Simulation Methods: General
    • O47 - Economic Development, Innovation, Technological Change, and Growth - - Economic Growth and Aggregate Productivity - - - Empirical Studies of Economic Growth; Aggregate Productivity; Cross-Country Output Convergence
    • O50 - Economic Development, Innovation, Technological Change, and Growth - - Economywide Country Studies - - - General

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