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Replication and Robustness Analysis of 'Energy and Economic Growth in the USA: A Multivariate Approach'

Author

Listed:
  • Stephan B. Bruns
  • Johannes König
  • David I. Stern

Abstract

We replicate Stern (1993, Energy Economics), who argues and empirically demonstrates that it is necessary (i) to use quality-adjusted energy use and (ii) to include capital and labor as control variables in order to find Granger causality from energy use to GDP. Though we could not access the original dataset, we can verify the main original inferences using data that are as close as possible to the original. We analyze the robustness of the original findings to alternative definitions of variables, model specifications, and estimation approach for both the (almost) original time span (1949- 1990) and an extended time span (1949-2015). p-values tend to be substantially smaller if energy use is quality adjusted rather than measured by total joules and if capital is included. Including labor has mixed results. These findings tend to largely support Stern's (1993) two main conclusions and emphasize the importance of accounting for changes in the energy mix in time series modeling of the energy-GDP relationship and controlling for other factors of production. We also discuss how the inclusion of the original author in designing the replication study using a pre-analysis plan can help to counterbalance the incentive of replicating authors to disconfirm major findings of the original article to increase the probability of getting published.

Suggested Citation

  • Stephan B. Bruns & Johannes König & David I. Stern, 2018. "Replication and Robustness Analysis of 'Energy and Economic Growth in the USA: A Multivariate Approach'," CAMA Working Papers 2018-18, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University.
  • Handle: RePEc:een:camaaa:2018-18
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    File URL: https://crawford.anu.edu.au/sites/default/files/2025-01/18_2018_bruns_konig_stern.pdf
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    Blog mentions

    As found by EconAcademics.org, the blog aggregator for Economics research:
    1. Annual Review 2018
      by noreply@blogger.com (David Stern) in Stochastic Trend on 2018-12-23 02:35:00
    2. Annual Review 2019
      by noreply@blogger.com (David Stern) in Stochastic Trend on 2019-12-25 00:24:00

    Citations

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    Cited by:

    1. T. Daniel Coggin, 2023. "CO2, SO2 and economic growth: a cross-national panel study," Journal of Economics and Finance, Springer;Academy of Economics and Finance, vol. 47(2), pages 437-457, June.
    2. Bruns, Stephan B. & Kalthaus, Martin, 2020. "Flexibility in the selection of patent counts: Implications for p-hacking and evidence-based policymaking," Research Policy, Elsevier, vol. 49(1).
    3. Hoang, Thi Hong Van & Shahzad, Syed Jawad Hussain & Czudaj, Robert L., 2020. "Renewable energy consumption and industrial production: A disaggregated time-frequency analysis for the U.S," Energy Economics, Elsevier, vol. 85(C).
    4. Bruns, Stephan B. & Asanov, Igor & Bode, Rasmus & Dunger, Melanie & Funk, Christoph & Hassan, Sherif M. & Hauschildt, Julia & Heinisch, Dominik & Kempa, Karol & König, Johannes & Lips, Johannes & Verb, 2019. "Reporting errors and biases in published empirical findings: Evidence from innovation research," Research Policy, Elsevier, vol. 48(9), pages 1-1.
    5. Chen, Huangxin & Shi, Yi & Zhao, Xin, 2022. "Investment in renewable energy resources, sustainable financial inclusion and energy efficiency: A case of US economy," Resources Policy, Elsevier, vol. 77(C).

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    JEL classification:

    • Q43 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Energy and the Macroeconomy
    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection

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