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Causality by Vote: Aggregating Evidence on Causal Relations in Economic Growth Processes

Author

Listed:
  • Manuel de Mier

    (UNS)

  • Fernando Delbianco

    (UNS/INMABB-CONICET)

  • Fernando Tohmé

    (UNS/INMABB-CONICET)

  • Luisina Patrizio

    (UNS)

  • Facundo Rodriguez

    (UNS)

  • Mauro Romero Stéfani

    (UNS)

Abstract

In this paper we investigate the performance of five causality-detection methods and how their results can be aggregated when multiple units are considered in a panel data setting. The aggregation procedure employs voting rules for determining which causal paths are identified for the sample population. Using simulated and real-world panel data, we show the performance of this methods in detecting the correct causal paths in comparison to a benchmark that comprises a standard representation of growth processes as ground truth model. We find that the results may be better when only simulated, instead of real-world, data are analyzed.While this may suggest that the methods presented here are are currently incapable of detecting causal links, it is plausible that the ground “truth” may incorporate false relations.

Suggested Citation

  • Manuel de Mier & Fernando Delbianco & Fernando Tohmé & Luisina Patrizio & Facundo Rodriguez & Mauro Romero Stéfani, 2023. "Causality by Vote: Aggregating Evidence on Causal Relations in Economic Growth Processes," Working Papers 260, Red Nacional de Investigadores en Economía (RedNIE).
    • Handle: RePEc:aoz:wpaper:260
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    File URL: https://rednie.eco.unc.edu.ar/files/DT/260.pdf
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    References listed on IDEAS

    as
    1. Burkhard Raunig, 2023. "Using causal graphs to test for the direction of instantaneous causality between economic policy uncertainty and stock market volatility," Empirical Economics, Springer, vol. 65(4), pages 1579-1598, October.
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    4. Stephan B. Bruns & David I. Stern, 2019. "Lag length selection and p-hacking in Granger causality testing: prevalence and performance of meta-regression models," Empirical Economics, Springer, vol. 56(3), pages 797-830, March.
    5. Dumitrescu, Elena-Ivona & Hurlin, Christophe, 2012. "Testing for Granger non-causality in heterogeneous panels," Economic Modelling, Elsevier, vol. 29(4), pages 1450-1460.
    6. Holtz-Eakin, Douglas & Newey, Whitney & Rosen, Harvey S, 1988. "Estimating Vector Autoregressions with Panel Data," Econometrica, Econometric Society, vol. 56(6), pages 1371-1395, November.
    7. Shurong Zheng & Ning-Zhong Shi & Zhengjun Zhang, 2012. "Generalized Measures of Correlation for Asymmetry, Nonlinearity, and Beyond," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 107(499), pages 1239-1252, September.
    8. Acemoglu, Daron, 2012. "Introduction to economic growth," Journal of Economic Theory, Elsevier, vol. 147(2), pages 545-550.
    Full references (including those not matched with items on IDEAS)

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    More about this item

    Keywords

    Granger causality; Transfer Entropy; Stochastic Causality; LiNGAM; Ground Truth; Economic Growth.;
    All these keywords.

    JEL classification:

    • C18 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Methodolical Issues: General
    • C43 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Index Numbers and Aggregation
    • O47 - Economic Development, Innovation, Technological Change, and Growth - - Economic Growth and Aggregate Productivity - - - Empirical Studies of Economic Growth; Aggregate Productivity; Cross-Country Output Convergence

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