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Causality Analysis: The study of Size and Power based on riz-PC Algorithm of Graph Theoretic Approach

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  • Fazal, Rizwan
  • Bhatti, M. Ishaq
  • Rehman, Atiq Ur

Abstract

This paper proposes modified Peter and Clark (PC) algorithm of graph-theoretic approach to study causality correlated data. The proposed algorithm is derived to determine the directions of the casual correlated complex variables. The PC algorithm treats VAR residuals as original variables while the proposed algorithm riz-PC uses modified R recursive residuals to find the correct causal direction among policy variables. This study evaluates the performance of these causal search algorithms in term of size and power properties. Our findings suggest that the newly proposed modified riz-PC algorithm can test causality better, as it successfully depicted the correct causal direction and was best at differentiating between true and spurious causality in routine Monte Carlo experiments.

Suggested Citation

  • Fazal, Rizwan & Bhatti, M. Ishaq & Rehman, Atiq Ur, 2022. "Causality Analysis: The study of Size and Power based on riz-PC Algorithm of Graph Theoretic Approach," Technological Forecasting and Social Change, Elsevier, vol. 180(C).
    • Handle: RePEc:eee:tefoso:v:180:y:2022:i:c:s0040162522002189
    DOI: 10.1016/j.techfore.2022.121691
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    References listed on IDEAS

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    1. Fazal, Rizwan & Rehman, Syed Aziz Ur & Rehman, Atiq Ur & Bhatti, Muhammad Ishaq & Hussain, Anwar, 2021. "Energy-environment-economy causal nexus in Pakistan: A graph theoretic approach," Energy, Elsevier, vol. 214(C).
    2. Selva Demiralp & Kevin D. Hoover & Stephen J. Perez, 2008. "A Bootstrap Method for Identifying and Evaluating a Structural Vector Autoregression," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 70(4), pages 509-533, August.
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    4. Selva Demiralp & Kevin D. Hoover, 2003. "Searching for the Causal Structure of a Vector Autoregression," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 65(s1), pages 745-767, December.
    5. Rehman, Atiq-ur- & Malik, Muhammad Irfan, 2014. "The modified R a robust measure of association for time series," MPRA Paper 60025, University Library of Munich, Germany.
    6. Hoover,Kevin D., 2001. "Causality in Macroeconomics," Cambridge Books, Cambridge University Press, number 9780521002882.
    7. Mazzarisi, Piero & Zaoli, Silvia & Campajola, Carlo & Lillo, Fabrizio, 2020. "Tail Granger causalities and where to find them: Extreme risk spillovers vs spurious linkages," Journal of Economic Dynamics and Control, Elsevier, vol. 121(C).
    8. Kevin D. Hoover, 2020. "The Discovery of Long-Run Causal Order: A Preliminary Investigation," Econometrics, MDPI, vol. 8(3), pages 1-25, August.
    9. Franck Jovanovic & Philippe Le Gall, 2021. "Mathematical Analogies: An Engine for Understanding the Transfers between Economics and Physics," Post-Print hal-03557745, HAL.
    10. Rizwan Fazal & Syed Aziz Ur Rehman & Muhammad Ishaq Bhatti & Atiq Ur Rehman & Fariha Arooj & Umar Hayat, 2021. "A Cross-Sectoral Investigation of the Energy–Environment–Economy Causal Nexus in Pakistan: Policy Suggestions for Improved Energy Management," Energies, MDPI, vol. 14(17), pages 1-22, September.
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    1. Fazal, Rizwan & Rehman, Syed Aziz Ur & Bhatti, M. Ishaq, 2022. "Graph theoretic approach to expose the energy-induced crisis in Pakistan," Energy Policy, Elsevier, vol. 169(C).

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