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A Network Analysis of the United Kingdom’s Consumer Price Index

Author

Listed:
  • Georgios Sarantitis

    (Democritus University of Thrace, Department of Economics)

  • Theophilos Papadimitriou

    (Democritus University of Thrace, Department of Economics)

  • Periklis Gogas

    (Democritus University of Thrace, Department of Economics)

Abstract

In this paper we model the United Kingdom’s Consumer Price Index as a complex network and we apply clustering and optimization techniques to study the network evolution through time. By doing this, we provide a dynamic, multi-level analysis of the mechanism that drives inflation in the U.K. We find that the CPI-classes’ network exhibits an evolving topology through time which depends substantially on the prevailing economic conditions in the U.K. We identify non-overlapping communities of these U.K. CPI classes and we observe that they do not correspond to the actual categories they belong into; a finding that suggests that diverse forces are driving the inter-relations of the CPI classes which are stronger between categories rather than within them. Finally, we present a reduced version of the U.K. CPI that fulfills the core inflation measure criteria and can possibly be used as an appropriate measure of the underlying inflation in the U.K. Since this new measure makes use of only 14 out of the 85 U.K. CPI classes, it can be used to complement the Bank of England’s arsenal of core inflation measures without the need for further resource allocation.

Suggested Citation

  • Georgios Sarantitis & Theophilos Papadimitriou & Periklis Gogas, 2015. "A Network Analysis of the United Kingdom’s Consumer Price Index," DUTH Research Papers in Economics 1-2016, Democritus University of Thrace, Department of Economics.
    • Handle: RePEc:ris:duthrp:2016_001
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    2. Wu, Tao & Sun, Xiaotong & Xu, Xin & Jia, Nanfei & Xuan, Siyuan, 2024. "New evidence of interdependence in forex markets: A connection of connection analysis," International Review of Financial Analysis, Elsevier, vol. 95(PA).
    3. Zhao, Yiran & Gao, Xiangyun & Zheng, Huiling & Zhang, Yupeng & Sun, Qingru & Wang, Anjian & An, HaiZhong, 2025. "Identifying influence pathways of oil price shocks on inflation based on impulse response networks," Energy, Elsevier, vol. 314(C).
    4. Michail Tsagris, 2021. "A New Scalable Bayesian Network Learning Algorithm with Applications to Economics," Computational Economics, Springer;Society for Computational Economics, vol. 57(1), pages 341-367, January.
    5. Sun, Qingru & Gao, Xiangyun & Wen, Shaobo & Chen, Zhihua & Hao, Xiaoqing, 2018. "The transmission of fluctuation among price indices based on Granger causality network," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 506(C), pages 36-49.
    6. Emiliano Alvarez & Juan Gabriel Brida & Pablo Mones, 2024. "On the Dynamics of Relative Prices and the Relationship with Inflation: An Empirical Approach," Computational Economics, Springer;Society for Computational Economics, vol. 63(1), pages 339-355, January.
    7. Qingru Sun & Xiangyun Gao & Ze Wang & Siyao Liu & Sui Guo & Yang Li, 2020. "Quantifying the risk of price fluctuations based on weighted Granger causality networks of consumer price indices: evidence from G7 countries," Journal of Economic Interaction and Coordination, Springer;Society for Economic Science with Heterogeneous Interacting Agents, vol. 15(4), pages 821-844, October.

    More about this item

    Keywords

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    JEL classification:

    • C61 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Optimization Techniques; Programming Models; Dynamic Analysis
    • E31 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Price Level; Inflation; Deflation
    • E52 - Macroeconomics and Monetary Economics - - Monetary Policy, Central Banking, and the Supply of Money and Credit - - - Monetary Policy

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