1. MMC
  2. All Volumes and Issues
  3. Volume 12, Number 3, 2025
  4. Enhancing CPI accuracy: A comparative analysis of weighting techniques and consumer behaviour in Malaysia

Enhancing CPI accuracy: A comparative analysis of weighting techniques and consumer behaviour in Malaysia

MMC.
2025;
Volume 12, Number 3
: pp. 745–756
https://doi.org/10.23939/mmc2025.03.745
Received: December 15, 2024
Revised: May 21, 2025
Accepted: July 06, 2025

Zulkifli F., Deni M. S., Abidin R. Z., Zulkifli N. Z.  Enhancing CPI accuracy: A comparative analysis of weighting techniques and consumer behaviour in Malaysia.  Mathematical Modeling and Computing. Vol. 12, No. 3, pp. 745–756 (2025)

Authors:
  1. F. Zulkifli
  2. M. S. Deni
  3. R. Z. Abidin
  4. N. Z. Zulkifli
1
College of Computing, Informatics, and Mathematics, University Teknologi MARA
2
College of Computing, Informatics, and Mathematics, University Teknologi MARA
3
College of Computing, Informatics, and Mathematics, University Teknologi MARA
4
College of Computing, Informatics, and Mathematics, University Teknologi MARA; Stratos Pinnacle Sdn Bhd. Signature 2

This study addresses the limitations of traditional Consumer Price Index (CPI) calculation methods by proposing an improved framework that incorporates optimized weighting techniques.  The proposed approach utilizes nonlinear programming to better reflect consumer spending behavior in Malaysia, enhancing the accuracy and relevance of inflation measurement.  Empirical analyses reveal fluctuations between the newly generated weights and those provided by the Department of Statistics Malaysia (DOSM), largely influenced by consumer behavior, economic conditions, and supply-side factors.  Strong positive correlations between the traditional and new CPI indicate consistency, while comparative analyses of CPI trends across states uncover significant regional variations in inflation dynamics.  These findings underscore the need for more adaptive weighting approaches to better reflect evolving consumer habits.  Future research is encouraged to incorporate granular data, dynamic factors, and alternative weighting models to improve CPI methodologies further.  This study provides valuable insights for policymakers, contributing to more effective economic planning and decision-making.

consumer price index
weighting technique
consumer spending behaviour
nonlinear programming methods
  1. Fan Y., Sun Z.  CPI Big Data Prediction Based on Wavelet Twin Support Vector Machine.  International Journal of Pattern Recognition and Artificial Intelligence.  35 (4), 2159013 (2021).
  2. Konarasinghe K. M. U. B.  Modeling Consumer Price Index of Malaysia: Application of Exponential Smoothers.  Journal of New Frontiers in Education and Social Sciences.  2 (1), 16–33 (2022).
  3. Graf B.  Consumer Price Index Manual, 2020: Concepts and Methods. Vol. 21.  International Monetary Fund.  87–93 (2020).
  4. Mobius M.  The Inflation Myth and the Wonderful World of Deflation.  John Wiley & Sons (2020).
  5. Hannon J. B. Jr.  Data collection and quality assurance problems in consumer price indices.  Statistical Journal of the United Nations Economic Commission for Europe.  15 (1), 85–96 (1998).
  6. Zulkifli F., Abidin R. Z., Deni S. M.  A New Method for Calculating Consumer Price Indices: Incorporating Consumer Perceptions and Attitudes with Item Response Theory.  International Journal of Academic Research in Economics and Management and Sciences.  12 (1), 373–381 (2023).
  7. Samsudin M.  The Compilation of Consumer Price Index in Malaysia.  Department of Statistics Malaysia (2016).
  8. Department of Statistics Malaysia (DOSM).  Consumer Price Index Malaysia February 2023.  https://open.dosm.gov.my/publications?page=1.
  9. Dawber J., Würz N., Smith P. A., Flower T., Thomas H., Schmid T., Tzavidis N.  Experimental UK Regional Consumer Price Inflation with Model-Based Expenditure Weights.  Journal of Official Statistics.  38 (1), 213–237 (2022).
  10. Białek J., Berȩsewicz M.  Scanner data in inflation measurement: From raw data to price indices.  Statistical Journal of the IAOS.  37 (4), 1315–1336 (2021).
  11. Cavallo A.  Inflation with Covid Consumption Baskets.  IMF Economic Review. 1–18 (2023).
  12. Arion G., Beatriz M., Leclair M., Morer N., Sueur E.  Perceived Inflation, Measured Inflation: Are There Differences Between Categories of Households?     (2018).
  13. Bai Y., Costlow L., Ebel A., Laves S., Ueda Y., Volin N., et al.  Review: Retail consumer price data reveal gaps and opportunities to monitor food systems for nutrition.  Food Policy.  104, 102148 (2021).
  14. Reinsdorf M.  COVID-19 and the CPI: Is Inflation Underestimated?  SSRN Electronic Journal.  2020 (224), 1–31 (2020).
  15. Seiler P.  Weighting bias and inflation in the time of COVID-19: evidence from Swiss transaction data.  Swiss Journal of Economics and Statistics.  156 (1), 13 (2020).
  16. Diewert W. E., Fox K. J.  Measuring real consumption and consumer price index bias under lockdown conditions.  Canadian Journal of Economics.  55 (S1), 480–502 (2020).
  17. Dabalen A., Gaddis I., Nguyen N. T. V.  CPI Bias and its Implications for Poverty Reduction in Africa.  The Journal of Economic Inequality.  18 (1), 13–44 (2020).
  18. Mathelinea D., Chandrashekar R., Omar N. F. A. C.  Inventory cost optimization through nonlinear programming with constraint and forecasting techniques.  AIP Conference Proceedings.  2184, 040011 (2019).
  19. Salah F. E., Moussaid N.  Machine learning and similar image-based techniques based on Nash game theory.  Mathematical Modeling and Computing.  11 (1), 120–133 (2024).
  20. Zerroudi B., Tayeq H., El Harrak A.  Effective interpolation of scattered data on a sphere through a Shepard-like method.  Mathematical Modeling and Computing.  10 (4), 1174–1186 (2023).
  21. Lin Y. T., Osman S., Cheng K. W., Sam T. H., Ruiteng X., Rajanthran S. K.  Factors Influencing Live-stream Shopping Behaviour among Malaysian Consumers.  Res Militaris: Social Science Journal.  13 (2), 1548–1569 (2023).
  22. Zulkifli F., Abidin R. Z.  Identity in the Digital Age: An Investigation of Malaysian Perspectives on Technology and Privacy.  Journal of Advanced Research in Applied Sciences and Engineering Technology.  43 (2), 1–20 (2025).