Uncovering inflation patterns across brazilian metropolitan areas: a principal component analysis of IPCA-15
DOI:
https://doi.org/10.34140/bjbv8n2-027Palavras-chave:
inflation, economy, multivariate analysis, correlationResumo
The use of multivariate analysis techniques has proven to be a relevant tool for understanding complex patterns in economic data. From these approaches, it is possible to extract important information about the dynamics of inflation in different regions, contributing to comparative analyses and supporting decision-making. In the Brazilian context, the National Consumer Price Index 15 (IPCA-15) provides a monthly measure of price variation in various consumption categories in the country's main metropolitan regions. However, challenges remain in identifying structural patterns, especially when considering differences in variability between categories and the relative importance of each group in household budgets. Therefore, this study aimed to apply Principal Component Analysis (PCA) to monthly IPCA-15 data from March 2024 to February 2026, in order to identify similar inflationary profiles among cities. In summary, the present results show that the incorporation of official weights allows for an interpretation more aligned with economic reality, contributing to a more robust understanding of inflationary heterogeneity in Brazil. The paper's main findings draw the existence of distinct groups of regions with similar inflationary behaviors, as well as the differentiated influence of specific categories on inflationary dynamics.
Referências
Bezerra, D. M. (2024). Teoria econômica do poder de compra da moeda e a ameaça da inflação: estudo aplicado ao desconto intertemporal de utilidade da geração brasileira das décadas de 1980/90. (entre)linhas: Revista Do Programa De Pós-Graduação Em História Da Universidade Federal De Ouro Preto, 3(1), 20. Recuperado de https://periodicos.ufop.br/entrelinhas/article/view/6959
Brito, L. N., Lima, E. C. R., & Brandão, A. S. P. (2024). Inflação e nível de atividade no Brasil: estimativas da curva de Phillips: Inflation and the level of activity in Brazil: Phillips curve estimates. Brazilian Journal of Business, 6(1), 90–115. https://doi.org/10.34140/bjbv6n1-007
Camargo A. (2022). PCAtest: testing the statistical significance of Principal Component Analysis in R. PeerJ, 10, e12967. https://doi.org/10.7717/peerj.12967
Cangelosi, R., & Goriely, A. (2007). Component retention in principal component analysis with application to cDNA microarray data. Biology direct, 2, 2. https://doi.org/10.1186/1745-6150-2-2
Dantas, D. H. C. (2025). Economia monetária. Editora SENAC SP, 2025. v. 1. 132p.
Goretzko, D., & Bühner, M. (2022). Factor Retention Using Machine Learning with Ordinal Data. Applied psychological measurement, 46(5), 406–421. https://doi.org/10.1177/01466216221089345
Ekhosuehi, V. U., & Omoregie, D. E. (2021). A principal component analysis of multivariate data on inflation for Nigeria. Mathematica Applicanda, 48(2). https://doi.org/10.14708/ma.v48i2.7043
Guirguis, H., Dutra, V. B., & McGreevy, Z. (2022). The impact of global economies on US inflation: A test of the Phillips curve. Journal of Economics and Finance, 46(3), 575–592. https://doi.org/10.1007/s12197-022-09583-x
Silva, A.R. Métodos de análise multivariada em R. Piracicaba-SP, FEALQ (2016)
SILVA, E. M.; BORTOLON, P. M. Inflação: uma análise da relação entre a variação do valor da cesta básica de alimentos e o poder de compra regional. Pesquisa e Planejamento Econômico, Brasília, v. 55, n. 2, p. 141-177, ago. 2025. DOI: http://dx.doi.org/10.38116/ppe55n2art5
Machado, C. C., & Campos, F. K. R. (2023). Análise multivariada: introdução aos conceitos. Editora Intersaberes.
Oliveira, W. de. (2025). INFLAÇÃO, METAS E INSTRUMENTOS DE POLÍTICA MONETÁRIA. Revista Contemporânea, 5(9), e9156. https://doi.org/10.56083/RCV5N9-090
R Core Team (2025). R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria.
Sclove, S. L. (2022). Determining an Adequate Number of Principal Components. In Advances in Principal Component Analysis. IntechOpen. https://doi.org/10.5772/intechopen.104534
Sclove, S.L. Using Model Selection Criteria to Choose the Number of Principal Components. J Stat Theory Appl 20, 450–461 (2021). https://doi.org/10.1007/s44199-021-00002-4
Zschornack, T. et al. (2020). Análise Dos Índices Econômicos De Inflação Para Uso Como Indexadores Em Contratos De Consumo. Iberoamerican Journal of Industrial Engineering.
