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Multivariate Cuban Consumer Price Index Database, Statistic Analysis and Forecast Baseline Based on Vector Autoregressive

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Progress in Artificial Intelligence and Pattern Recognition (IWAIPR 2023)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 14335))

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Abstract

The global Consumer Price Index (CPI) is a monthly multivariate time series, which allows measuring the variation of the final consumer prices of a given set of goods and services of households living in a given geographic region, city or country. The present work addresses the problem of the multivariate time series database of Cuba’s CPI and a respective forecasting model based on Vector Autoregressive to establish a baseline for this dataset. An statistical analysis of the data will allow characterizing each variable of the series in terms of relevance to the multivariate problem, its causal relationships and the respective stationary analysis to evaluate the best lag to be considered in the forecasting model. The main statistics evidences of each test were reported in the paper as starting point for futures researches in the field of deep learning.

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References

  1. Akin, A.C., Cevrimli, M.B., Arikan, M.S., Tekindal, M.A.: Determination of the causal relationship between beef prices and the consumer price index in turkey. Turk. J. Vet. Anim. Sci. 43(3), 353–358 (2019)

    Article  Google Scholar 

  2. Banerjee, A.: Forecasting price levels in India-an Arima framework. Acad. Mark. Stud. J. 25(1), 1–15 (2021)

    MathSciNet  Google Scholar 

  3. Cheung, Y.-W., Lai, K.S.: Lag order and critical values of the augmented dickey-fuller test. J. Bus. Econ. Stat. 13(3), 277–280 (1995)

    Google Scholar 

  4. Cromwell, J.B.: Multivariate Tests for Time Series Models. Number 100. Sage (1994)

    Google Scholar 

  5. Diewert, W.E.: Index number issues in the consumer price index. J. Econ. Perspect. 12(1), 47–58 (1998)

    Article  Google Scholar 

  6. García Molina, J.M.: La economía cubana a inicios del siglo XXI: desafíos y oportunidades de la globalización. CEPAL (2005)

    Google Scholar 

  7. Ghazo, A., et al.: Applying the ARIMA model to the process of forecasting GDP and CPI in the Jordanian economy. Int. J. Financ. Res. 12(3), 70 (2021)

    Article  Google Scholar 

  8. Granger, C.W.J.: Investigating causal relations by econometric models and cross-spectral methods. Econom.: J. Econom. Soc. 424–438 (1969)

    Google Scholar 

  9. Jere, S., Banda, A., Chilyabanyama, R., Moyo, E., et al.: Modeling consumer price index in Zambia: a comparative study between multicointegration and ARIMA approach. Open J. Stat. 9(02), 245 (2019)

    Article  Google Scholar 

  10. Korkmaz, S., Abdullazade, M.: The causal relationship between unemployment and inflation in g6 countries. Adv. Econ. Bus. 8(5), 303–309 (2020)

    Article  Google Scholar 

  11. Anaya, L.M.L., Moreno, V.M.L., Aguirre, H.R.O., López, M.Q.: Predicción del ipc mexicano combinando modelos econométricos e inteligencia artificial. Rev. Mexicana Econ. Finanzas 13(4), 603–629 (2018)

    Article  Google Scholar 

  12. Mallick, L., Behera, S.R., Dash, D.P.: Does CPI granger cause WPI? Empirical evidence from threshold cointegration and spectral granger causality approach in India. J. Dev. Areas 54(2) (2020)

    Google Scholar 

  13. Manik, D.P., et al.: A strategy to create daily consumer price index by using big data in statistics Indonesia. In: 2015 International Conference on Information Technology Systems and Innovation (ICITSI), pp. 1–5. IEEE (2015)

    Google Scholar 

  14. Mohamed, J.: Time series modeling and forecasting of Somaliland consumer price index: a comparison of ARIMA and regression with ARIMA errors. Am. J. Theor. Appl. Stat. 9(4), 143–53 (2020)

    Article  Google Scholar 

  15. Nakamura, E., Steinsson, J.: Identification in macroeconomics. J. Econ. Perspect. 32(3), 59–86 (2018)

    Article  Google Scholar 

  16. Nyoni, T.: Modeling and forecasting inflation in Kenya: Recent insights from ARIMA and GARCH analysis. Dimorian Rev. 5(6), 16–40 (2018)

    Google Scholar 

  17. Nyoni, T.: ARIMA modeling and forecasting of consumer price index (CPI) in Germany (2019)

    Google Scholar 

  18. ONEI. Índice de precios al consumidor base diciembre 2010 (2022)

    Google Scholar 

  19. Qin, X., Sun, M., Dong, X., Zhang, Y.: Forecasting of china consumer price index based on EEMD and SVR method. In: 2018 2nd International Conference on Data Science and Business Analytics (ICDSBA), pp. 329–333. IEEE (2018)

    Google Scholar 

  20. Riofrío, J., Chang, O., Revelo-Fuelagán, E.J., Peluffo-Ordóñez, D.H.: Forecasting the consumer price index (CPI) of Ecuador: a comparative study of predictive models. Int. J. Adv. Sci. Eng. Inf. Technol. 10(3), 1078–1084 (2020)

    Article  Google Scholar 

  21. Rosado, R., Abreu, A.J., Arencibia, J.C., Gonzalez, H., Hernandez, Y.: Consumer price index forecasting based on univariate time series and a deep neural network. In: Hernández Heredia, Y., Milián Núñez, V., Ruiz Shulcloper, J. (eds.) IWAIPR 2021. LNCS, vol. 13055, pp. 33–42. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-89691-1_4

    Chapter  Google Scholar 

  22. Sünbül, E.: Linear and nonlinear relationship between real exchange rate, real interest rate and consumer price index: an empirical application for countries with different levels of development. Sci. Ann. Econ. Bus. 70(1), 57–70 (2023)

    Article  Google Scholar 

  23. Torres, J.F., Hadjout, D., Sebaa, A., Martinez-Alvarez, F., Troncoso, A.: Deep learning for time series forecasting: a survey. Big Data 9(1), 3–21 (2021)

    Article  Google Scholar 

  24. Triplett, J.: Handbook on Hedonic Indexes and Quality Adjustments in Price Indexes: Special Application to Information Technology Products (2004)

    Google Scholar 

  25. Zahara, S., Ilmiddaviq, M.B., et al.: Consumer price index prediction using long short term memory (LSTM) based cloud computing. J. Phys.: Conf. Ser. 1456, 012022 (2020)

    Google Scholar 

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Acknowledgement

This work has been partially funded by FONCI through project: Plataforma para el anélisis de grandes volúmenes de datos y su aplicación a sectores estratégicos.

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Authors and Affiliations

  1. Universidad de las Ciencias Informaticas (UCI), La Habana, Cuba

    Reynaldo Rosado, Héctor González Diéz, Orlando Grabiel Toledano-López & Yanio Hernández Heredia

Authors
  1. Reynaldo Rosado
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  2. Héctor González Diéz
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  3. Orlando Grabiel Toledano-López
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  4. Yanio Hernández Heredia
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Corresponding author

Correspondence to Héctor González Diéz .

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Editors and Affiliations

  1. Universidad de las Ciencias Informáticas, Havana, Cuba

    Yanio Hernández Heredia

  2. Universidad de las Ciencias Informáticas, Havana, Cuba

    Vladimir Milián Núñez

  3. Universidad de las Ciencias Informáticas, Havana, Cuba

    José Ruiz Shulcloper

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Rosado, R., González Diéz, H., Toledano-López, O.G., Hernández Heredia, Y. (2024). Multivariate Cuban Consumer Price Index Database, Statistic Analysis and Forecast Baseline Based on Vector Autoregressive. In: Hernández Heredia, Y., Milián Núñez, V., Ruiz Shulcloper, J. (eds) Progress in Artificial Intelligence and Pattern Recognition. IWAIPR 2023. Lecture Notes in Computer Science, vol 14335. Springer, Cham. https://doi.org/10.1007/978-3-031-49552-6_3

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  • DOI: https://doi.org/10.1007/978-3-031-49552-6_3

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  • Online ISBN: 978-3-031-49552-6

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