Forecasting the Consumer Price Index (CPI) of Ecuador: A Comparative Study of Predictive Models

Juan Riofrío, Oscar Chang, E. J. Revelo-Fuelagán, Diego H. Peluffo-Ordóñez

Abstract


The Consumer Price Index (CPI) is one of the most important economic indicators for countries’ characterization and is typically considered an official measure of inflation. The CPI considers the monthly price variation of a determined set of goods and services in a specific region, and it is key in the economic and social planning of a given country, hence the great importance of CPI forecasting. In this paper, we outline a comparative study of state-of-the-art predictive models over an Ecuadorian CPI dataset with 174 monthly registers, from 2005 to 2019. This small available dataset makes forecasting a challenging time-series-prediction task. Another difficulty is last year´s trend variation, which since mid-2016, has changed from an upward average of 3.5 points to a stable trend of ±0.8 points. This paper explores the performance of relevant predictive models when tackling the Ecuadorian CPI forecasting problem accurately for the next 12 months. For this, a comparative study considering a variety of predictive models is carried out, including the Neural networks approach using a Sequential Model with Long Short-Term Memory layers machine learning using Support Vector Regression, as well as classical approaches like SARIMA and Exponential Smoothing. We also consider big corporations´ tools like Facebook Prophet. As a result, the paper presents the best predictive models, and parameters found, along with Ecuador´s CPI forecasting for the next 12 months (part of 2020). This information could be used for decision-making in several important topics related to social and economic activities.


Keywords


Consumer Price Index (CPI); Ecuador; predictive models; forecasting.

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References


M. A. Terán Saltos, “Discursos sobre las causas de la inflación en una economía dolarizada.” 2002.

Banco Central del Ecuador(BCE)., “Ecuador: Reporte mensual de inflación.” 2018.

Instituto Nacional de Estadisticas y Censos(INEC)., “Metodología del Índice de Precios al Consumidor (IPC) Base Anual: 2014 = 100.” 2015.

S. S. Sharma, “Can consumer price index predict gold price returns?,” Econ. Model., vol. 55, pp. 269–278, Jun. 2016.

A. V Babkin, E. P. Karlina, and N. S. Epifanova, “Neural networks as a tool of forecasting of socioeconomic systems strategic development,” Procedia-Social Behav. Sci., vol. 207, pp. 274–279, 2015.

Instituto Nacional de estadistica y censos, “El índice de Precios al Consumidor (IPC),” 2020. [Online]. Available: https://www.ecuadorencifras.gob.ec/indice-de-precios-al-consumidor/.

A. J. Smola and B. Schölkopf, “A tutorial on support vector regression,” Statistics and Computing, vol. 14, no. 3. pp. 199–222, Aug-2004.

S. Hochreiter and J. Schmidhuber, “Long Short-Term Memory,” Neural Comput., vol. 9, no. 8, pp. 1735–1780, Nov. 1997.

K. Greff, R. K. Srivastava, J. Koutn’ik, B. R. Steunebrink, and J. Schmidhuber, “LSTM: A search space odyssey,” IEEE Trans. neural networks Learn. Syst., vol. 28, no. 10, pp. 2222–2232, 2016.

G. E. P. Box and G. M. Jenkins, Time series analysis forecasting and control. San Francisco Holden-Day, 1970.

R. G. Brown and R. F. Meyer, “The Fundamental Theorem of Exponential Smoothing,” Oper. Res., vol. 9, no. 5, pp. 673–685, Oct. 1961.

S. J. Taylor and B. Letham, “Forecasting at scale,” Am. Stat., vol. 72, no. 1, pp. 37–45, 2018.

A. De Myttenaere, B. Golden, B. Le Grand, and F. Rossi, “Mean Absolute Percentage Error for regression models,” May 2016.

S. Hu, “Akaike information criterion,” Cent. Res. Sci. Comput., vol. 93, 2007.

X. Qin, M. Sun, X. Dong, and Y. Zhang, “Forecasting of China Consumer Price Index Based on EEMD and SVR Method,” in Proceedings - 2nd International Conference on Data Science and Business Analytics, ICDSBA 2018, 2018, pp. 329–333.

G. Cao and L. Wu, “Support vector regression with fruit fly optimization algorithm for seasonal electricity consumption forecasting,” Energy, vol. 115, pp. 734–745, Nov. 2016.




DOI: http://dx.doi.org/10.18517/ijaseit.10.3.10813

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