On the Analysis and Modelling of the Harmonized Consumer Price Indices of West African Economic and Monetary Union Member States
American Journal of Theoretical and Applied Statistics
Volume 9, Issue 6, November 2020, Pages: 283-295
Received: Oct. 23, 2020; Accepted: Nov. 6, 2020; Published: Nov. 19, 2020
Views 8      Downloads 6
Authors
Joseph Koula, Department of Mathematics and Computer Science, National Polytechnic Institute Felix Houphouet-Boigny, Yamoussoukro, Cote d’Ivoire
Tagouelbe Tiho, Department of Agriculture and Animal Resources, National Polytechnic Institute Felix Houphouet-Boigny, Yamoussoukro, Cote d’Ivoire
Adasse Christophe Chiapo, Department of Management, Business and Applied Economics, National Polytechnic Institute Felix Houphouet-Boigny, Yamoussoukro, Cote d’Ivoire
Article Tools
Follow on us
Abstract
The major goal of this paper is a better understanding of the price dynamics of the eight West African Economic and Monetary Union (WAEMU) member states. More specifically, the study intends to find the best models with suitable forecasting power for the monthly Harmonized Consumer Price Indices (HCPI) of each of the WAEMU countries. Descriptive statistics and time series modeling approaches were applied to the HCPI base 100=2008 series covering the period from January 1998 to December 2019. The analysis revealed that Guinea-Bissau had the highest average HCPI of 99.88 and Senegal the lowest of 93.73. Togo attained the highest HCPI of 119.60 and Benin the lowest of 71.54 over the period studied. The indices of Togo and Guinea-Bissau have the highest and the smallest variance of 225.56 and 79.60, respectively. All the indices have an upward trend and contain cyclical and seasonal components. Using the Box-Jenkins methodology and Expert Modeler of SPSS five types of outliers, i.e. additive, additive patch, transient, innovational and level change, have been detected and different SARIMA models were proposed. Bartlett's B-test detects significant periodic effects in the residuals of the models for Burkina-Faso and Côte d’Ivoire. The residuals of all the models have been declared Gaussian by Shapiro-Wilks and Jarque-Bera normality tests while those of Côte d’Ivoire fail the latest test for normality due to the discrepancy of their skewness with that of a normal distribution. Adequacy of the claimed models has been corroborated by adequate values of key fitting and predicting statistics and the non-significance of the paired t-test on the mean difference between the observed and the adjusted values. Thus SARIMA (0,1,0) (0,1,1)12 model was found to best fit the HCPI for Burkina Faso, Côte d'Ivoire, Niger, Senegal and Togo; and the data for Benin, Guinea Bissau and Mali are found to be SARIMA (3,1,0) (1,0,1)12, SARIMA (0,1,0) (1,0,1)12 and SARIMA (1,1,1) (0,1,1)12 process, respectively. The differences between the retained models raise doubts on the claimed objective of convergence of the economies of the WAEMU countries. Engle's Lagrange Multiplier test for autoregressive conditional heteroscedasticity (ARCH) reveals the homoscedasticity of the residuals of all the models but the one of Côte d'Ivoire. Thus, for better modeling of the index of Côte d’Ivoire, a GARCH model may be envisioned.
Keywords
Harmonized Consumer Price Index, SARIMA, WAEMU, Outliers
To cite this article
Joseph Koula, Tagouelbe Tiho, Adasse Christophe Chiapo, On the Analysis and Modelling of the Harmonized Consumer Price Indices of West African Economic and Monetary Union Member States, American Journal of Theoretical and Applied Statistics. Vol. 9, No. 6, 2020, pp. 283-295. doi: 10.11648/j.ajtas.20200906.14
Copyright
Copyright © 2020 Authors retain the copyright of this article.
This article is an open access article distributed under the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
References
[1]
D. M. Toé. Prévision de l’inflation dans la zone UEMOA: une approche par composantes. BCEAO. Dept. Etudes Economiques et de la Monnaie, 2011, N° DER/11/02.
[2]
G. E. P. Box, G. M. Jenkins, and G. C. Reinsel, Time series analysis: forecasting and control. 4th ed. Hoboken, NJ: Wiley, 2008, p. 765.
[3]
E. Aidoo. Modeling and forecasting inflation rates in Ghana: an application of SARIMA models, 2010, Unpublished Master Thesis for Degree in Applied Statistics, School of Technology and Business Studies, Hogskolan Dalarna.
[4]
Gikungu, S. W., Waititu, A. G., and Kihoro, J. M (2015). Forecasting inflation rate in Kenya using SARIMA model. American Journal of Theoretical and Applied Statistics; 4 (1): 15-18.
[5]
Etuk, H. E. (2012). Seasonal Arima Model to Nigerian consumer price index data. American Journal of Science and Industrial Research, 3 (5): 283-287.
[6]
Habimana, N., Wanjoya, A., and Waititu, A. (2016). Modeling and Forecasting Consumer Price Index (Case of Rwanda). American Journal of Theoretical and Applied Statistics, 5 (3): 101-107.
[7]
Akpanta, A. C. and Okorie, I. E. (2015) On the Time Series Analysis of Consumer Price Index data of Nigeria -1996 to 2013. American Journal of Economics 2015, 5 (3): 363-369.
[8]
R. H. Shumway and D. S. Stoffer, Time Series Analysis and Its application, Springer, New-York, Berlin, 2000, p. 568.
[9]
IBM, (2016). IBM SPSS Statistics 24 Algorithms. Copyright IBM Corporation 1989, 2016.
[10]
Tsasa, J.-P. K. (2014) Test de racine unité et analyse des ruptures. Tendance stochastique, correction [non] paramétrique et stratégie de détection, Société congolaise d’économétrie, papier SCE 3, 81-111.
[11]
Perron, P. (1989). The great crash, the oil price shock, and the unit root hypothesis, Econometrica, 57, 1361-1401.
[12]
Clemente, J., Montañés A., and Reyes M. (1998). Testing for a unit root in variables with a double change In The mean, Economics Letters, 59, 175-182.
[13]
Kapetanios, G. (2002), Unit root testing against the alternative hypothesis of up to m structural breaks, Working Papers 469, Queen Mary, University of London, Department of Economics.
[14]
R. Kaiser, and A. Maravall, Seasonal outliers in time series. Banco d’Espana-Servicio de Estudios, Documento de Trabajo n' 9915, 2001, p. 37.
[15]
Fox, A. J. (1972). Outliers in Time Series. Journal of Royal Statistics Society, B (34), 350-363.
[16]
Chen; C., and Liu, L. (1993). Joint Estimation of Model Parameters and Outliers in Time Series, Journal of the American Statistical Association, 88, 284-297.
[17]
D. Pena, Outliers, Influential Observations, and Missing Data, Chapter 6 in D. Peña, G. C. Tiao, and R. S. Tsay, A Course in Time Series Analysis, 2001, pp: 136-170.
[18]
IBM, (2015). IBM SPSS Forecasting 24. Copyright IBM France 2015.
[19]
Tsay, S. R. (1988). Outliers, Level Shifts, and Variance Changes in Time Series; Journal of Forecasting, 7, 1-20.
[20]
C. Chatfield, The Analysis of Time Series: An Introduction. 6th ed. Boca Raton, FL: Chapman & Hall/CRC, 2004, p. 338.
[21]
G. M. Jenkins and D. G. Watts, “Spectral Analysis and Its Applications,” Holden-Day, San Francisco, 1968, p. 525.
[22]
Etuk, H. E. (2013). Multiplicative SARIMA modeling of daily naira –euro exchange rates. International Journal of Mathematics and Statistics Studies, 1 (3): 1-8.
[23]
Yap, B. W., and Sim, C. H. (2011). Comparison of Various Types of Normality Tests. Journal of Statistical Computation and Simulation, 81 (12): 2141-2155.
ADDRESS
Science Publishing Group
1 Rockefeller Plaza,
10th and 11th Floors,
New York, NY 10020
U.S.A.
Tel: (001)347-983-5186