Time Series Modeling and Forecasting of Somaliland Consumer Price Index: A Comparison of ARIMA and Regression with ARIMA Errors

Jama Mohamed

doi:doi:10.11648/j.ajtas.20200904.18

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Time Series Modeling and Forecasting of Somaliland Consumer Price Index: A Comparison of ARIMA and Regression with ARIMA Errors

Jama Mohamed

Published in American Journal of Theoretical and Applied Statistics (Volume 9, Issue 4)

Received: 11 June 2020 Accepted: 22 June 2020 Published: 13 July 2020

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Abstract

In recent years, the Consumer Price Index (CPI) prediction has attracted the attention of many researchers due to its excellent measurement of macroeconomic performance. It is an important index that is used to measure the rate of inflation or deflation of commodities. In this paper, Autoregressive Integrated Moving Average (ARIMA) and regression with ARIMA errors, where the covariate is the time, were compared to forecast Somaliland Consumer Price Index using monthly time series data from 2013 – 2020. The study used and applied both models to produce the necessary forecasts. Also, Akaike Information Criterion (AIC), Corrected Akaike Information Criterion (AICc), Bayesian Information Criterion (BIC) and other model accuracy measures were used to measure model’s predictive ability. By utilizing these methods, it is obtained that ARIMA (0, 1, 3) is the most suitable model for predicting CPI in Somaliland. Furthermore, the diagnostic tests show that the model presented is reliable and appropriate for forecasting Somaliland CPI data. The study results obviously indicate that CPI in Somaliland is more likely to proceed on an upward trend in the coming year. The study guides policymakers to use strict monetary and fiscal policy measures to address Somaliland’s inflation.

Published in	American Journal of Theoretical and Applied Statistics (Volume 9, Issue 4)
DOI	10.11648/j.ajtas.20200904.18
Page(s)	143-153
Creative Commons	This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.
Copyright	Copyright © The Author(s), 2020. Published by Science Publishing Group

Keywords

Augmented Dickey-Fuller Test, Autocorrelation, Autoregressive Integrated Moving Average (ARIMA), Consumer Price Index (CPI), Ljung-Box Test

References

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[3]	Costa, D. L. (2001) Estimating Real Income in the United States from 1888 to 1994: Correcting CPI Bias Using Engel Curves. Journal of Political Economy, Vol. 109, pp. 1288-1310.
[4]	Central Statistics Department. (2020). Consumer Price Index Publication. Ministry of National Planning and Development, Hargeisa, Somaliland.
[5]	Isakova, A. (2007). Modeling and Forecasting inflation in developing Countries: The case of Economies in Central Asia. Center for Economic Research and Graduate Education, Discussion Paper, (2007-174).
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[7]	Montgomery, D. C., Jennings, C. L., and Kulahci, M. (2008). Introduction to Time Series Analysis and Forecasting. New York: John Wiley & Sons.
[8]	Brocwell, P. J., and Davis, R. A. (2002). Introduction to time series and Forecasting. New York: Springer.
[9]	Xiao, Z. (2016). CPI Prediction Based on ARIMA Model. In 7th International Conference on Education, Management, Information and Computer Science (ICEMC 2017). Atlantis Press.
[10]	Chatfield, C. (2004). The Analysis of Time Series an Introduction Sixth Edition. New York: Chapman & Hall/CRC.
[11]	Wei, W. S. (2006). Time Series Analysis Univariate and Multivariate Methods Second Edition. Canada: Pearson education Inc.
[12]	Cryer, J. D., and Chan K. S. (2008). Time Series Analysis with Application in R. New York: Springer.
[13]	Box, G. E. P., and Jenkins, G. M. (1970). Time series analysis: Forecasting and control. Holden-Day, San Francisco.
[14]	Adam, S. O., Awujola, A., and Alumgudu, A. I., (2014). Modeling Nigeria’s CPI using ARIMA model. Int. Journal of Development and Economic Sustainability, Vol. 2, No. 2, pp. 37-47.
[15]	Hamid, S. A., and Dhakar, T. S. (2008). The behaviour of the US consumer price index 1913–2003: a study of seasonality in the monthly US CPI. Applied Economics, Vol. 40, No. 13, pp. 1637-1650.
[16]	Zhang, F. W., Che, W. G., Xu, B. B., and Xu, J. Z. (2013). The Research of ARMA Model in CPI Time Series. In Applied Mechanics and Materials (Vol. 347, pp. 3099-3103). Trans Tech Publications Ltd.
[17]	Kharimah, F., Usman, M., Widiarti, W., and Elfaki, F. A. M. (2015). Time series modeling and forecasting of the consumer price index Bandar Lampung. Science International Lahore, Vol. 27, No. 5, pp. 4619-4624.
[18]	Gujarati, D. (1995). Basic Econometrics. McGraw-Hill Inc., Singapore.
[19]	Ljung, G. M., and Box, G. E. (1978). On a measure of lack of fit in time series models. Biometrika, Vol. 65, No. 2, pp. 297-303.
[20]	Nyoni, T. (2018). Box –Jenkins ARIMA Approach to Predicting net FDI inflows in Zimbabwe, Munich University Library – Munich Personal RePEc Archive (MPRA), Paper No. 87737.

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APA Style

Jama Mohamed. (2020). Time Series Modeling and Forecasting of Somaliland Consumer Price Index: A Comparison of ARIMA and Regression with ARIMA Errors. American Journal of Theoretical and Applied Statistics, 9(4), 143-153. https://doi.org/10.11648/j.ajtas.20200904.18

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ACS Style

Jama Mohamed. Time Series Modeling and Forecasting of Somaliland Consumer Price Index: A Comparison of ARIMA and Regression with ARIMA Errors. Am. J. Theor. Appl. Stat. 2020, 9(4), 143-153. doi: 10.11648/j.ajtas.20200904.18

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AMA Style

Jama Mohamed. Time Series Modeling and Forecasting of Somaliland Consumer Price Index: A Comparison of ARIMA and Regression with ARIMA Errors. Am J Theor Appl Stat. 2020;9(4):143-153. doi: 10.11648/j.ajtas.20200904.18

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@article{10.11648/j.ajtas.20200904.18,
  author = {Jama Mohamed},
  title = {Time Series Modeling and Forecasting of Somaliland Consumer Price Index: A Comparison of ARIMA and Regression with ARIMA Errors},
  journal = {American Journal of Theoretical and Applied Statistics},
  volume = {9},
  number = {4},
  pages = {143-153},
  doi = {10.11648/j.ajtas.20200904.18},
  url = {https://doi.org/10.11648/j.ajtas.20200904.18},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajtas.20200904.18},
  abstract = {In recent years, the Consumer Price Index (CPI) prediction has attracted the attention of many researchers due to its excellent measurement of macroeconomic performance. It is an important index that is used to measure the rate of inflation or deflation of commodities. In this paper, Autoregressive Integrated Moving Average (ARIMA) and regression with ARIMA errors, where the covariate is the time, were compared to forecast Somaliland Consumer Price Index using monthly time series data from 2013 – 2020. The study used and applied both models to produce the necessary forecasts. Also, Akaike Information Criterion (AIC), Corrected Akaike Information Criterion (AICc), Bayesian Information Criterion (BIC) and other model accuracy measures were used to measure model’s predictive ability. By utilizing these methods, it is obtained that ARIMA (0, 1, 3) is the most suitable model for predicting CPI in Somaliland. Furthermore, the diagnostic tests show that the model presented is reliable and appropriate for forecasting Somaliland CPI data. The study results obviously indicate that CPI in Somaliland is more likely to proceed on an upward trend in the coming year. The study guides policymakers to use strict monetary and fiscal policy measures to address Somaliland’s inflation.},
 year = {2020}
}

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TY  - JOUR
T1  - Time Series Modeling and Forecasting of Somaliland Consumer Price Index: A Comparison of ARIMA and Regression with ARIMA Errors
AU  - Jama Mohamed
Y1  - 2020/07/13
PY  - 2020
N1  - https://doi.org/10.11648/j.ajtas.20200904.18
DO  - 10.11648/j.ajtas.20200904.18
T2  - American Journal of Theoretical and Applied Statistics
JF  - American Journal of Theoretical and Applied Statistics
JO  - American Journal of Theoretical and Applied Statistics
SP  - 143
EP  - 153
PB  - Science Publishing Group
SN  - 2326-9006
UR  - https://doi.org/10.11648/j.ajtas.20200904.18
AB  - In recent years, the Consumer Price Index (CPI) prediction has attracted the attention of many researchers due to its excellent measurement of macroeconomic performance. It is an important index that is used to measure the rate of inflation or deflation of commodities. In this paper, Autoregressive Integrated Moving Average (ARIMA) and regression with ARIMA errors, where the covariate is the time, were compared to forecast Somaliland Consumer Price Index using monthly time series data from 2013 – 2020. The study used and applied both models to produce the necessary forecasts. Also, Akaike Information Criterion (AIC), Corrected Akaike Information Criterion (AICc), Bayesian Information Criterion (BIC) and other model accuracy measures were used to measure model’s predictive ability. By utilizing these methods, it is obtained that ARIMA (0, 1, 3) is the most suitable model for predicting CPI in Somaliland. Furthermore, the diagnostic tests show that the model presented is reliable and appropriate for forecasting Somaliland CPI data. The study results obviously indicate that CPI in Somaliland is more likely to proceed on an upward trend in the coming year. The study guides policymakers to use strict monetary and fiscal policy measures to address Somaliland’s inflation.
VL  - 9
IS  - 4
ER  -

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Author Information

Jama Mohamed

Faculty of Mathematics and Statistics, College of Applied and Natural Science, University of Hargeisa, Hargeisa, Somaliland

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Plain Text BibTeX RIS

APA Style

Jama Mohamed. (2020). Time Series Modeling and Forecasting of Somaliland Consumer Price Index: A Comparison of ARIMA and Regression with ARIMA Errors. American Journal of Theoretical and Applied Statistics, 9(4), 143-153. https://doi.org/10.11648/j.ajtas.20200904.18

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ACS Style

Jama Mohamed. Time Series Modeling and Forecasting of Somaliland Consumer Price Index: A Comparison of ARIMA and Regression with ARIMA Errors. Am. J. Theor. Appl. Stat. 2020, 9(4), 143-153. doi: 10.11648/j.ajtas.20200904.18

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AMA Style

Jama Mohamed. Time Series Modeling and Forecasting of Somaliland Consumer Price Index: A Comparison of ARIMA and Regression with ARIMA Errors. Am J Theor Appl Stat. 2020;9(4):143-153. doi: 10.11648/j.ajtas.20200904.18

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@article{10.11648/j.ajtas.20200904.18,
  author = {Jama Mohamed},
  title = {Time Series Modeling and Forecasting of Somaliland Consumer Price Index: A Comparison of ARIMA and Regression with ARIMA Errors},
  journal = {American Journal of Theoretical and Applied Statistics},
  volume = {9},
  number = {4},
  pages = {143-153},
  doi = {10.11648/j.ajtas.20200904.18},
  url = {https://doi.org/10.11648/j.ajtas.20200904.18},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajtas.20200904.18},
  abstract = {In recent years, the Consumer Price Index (CPI) prediction has attracted the attention of many researchers due to its excellent measurement of macroeconomic performance. It is an important index that is used to measure the rate of inflation or deflation of commodities. In this paper, Autoregressive Integrated Moving Average (ARIMA) and regression with ARIMA errors, where the covariate is the time, were compared to forecast Somaliland Consumer Price Index using monthly time series data from 2013 – 2020. The study used and applied both models to produce the necessary forecasts. Also, Akaike Information Criterion (AIC), Corrected Akaike Information Criterion (AICc), Bayesian Information Criterion (BIC) and other model accuracy measures were used to measure model’s predictive ability. By utilizing these methods, it is obtained that ARIMA (0, 1, 3) is the most suitable model for predicting CPI in Somaliland. Furthermore, the diagnostic tests show that the model presented is reliable and appropriate for forecasting Somaliland CPI data. The study results obviously indicate that CPI in Somaliland is more likely to proceed on an upward trend in the coming year. The study guides policymakers to use strict monetary and fiscal policy measures to address Somaliland’s inflation.},
 year = {2020}
}

Copy | Download

TY  - JOUR
T1  - Time Series Modeling and Forecasting of Somaliland Consumer Price Index: A Comparison of ARIMA and Regression with ARIMA Errors
AU  - Jama Mohamed
Y1  - 2020/07/13
PY  - 2020
N1  - https://doi.org/10.11648/j.ajtas.20200904.18
DO  - 10.11648/j.ajtas.20200904.18
T2  - American Journal of Theoretical and Applied Statistics
JF  - American Journal of Theoretical and Applied Statistics
JO  - American Journal of Theoretical and Applied Statistics
SP  - 143
EP  - 153
PB  - Science Publishing Group
SN  - 2326-9006
UR  - https://doi.org/10.11648/j.ajtas.20200904.18
AB  - In recent years, the Consumer Price Index (CPI) prediction has attracted the attention of many researchers due to its excellent measurement of macroeconomic performance. It is an important index that is used to measure the rate of inflation or deflation of commodities. In this paper, Autoregressive Integrated Moving Average (ARIMA) and regression with ARIMA errors, where the covariate is the time, were compared to forecast Somaliland Consumer Price Index using monthly time series data from 2013 – 2020. The study used and applied both models to produce the necessary forecasts. Also, Akaike Information Criterion (AIC), Corrected Akaike Information Criterion (AICc), Bayesian Information Criterion (BIC) and other model accuracy measures were used to measure model’s predictive ability. By utilizing these methods, it is obtained that ARIMA (0, 1, 3) is the most suitable model for predicting CPI in Somaliland. Furthermore, the diagnostic tests show that the model presented is reliable and appropriate for forecasting Somaliland CPI data. The study results obviously indicate that CPI in Somaliland is more likely to proceed on an upward trend in the coming year. The study guides policymakers to use strict monetary and fiscal policy measures to address Somaliland’s inflation.
VL  - 9
IS  - 4
ER  -

Copy | Download