A Multiplicative Autoregressive Integrated Moving Average Model for Kenya’s Inflation (2000:1 – 2013:12)

Nyabwanga Robert Nyamao

doi:doi:10.11648/j.sjams.20140206.14

| Peer-Reviewed

A Multiplicative Autoregressive Integrated Moving Average Model for Kenya’s Inflation (2000:1 – 2013:12)

Nyabwanga Robert Nyamao

Published in Science Journal of Applied Mathematics and Statistics (Volume 2, Issue 6)

Received: 14 December 2014 Accepted: 23 December 2014 Published: 31 December 2014

Views: Downloads:

Download PDF

Share This Article

Twitter
Linked In
Facebook

Abstract

Using monthly inflation data from January 2000 to December 2013, we find that SARIMA (1,1,1)(1,0,1)12 can represent the data behavior of inflation rate in Kenya well. Based on the selected model, we forecast seven (12) months inflation rates of Kenya outside the sample period (i.e. from January 2014 to December 2014). The observed inflation rates from January to November which were published by Kenya Bureau of Statistics fall within the 95% confidence interval obtained from the designed model. However, the confidence intervals were wider an indication of high volatility of Kenya’s inflation rates.

Published in	Science Journal of Applied Mathematics and Statistics (Volume 2, Issue 6)
DOI	10.11648/j.sjams.20140206.14
Page(s)	122-129
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), 2024. Published by Science Publishing Group

Keywords

Inflation, Forecasting, Box-Jenkins Approach, Multiplicative ARIMA Model, Unit Root Test, ADF Test, Ljung-Box Test

References

[1]	Akaike, H. (1974). A New Look at the Statistical Model Identification. IEEE Transactions on Automatic Control 19 (6): 716-723.
[2]	Akofio-Sowah (2009) Akofio-Sowah, N, (2009).Is there a link between Exchange Rate pass-through and the monetary regime: Evidence from Sub-Saharan Africa and Latin America. International Atlantic Economic Society. Available: http://www.springerlink.com
[3]	Box, G. E. P and Jenkins, G.M., (1976). “Time series analysis: Forecasting and control,” Holden-Day, San Francisco.
[4]	Buckman A. and Mintah A. (2013). An Autoregressive Integrated Moving Average (ARIMA) Model For Ghana’s Inflation (1985 – 2011).Mathematical Theory and Modeling Vol.3, No.3, 2013. www.iiste.org
[5]	Dickey, D.A. & Fuller, W.A. (1981). Likelihood Ratio Statistics for Autoregressive Time Series with a Unit Root. Econometrica, 49(4), 1057-1072.
[6]	Fisher, S., Sahay, R.&Vegh, C. (2002). “Modern Hyper-and High Inflation” .Journal of Economic Literature,. 40, 837-80
[7]	Fritzer, F., Gabriel, M. and Johann, S. (2002). "Forecasting Austrian HICP and its Components using VAR and ARIMA Models," Working Papers 73, Oesterreichische National bank (Austrian Central Bank).
[8]	Kwiatkowski, D., Phillips, P. C. B., Schmidt, P. & Shin, Y. (1992): Testing the Null Hypothesis of Stationarity against the Alternative of a Unit Root. Journal of Econometrics 54, 159–178.
[9]	Mishkin, F. (2008).Exchange Rate Pass-through and Monetary Policy
[10]	Otu, A., Osuji, G., Opara, J., Mbachu, H. and Iheagwara A. (2014). Application of Sarima Models in Modelling and Forecasting Nigeria’s Inflation Rates. American Journal of Applied Mathematics and Statistics, 2014, Vol. 2, No. 1, 16-28
[11]	Rotich, H., Kathanje, M &Maana, I. (2007). A monetary policy reaction function for Kenya. Paper Presented During the 13th Annual African Econometric SocietyConference in Pretoria, South Africa from 9th to 11th July 2008.
[12]	Stokes, G. (2009). FA news on line South Africa’s premier financial and advisory news and information portal.
[13]	Webster, D. (2000). Webster's New Universal Unabridged Dictionary. Barnes & Noble Books, New York

Cite This Article

Plain Text BibTeX RIS

APA Style

Nyabwanga Robert Nyamao. (2014). A Multiplicative Autoregressive Integrated Moving Average Model for Kenya’s Inflation (2000:1 – 2013:12). Science Journal of Applied Mathematics and Statistics, 2(6), 122-129. https://doi.org/10.11648/j.sjams.20140206.14

Copy | Download

ACS Style

Nyabwanga Robert Nyamao. A Multiplicative Autoregressive Integrated Moving Average Model for Kenya’s Inflation (2000:1 – 2013:12). Sci. J. Appl. Math. Stat. 2014, 2(6), 122-129. doi: 10.11648/j.sjams.20140206.14

Copy | Download

AMA Style

Nyabwanga Robert Nyamao. A Multiplicative Autoregressive Integrated Moving Average Model for Kenya’s Inflation (2000:1 – 2013:12). Sci J Appl Math Stat. 2014;2(6):122-129. doi: 10.11648/j.sjams.20140206.14

Copy | Download

@article{10.11648/j.sjams.20140206.14,
  author = {Nyabwanga Robert Nyamao},
  title = {A Multiplicative Autoregressive Integrated Moving Average Model for Kenya’s Inflation (2000:1 – 2013:12)},
  journal = {Science Journal of Applied Mathematics and Statistics},
  volume = {2},
  number = {6},
  pages = {122-129},
  doi = {10.11648/j.sjams.20140206.14},
  url = {https://doi.org/10.11648/j.sjams.20140206.14},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.sjams.20140206.14},
  abstract = {Using monthly inflation data from January 2000 to December 2013, we find that SARIMA (1,1,1)(1,0,1)12 can represent the data behavior of inflation rate in Kenya well. Based on the selected model, we forecast seven (12) months inflation rates of Kenya outside the sample period (i.e. from January 2014 to December 2014). The observed inflation rates from January to November which were published by Kenya Bureau of Statistics fall within the 95% confidence interval obtained from the designed model. However, the confidence intervals were wider an indication of high volatility of Kenya’s inflation rates.},
 year = {2014}
}

Copy | Download

TY  - JOUR
T1  - A Multiplicative Autoregressive Integrated Moving Average Model for Kenya’s Inflation (2000:1 – 2013:12)
AU  - Nyabwanga Robert Nyamao
Y1  - 2014/12/31
PY  - 2014
N1  - https://doi.org/10.11648/j.sjams.20140206.14
DO  - 10.11648/j.sjams.20140206.14
T2  - Science Journal of Applied Mathematics and Statistics
JF  - Science Journal of Applied Mathematics and Statistics
JO  - Science Journal of Applied Mathematics and Statistics
SP  - 122
EP  - 129
PB  - Science Publishing Group
SN  - 2376-9513
UR  - https://doi.org/10.11648/j.sjams.20140206.14
AB  - Using monthly inflation data from January 2000 to December 2013, we find that SARIMA (1,1,1)(1,0,1)12 can represent the data behavior of inflation rate in Kenya well. Based on the selected model, we forecast seven (12) months inflation rates of Kenya outside the sample period (i.e. from January 2014 to December 2014). The observed inflation rates from January to November which were published by Kenya Bureau of Statistics fall within the 95% confidence interval obtained from the designed model. However, the confidence intervals were wider an indication of high volatility of Kenya’s inflation rates.
VL  - 2
IS  - 6
ER  -

Copy | Download

Author Information

Nyabwanga Robert Nyamao

Kisii University, School of Pure and Applied Sciences, Department of Statistics and Actuarial Science, Kisii, Kenya

Download PDF

Sections

Plain Text BibTeX RIS

APA Style

Nyabwanga Robert Nyamao. (2014). A Multiplicative Autoregressive Integrated Moving Average Model for Kenya’s Inflation (2000:1 – 2013:12). Science Journal of Applied Mathematics and Statistics, 2(6), 122-129. https://doi.org/10.11648/j.sjams.20140206.14

Copy | Download

ACS Style

Nyabwanga Robert Nyamao. A Multiplicative Autoregressive Integrated Moving Average Model for Kenya’s Inflation (2000:1 – 2013:12). Sci. J. Appl. Math. Stat. 2014, 2(6), 122-129. doi: 10.11648/j.sjams.20140206.14

Copy | Download

AMA Style

Nyabwanga Robert Nyamao. A Multiplicative Autoregressive Integrated Moving Average Model for Kenya’s Inflation (2000:1 – 2013:12). Sci J Appl Math Stat. 2014;2(6):122-129. doi: 10.11648/j.sjams.20140206.14

Copy | Download

@article{10.11648/j.sjams.20140206.14,
  author = {Nyabwanga Robert Nyamao},
  title = {A Multiplicative Autoregressive Integrated Moving Average Model for Kenya’s Inflation (2000:1 – 2013:12)},
  journal = {Science Journal of Applied Mathematics and Statistics},
  volume = {2},
  number = {6},
  pages = {122-129},
  doi = {10.11648/j.sjams.20140206.14},
  url = {https://doi.org/10.11648/j.sjams.20140206.14},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.sjams.20140206.14},
  abstract = {Using monthly inflation data from January 2000 to December 2013, we find that SARIMA (1,1,1)(1,0,1)12 can represent the data behavior of inflation rate in Kenya well. Based on the selected model, we forecast seven (12) months inflation rates of Kenya outside the sample period (i.e. from January 2014 to December 2014). The observed inflation rates from January to November which were published by Kenya Bureau of Statistics fall within the 95% confidence interval obtained from the designed model. However, the confidence intervals were wider an indication of high volatility of Kenya’s inflation rates.},
 year = {2014}
}

Copy | Download

TY  - JOUR
T1  - A Multiplicative Autoregressive Integrated Moving Average Model for Kenya’s Inflation (2000:1 – 2013:12)
AU  - Nyabwanga Robert Nyamao
Y1  - 2014/12/31
PY  - 2014
N1  - https://doi.org/10.11648/j.sjams.20140206.14
DO  - 10.11648/j.sjams.20140206.14
T2  - Science Journal of Applied Mathematics and Statistics
JF  - Science Journal of Applied Mathematics and Statistics
JO  - Science Journal of Applied Mathematics and Statistics
SP  - 122
EP  - 129
PB  - Science Publishing Group
SN  - 2376-9513
UR  - https://doi.org/10.11648/j.sjams.20140206.14
AB  - Using monthly inflation data from January 2000 to December 2013, we find that SARIMA (1,1,1)(1,0,1)12 can represent the data behavior of inflation rate in Kenya well. Based on the selected model, we forecast seven (12) months inflation rates of Kenya outside the sample period (i.e. from January 2014 to December 2014). The observed inflation rates from January to November which were published by Kenya Bureau of Statistics fall within the 95% confidence interval obtained from the designed model. However, the confidence intervals were wider an indication of high volatility of Kenya’s inflation rates.
VL  - 2
IS  - 6
ER  -

Copy | Download