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Time Series Modeling and Forecasting Information: Evidence from Nigeria

Author

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  • Ikechukwu Kelikume
  • Adedoyin Salami

Abstract

A major concern of entrepreneurs and monetary authorities in Nigeria in the past decades was successful prediction general price level movements. The results allow successful planning on the part of monetary authorities and continued profit drive on the part of entrepreneurs and investors. This study uses a univariate model in the form of Autoregressive Integrated Moving Average model developed by Box and Jenkins and multivariate time series model in the form of Vector Autoregressive model to forecast inflation for Nigeria. This paper use changes in monthly consumer price index obtained from the National Bureau of Statistics and the Central bank of Nigeria over the period 2003 to 2012 to predict movements in the general price level. Based on different diagnostic and evaluation criteria, the best forecasting model for predicting inflation in Nigeria is identified. The results will enable policy makers and businesses to track the performance and stability of key macroeconomic indicators using the forecasted inflation.

Suggested Citation

  • Ikechukwu Kelikume & Adedoyin Salami, 2014. "Time Series Modeling and Forecasting Information: Evidence from Nigeria," The International Journal of Business and Finance Research, The Institute for Business and Finance Research, vol. 8(2), pages 41-51.
    • Handle: RePEc:ibf:ijbfre:v:8:y:2014:i:2:p:41-51
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    References listed on IDEAS

    as
    1. Mr. Toshitaka Sekine, 2001. "Modeling and Forecasting Inflation in Japan," IMF Working Papers 2001/082, International Monetary Fund.
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    Cited by:

    1. Emmanuel O. Akande & Elijah O. Akanni & Oyedamola F. Taiwo & Jeremiah D. Joshua & Abel Anthony, 2023. "Predicting inflation component drivers in Nigeria: a stacked ensemble approach," SN Business & Economics, Springer, vol. 3(1), pages 1-32, January.
    2. Jackson, Emerson Abraham & Tamuke, Edmund, 2019. "Predicting disaggregated tourist arrivals in Sierra Leone using ARIMA model," MPRA Paper 96845, University Library of Munich, Germany, revised 23 Dec 2019.
    3. EMERSON Abraham Jackson, 2018. "Comparison Between Static And Dynamic Forecast In Autoregressive Integrated Moving Average For Seasonally Adjusted Headline Consumer Price Index," Revista Economica, Lucian Blaga University of Sibiu, Faculty of Economic Sciences, vol. 70(1), pages 53-65, August.
    4. Igor Živko & Mile Bošnjak, 2017. "Time Series Modeling of Inflation and its Volatility in Croatia," Notitia - journal for economic, business and social issues, Notitia Ltd., vol. 1(3), pages 1-10, December.
    5. Musa Nakorji & Umaru Aminu, 2022. "Forecasting inflation using machine learning techniques," The Review of Finance and Banking, Academia de Studii Economice din Bucuresti, Romania / Facultatea de Finante, Asigurari, Banci si Burse de Valori / Catedra de Finante, vol. 14(1), pages 45-55, June.
    6. Tumala, Mohammed M & Olubusoye, Olusanya E & Yaaba, Baba N & Yaya, OlaOluwa S & Akanbi, Olawale B, 2017. "Forecasting Nigerian Inflation using Model Averaging methods: Modelling Frameworks to Central Banks," MPRA Paper 88754, University Library of Munich, Germany, revised Feb 2018.

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    More about this item

    Keywords

    Modeling Inflation; Forecasting; ARIMA; VAR;
    All these keywords.

    JEL classification:

    • E3 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles
    • E17 - Macroeconomics and Monetary Economics - - General Aggregative Models - - - Forecasting and Simulation: Models and Applications
    • E31 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Price Level; Inflation; Deflation

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