Modeling rates of inflation in Nigeria: an application of ARMA, ARIMA and GARCH models

NYONI, THABANI and NATHANIEL, SOLOMON PRINCE (2018): Modeling rates of inflation in Nigeria: an application of ARMA, ARIMA and GARCH models.

Abstract

Based on time series data on inflation rates in Nigeria from 1960 to 2016, we model and forecast inflation using ARMA, ARIMA and GARCH models. Our diagnostic tests such as the ADF tests indicate that NINF time series data is essentially I (1), although it is generally I (0) at 10% level of significance. Based on the minimum Theil’s U forecast evaluation statistic, the study presents the ARMA (1, 0, 2) model, the ARIMA (1, 1, 1) model and the AR (3) – GARCH (1, 1) model; of which the ARMA (1, 0, 2) model is clearly the best optimal model. Our diagnostic tests also indicate that the presented models are stable and hence reliable. The results of the study reveal that inflation in Nigeria is likely to rise to about 17% per annum by end of 2021 and is likely to exceed that level by 2027. In order to address the problem of inflation in Nigeria, three main policy prescriptions have been suggested and are envisioned to assist policy makers in stabilizing the Nigerian economy.

• Item Type: MPRA Paper
• Language: English
• Keywords: ARIMA; ARMA; Forecasting; GARCH; Inflation; Nigeria
• Subjects: C - Mathematical and Quantitative Methods > C5 - Econometric Modeling > C53 - Forecasting and Prediction Methods ; Simulation Methods; E - Macroeconomics and Monetary Economics > E3 - Prices, Business Fluctuations, and Cycles > E31 - Price Level ; Inflation ; Deflation; E - Macroeconomics and Monetary Economics > E3 - Prices, Business Fluctuations, and Cycles > E37 - Forecasting and Simulation: Models and Applications; E - Macroeconomics and Monetary Economics > E4 - Money and Interest Rates > E47 - Forecasting and Simulation: Models and Applications

References

[1] Adebiyi, M. A., Adenuga, A. O., Abeng, M. O., Omanukwe, P. N., & Ononugbo, M. C. (2010). Inflation forecasting models for Nigeria, Central Bank of Nigeria Occasional Paper No. 36, Abuja, Research and Statistics Department.

[2] Aghevei, B.B. & Khan, M.S. (1977). Inflationary Finance and Economic Growth, Journal of Political Economy, 85 (4)

[3] Alnaa, S. E. & Ahiakpor, F (2011). ARIMA (Autoregressive Integrated Moving Average) approach to predicting inflation in Ghana, Journal of Economics and International Finance, 3 (5): 328 – 336.

[4] Altug, S. & C. Cakmakli (2016). Forecasting Inflation Using Survey Expectations and Target Inflation, Evidence for Brazil and Turkey, International Journal of Forecasting 32, 138-153.

[5] Aron, J. & J. Muellbauer (2012). Improving Forecasting in an Emerging Economy, South Africa: Changing Trends, Long Run Restrictions and Disaggregation, International Journal of Forecasting 28, 456-476.

[6] Balcilar, M., R. Gupta, & K. Kotze (2015). Forecasting Macroeconomic Data for an Emerging Market with a Nonlinear DSGE Model, Economic Modelling 44, 215-228.

[7] Banerjee, S (2017). Empirical Regularities of Inflation Volatility: Evidence from Advanced and Developing Countries, South Asian Journal of Macroeconomics and Public Finance, 6 (1): 133 – 156.

[8] Bernanke, B. S (2005). Inflation in Latin America – A New Era? – Remarks at the Stanford Institute for Economic Policy Research – Economic Summit, February 11. http://www.federalreserve.gov/boarddocs/speeches/2005/20050211/default.htm

[9] Box, D. E. & Jenkins, G. M (1970). Time Series Analysis, Forecasting and Control, Holden Day.

[10] Box, D. E. & Jenkins, G. M (1974). Time Series Analysis, Forecasting and Control, Revised Edition, Holden Day.

[11] Chen, Y.C., S. J. Turnovsky, & E. Zivot (2014). Forecasting Inflation Using Commodity Price Aggregates, Journal of Econometrics 183, 117-134.

[12] Doguwa, S. I. and Alade, S. O. (2013): Short-term Inflation Forecasting Models for Nigeria. CBN Journal of Applied Statistics, 4 (2), 1-29.

[13] Duncan, R., & Martínez-García, E. (2018). New Perspectives on Forecasting Inflation in Emerging Market Economies: An Empirical Assessment, Federal Reserve Bank of Dallas, Working Paper No. 338, Globalization and Monetary Policy Institute.

[14] Etuk, E. H., Uchendu, B. & Victoredema, U. A. (2012). Forecasting Nigeria Inflation Rates by a Seasonal ARIMA Model, Canadian Journal of Pure and Applied Sciences, 6 (3), 2179-2185.

[15] Friedman, M (1956). The quantity theory of money: A restatement, Studies in the Quantity Theory of Money, University of Chicago Press, Chicago.

[16] Friedman, M (1960). A Program for Monetary Stability, The Millar Lectures, No. 3, Fordham University Press, New York.

[17] Friedman, M (1971). The Theoretical Framework of Monetary Analysis, National Bureau of Economic Research, Occasional paper 112

[18] Fwaga S. O., Orwa, G & Athiany, H (2017). Modelling Rates of Inflation in Kenya: An Application of Garch and Egarch models, Mathematical Theory and Modelling, 7 (5): 75 – 83.

[19] Hadrat, Y. M, Isaac E, N., & Eric E, S. (2015) Inflation Forecasting in Ghana-Artificial Neural Network Model Approach, Int. J. Econ. Manag. Sci 4: 274.

[20] Iftikhar, N. & Iftikhar-ul-amin (2013). Forecasting the Inflation in Pakistan – The Box-Jenkins Approach, World Applied Sciences Journal 28 (11): 1502-1505.

[21] Inam, U. S. (2017). Forecasting Inflation in Nigeria: A vector Autoregression Approach, International Journal of Economics, Commerce and Management, 5(1), 92-104.

[22] Ingabire. J & Mung’atu. J. K. (2016). Measuring the Performance of Autoregressive Integrated Moving Average and Vector Autoregressive Models in Forecasting Inflation Rate in Rwanda, International Journal of Mathematics and Physical Sciences Research, 4(1) :15-25

[23] Islam, N. (2018). Forecasting Bangladesh’s Inflation through Econometric Models, American Journal of Economics and Business Administration. https://www.researchgate.net/publication/321391829

[24] Jere, S., & Siyanga, M. (2016). Forecasting inflation rate of Zambia using Holt’s exponential smoothing. Open journal of Statistics, 6(02), 363.

[25] John, E. E., & Patrick, U. U. (2016). Short-term forecasting of Nigeria inflation rates using seasonal ARIMA Model. Science Journal of Applied Mathematics and Statistics, 4(3), 101-107.

[26] Kabukcuoglu, A. and E. Martnez-Garca (2018). Inflation as a Global Phenomenon: Some Implications for Inflation Modelling and Forecasting, Journal of Economic Dynamics and Control 87(2), 46-73.

[27] Kavila, W & Roux, P. L (2017). The reaction of inflation to macroeconomic shocks: The case of Zimbabwe (2009 – 2012), Economic Research South Africa (ERSA), ERSA Working Paper No. 707.

[28] Kelikume, I., & Salami, A. (2014). Time Series Modelling and Forecasting Inflation: Evidence from Nigeria. The International Journal of Business and Finance Research,8(2) : 91-98.

[29] Khan, M. S & Schimmelpfennig, A (2006). Inflation in Pakistan: Money or Wheat? IMF, Working Paper No. WP/06/60.

[30] Lidiema, C. (2017). Modelling and Forecasting Inflation Rate in Kenya Using SARIMA and Holt-Winters Triple Exponential Smoothing. American Journal of Theoretical and Applied Statistics, 6(3): 161-169.

[31] Mandalinci, Z. (2017). Forecasting Inflation in Emerging Markets: An Evaluation of Alternative Models, International Journal of Forecasting 33(4), 1082-1104.

[32] Marbuah, G (2010). The inflation – growth nexus: testing for optimal inflation for Ghana, Journal of Monetary and Economic Integration, 11 (2): 71 – 89.

[33] Medel, C. A., M. Pedersen, & P. M. Pincheira (2016). The Elusive Predictive Ability of Global Inflation, International Finance 19(2), 120-146.

[34] Molebatsi, K., & Raboloko, M. (2016). Time Series Modelling of Inflation in Botswana Using Monthly Consumer Price Indices, International Journal of Economics and Finance, 8(3), 15.

[35] Mustapha, A. M., & Kubalu, A. I.(2016) Application Of Box-Inflation Dynamics. Ilimi Journal of Arts and Social Sciences, 2(1), May/June, 2016.

[36] Ngailo, E., Luvanda, E., & Massawe, E. S. (2014). Time Series Modelling with Application to Tanzania Inflation Data, Journal of Data Analysis and Information Processing, 2(02), 49.

[37] Nyoni, T & Bonga, W. G (2017h). Population Growth in Zimbabwe: A Threat to Economic Development? DRJ – Journal of Economics and Finance, 2 (6): 29 – 39. https://www.researchgate.net/publication/318211505

[38] Nyoni, T & Bonga, W. G (2018a). What Determines Economic Growth In Nigeria? DRJ – Journal of Business and Management, 1 (1): 37 – 47. https://www.researchgate.net/publication/323068826

[39] Nyoni, T (2018i). Box – Jenkins ARIMA Approach to Predicting net FDI inflows in Zimbabwe, Munich University Library – Munich Personal RePEc Archive (MPRA), Paper No. 87737. https://www.researchgate.net/publications/326270598

[40] Nyoni, T (2018k). Modelling and Forecasting Inflation in Zimbabwe: a Generalized Autoregressive Conditionally Heteroskedastic (GARCH) approach, Munich University Library – Munich Personal RePEc Archive (MPRA), Paper No. 88132. https://www.researchgate.net/publication/326697913

[41] Nyoni, T (2018l). Modelling and Forecasting Naira / USD Exchange Rate In Nigeria: a Box – Jenkins ARIMA approach, Munich University Library – Munich Personal RePEc Archive (MPRA), Paper No. 88622. https://www.researchgate.net/publication/327262575

[42] Ogunc, F., K. Akdogan, S. Baser, M. G. Chadwick, D. Ertug, T. Hlag, S. Ksem, M. U. zmen, and N. Tekatli (2013). Short-term Inflation Forecasting Models for Turkey and a Forecast Combination Analysis, Economic Modelling 33, 312-325.

[43] Okafor, C., & Shaibu, I. (2013). Application of ARIMA models to Nigerian inflation dynamics, Research Journal of Finance and Accounting, 4(3), 138-150.

[44] Olajide, J. T., Ayansola, O. A., Odusina, M. T., & Oyenuga, I. F. (2012). Forecasting the Inflation Rate in Nigeria: Box Jenkins Approach, IOSR Journal of Mathematics (IOSR-JM).

[45] Otu, A. O., Osuji, G. A., Opara, J., Mbachu, H. I., & Iheagwara, A. I. (2014). Application of Sarima models in modelling and forecasting Nigeria’s inflation rates, American Journal of Applied Mathematics and Statistics, 2(1), 16-28.

[46] Pincheira, P. M. & C. A. Medel (2015). Forecasting Inflation with a Simple and Accurate Benchmark: The Case of the U.S. and a Set of Inflation Targeting Countries, Czech Journal of Economics and Finance 65(1).

[47] Popoola, O. P., Ayanrinde, A. W., Rafiu, A. A., & Odusina, M. T. (2017). Time Series Analysis to Model and Forecast Inflation Rate in Nigeria, Annals. Computer Science Series, 15(1).

[48] Samuelson, P. A (1971). Reflections on the Merits and Demerits of Monetarism – in Issues in Fiscal and Monetary Policy: The Eclectic Economist Views the Controversy, Ed, J. J. Diamond, De Paul University.

[49] Sergii, P (2009). Inflation and economic growth: the non – linear relationship – evidence from CIS countries, Kyiv School of Economics, Ukraine.

[50] Thirlwell, (1974). Inflation, savings and growth in developing economics, London: the macorellan press ltd.

[51] Udom. P & Phumchusri. N (2014). A comparison study between time series model and ARIMA model for sales forecasting of distributor in plastic industry, IOSR Journal of Engineering (IOSRJEN),4(2): 2278-8719.

[52] Uko, A. K., & Nkoro, E. (2012). Inflation forecasts with ARIMA, vector autoregressive and error correction models in Nigeria. European Journal of Economics, Finance & Administrative Science, 50, 71-87.

[53] Uwilingiyimana, C., Munga’tu, J., & Harerimana, J. D. D. (2015). Forecasting Inflation in Kenya Using Arima-Garch Models, International Journal of Management and Commerce Innovations, 3(2):15-27.

[54] Yusif, M. H., Eshun Nunoo, I. K., & Effah Sarkodie, E. (2015). Inflation Forecasting in Ghana-Artificial Neural Network Model Approach.