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2019 | 128 | 2 | 348-362
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Time Series ARIMA Model for Predicting Nigeria Net Foreign Direct Investment (FDI)

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This paper presents an empirical study of modelling and forecasting time series data of Nigeria net foreign direct investment (FDI). The Box-Jenkins ARIMA methodology was used for forecasting the yearly data collected from 1972 to 2018. Result of the analysis revealed that the series became stationary at first difference. The diagnostic checking has shown that ARIMA (1, 1, 2) is appropriate or optimal model based on the Akaike’s Information Criterion (AIC), the Bayesian Information Criterion (BIC) and Hannan Quinn criterion (HQ). A twenty (20) year forecast was made from 2019-2039, the result of the forecast showed that the net FDI in Nigeria will continue to grow in the period forecasted. These forecasts will help policy makers in Nigeria to sustain their efforts to expand the tax base, reduce red tape, and strengthen the regulatory framework to investment and also investors friendly policies in order to attract the much needed FDI.
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  • Department of Mathematical Sciences, Abubakar Tafawa Balewa University, Bauchi, Nigeria
  • Department of Mathematical Sciences, Bauchi State University Gadau, Nigeria
  • Department of Mathematical Sciences, Bauchi State University Gadau, Nigeria
  • Department of Mathematical Sciences, Abubakar Tafawa Balewa University, Bauchi, Nigeria
  • [1] Hanzacebi .C (2008). Improving Artificial Neural Network Performance in Seasonal Time Series Forecasting. Information Science, 178, 4550-4559
  • [2] Zhang .G.P (2007). Neural Network Ensemble Method with Jilted Training Data for Time Series Forecasting. Information Science, 177, 5329-5346
  • [3] Zhang. G.P (2003). Time Series Forecasting Using Hybrid ARIMA and Neural Network Model. Neural Computing, 50, 159-175
  • [4] Tong H. Threshold Model in Non-Linear Time Series Analysis, Spring-Verlag, New York, 1983
  • [5] Box G.E.P and Jenkins G. Time Series Analysis, Forecasting and Control. Holden-Day, San Francisco, 1970.
  • [6] Hipel K.W and McLeod A.I. Time Series Modelling of Water Resources and Environmental Systems. Amsterdam, Elsevier, 1994.
  • [7] Adewumi. S (2006). The Impact of FDI on Growth in Developing Countries: An African Experience. Jonkoping International Business School, Jonkoping University, Sweden.
  • [8] Dhingra, V.S., Bulsara, H.P & Ghandhi, S (2015). Forecasting Institutional Flow Towards India Using ARIMA Modeling, Management 75, 13-26
  • [9] Prasanna, P.W.L (2015). Modelling and Forecasting Foreign Investment (FDI) into SAARC for the Period of 2013-2037 with ARIMA. International Journal of Business and Social Science 6(2), 264-272
  • [10] Jere, S., Kasense, B. and Chilyabanyama, O. (2017) Forecasting Foreign Direct Investment to Zambia: A Time Series Analysis. Open Journal of Statistics, 7, 122-131.
  • [11] Thabani Nyoni (2018). Box Jenkins Approach to Predicting Net FDI Inflows in Zimbabwe. MPRA Paper NO 87737
  • [12] Moffat, I. U. & Akpan, E. A. (2014). Time Series Forecasting: A Tool for Out-Sample Model and evaluation. American Journal of Scientific and Industrial Research 5(6), 185-194
  • [13] Alvaro Santos Pereira, João Tovar Jalles, Martin A. Andresen. Structural change and foreign direct investment: globalization and regional economic integration. Portuguese Economic Journal April 2012, Volume 11, Issue 1, pp 35-82.
  • [14] Ng S, Perron P (1995) Unit root tests in ARMA models with data-dependent methods for the selection of the truncation lag. J Am Stat Assoc 90: 268-281
  • [15] Coakley J, Fuertes A-M, Smith RP (2006). Unobserved heterogeneity in panel time series models. Comput Stat Data Anal 50(9): 2361-2380
  • [16] Carr DL, Markusen JR, Maskus KE (2001). Estimating the knowledge-capital model of the multinational enterprise. Am Econ Rev 91: 693-708
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