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Forecasting Foreign Direct Investment to Zambia: A Time Series Analysis

Open Journal of Statistics 2017年第1期7卷 122-131页

作者： Stanley Jere bornwell kasense Obvious Chilyabanyama Department of Mathematics and Statistics Mulungushi University Kabwe Zambia

Three methods are considered in this paper: Simple exponential smoothing (SES), Holt-Winters exponential smoothing (HWES) and autoregressive integrated moving average (ARIMA). The best fit model was then used to forecast Zambia’s annual net foreign direct investment (FDI) inflows from 1970 to 2014. Foreign direct investment is foreign capital investment to Zambia. Throughout the paper the methods are illustrated using Zambia’s annual Net FDI inflows. A comparison of the three methods shows that the ARIMA (1, 1, 5) is the best fit model because it has the minimum error. Forecasting results give a gradual increase in annual net FDI inflows of about 44.36% by 2024. Forecasting results plays a vital role to policy makers. Decision making, coming up with good policies and suitable strategic plans, depends on accurate forecasts. Zambian FDI policy makers can use the results obtained in this study and create suitable strategic plans to promote FDI.

关键词： Foreign Direct Investment Simple Exponential Smoothing Holt-Winters Exponential Smoothing Autoregressive Integrated Moving Average Forecasting

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Univariate Time-Series Analysis of Second-Hand Car Importation in Zambia

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Open Journal of Statistics 2017年第4期7卷 718-730页

作者： Stanley Jere bornwell kasense Bwalya Bupe Bwalya Department of Mathematics and Statistics Mulungushi University Kabwe Zambia

Zambia largely depends on the international second-hand car (SHC) market for their motor vehicle supply. The importation of Second hand Cars in Zambia presents a time series problem. The data used in this paper is monthly data on SHC importation from 1st January, 2014 to 31st December, 2016. Data was analyzed using Exponential Smoothing (ES) and Autoregressive Integrated Moving Average (ARIMA) models. The results showed that ARIMA (2, 1, 2) was the best fit for the SHC importation since its errors were smaller than those of the SES, DES and TES. The four error measures used were Root-mean-square error (RMSE), Mean absolute error (MAE), Mean percentage error (MPE) and Mean absolute percentage error (MAPE). The forecasts were also produced using the ARIMA (2, 1, 2) model for the next 18 months from January 2017. Although there is percentage increase of 90.6% from November 2015 to December 2016, the SHC importation generally is on the decrease in Zambia with percentage change of 59.5% from January 2014 to December 2016. The forecasts also show a gradual percentage decrease of 1.12% by June 2018. These results are more useful to policy and decision makers of Government departments such as Zambia Revenue Authority (ZRA) and Road Development Agency (RDA) in a bid to plan and execute their duties effectively.

关键词： Zambia Importation Second Hand Car Exponential Smoothing Models ARIMA Models Forecasting

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Forecasting Annual International Tourist Arrivals in Zambia Using Holt-Winters Exponential Smoothing

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Open Journal of Statistics 2019年第2期9卷 258-267页

作者： Stanley Jere Alick Banda bornwell kasense Ian Siluyele Edwin Moyo Department of Mathematics and Statistics Mulungushi University Kabwe Zambia Zambia Information and Communications Technology Authority Lusaka Zambia Department of Mathematics and Statistics Northrise University Ndola Zambia

Tourism is one of the major contributors to foreign exchange earnings to Zambia and world economy. Annual International tourist arrivals in Zambia from 1995 to 2014 are considered in this paper. In this study we evaluated the model performance of Auto-Regressive Integrated Moving Average (ARIMA) and Holt Winters exponential smoothing (HWES). The error indicators: Mean percentage error (MPE), Mean absolute error (MAE), Mean absolute scaled error (MASE), Root-mean-square error (RMSE) and Mean absolute percentage error (MAPE) showed that HWES is an appropriate model with reasonable forecast accuracy. The HWES (α = 1, β = 0.1246865) showed smallest error than those of the ARIMA (0, 1, 2) models. Hence, the HWES (α = 1, β = 0.1246865) can be used to model annual international tourist arrivals in Zambia. Further, forecasting results give a gradual increase in annual international tourist arrivals of about 42% by 2024. Accurate forecasts are key to new investors and Policymakers. The Zambian government should use such forecasts in formulating policies and making strategies that will promote the tourism industry.

关键词： Zambia International Tourist Arrivals Holt-Winters Model ARIMA Forecasting

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