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Modelling and Forecasting the Impact of the COVID-19 Pandemic on South Africa’s New Car Sales |
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PP: 1127-1140 |
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doi:10.18576/jsap/130401
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Author(s) |
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D. Chikobvu,
T. Makoni,
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Abstract |
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| The goal of this study is to use time series modeling to analyse and forecast, and to show how the COVID-19 pandemic impacted South Africa (SA)s new car sales. An empirical approach employing the Box-Jenkins methodology to time series analysis is employed in this paper. A SARIMA (0,1,1)(0,0,2)12 model, among other models, is found to give the best fit as measured by the root mean square error (RMSE), Akaike Information Criteria (AIC) and the Bayesian Information criterion (BIC). The analysis reveals a significant, average 84.5% decline in monthly new car sales following the pandemics official declaration by the SA government. Future projections show that the prevailing rate of increase in new car sales has resulted in a recovery, and new car sales have reached pre-COVID-19 levels and even surpassed expected projected levels. The proposed methodology and findings are useful to the SA automotive industry to help develop motor vehicle sales plans and to help provide information to mitigate against unforeseen cash flow issues and better manage needs for car manufacturing, personnel, and financing of new car sales. The study uses a well-established statistical methodology to quantify the COVID-19 pandemic’s impact on South Africa’s car manufacturing industry. This study is one of the few empirical ones that look at how COVID-19 has affected the sales of new car sales in a developing nation, like SA. A post- mortem to determine/quantify how the COVID-19 pandemic affected the car manufacturing sector’s business is essential to the countrys future industrial and economic landscape.
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