The current study investigates the impact of the 2008 US financial crises on the real exchange rate in South Africa. The data used in this empirical analysis is for the period from January 2000 to June 2017. The Seasonal autoregressive integrated moving average (SARIMA) intervention charter was used to carry out the analysis. Results revealed that the financial crises period in South Africa occurred in March 2008 and significantly affected the exchange rate. Hence, the impact pattern was abrupt. Using the SARIMA model as a benchmark, four error metrics; to be precise mean absolute error (MAE), mean absolute percentage error (MAPE), mean error (ME) and Mean percentage error (MPE) was used to assess the performance of the intervention model and SARIMA model. The results of the SARIMA intervention model produced better forecasts as compared to that one of SARIMA model.
The primary motivation behind this study was to explore the consequential effects of budget deficit on South Africa`s economic growth. Six variables were used, namely: real GDP, budget deficit, real interest rate, labour, gross fixed capital formation and unemployment. The Vector Error Correction Model (VECM) was used to estimate the long-run equation and also measure the correction from disequilibrium of preceding periods. Using annual time series data spanning the period 1985 to 2015, empirical evidence from the study revealed that budget deficits and economic growth are inversely related. It was therefore concluded that high levels of budget deficit in South Africa have detrimental effects on the growth of the economy. The estimate of the speed of adjustment coefficient found in this study revealed that about 29 per cent of the variation in GDP from its equilibrium level is corrected within one year. The results obtained in this study are favourably similar to those in the literature and are also sustained by previous studies.
Previous studies generally find mixed empirical evidence on the relationship between government spending and economic growth. This study re-examine the relationship between government expenditure and economic growth in South Africa for the period of 1990 to 2015 using the Vector Error Correction Model and Granger Causality techniques. The time series data included in the model were gross domestic Product (GDP), government expenditure, national savings, government debt and consumer price index or inflation. Results obtained from the analysis showed a negative long-run relationship between government expenditure and economic growth in South Africa. Furthermore, the estimate of the speed of adjustment coefficient found in this study has revealed that 49 per cent of the variation in GDP from its equilibrium level is corrected within of a year. Furthermore, the study discovered that the causality relationship run from economic growth to government expenditure. This implied that the Wagner’s law is applicable to South Africa since government expenditure is an effect rather than a cause of economic growth. The results presented in this study are similar to those in the literature and are also sustained by preceding studies.
It has been proven several times that linear models are unable to encapsulate nonlinear dynamics of macroeconomic and financial data such as inflation rates, exchange rates and stock prices to mention fewer. As a result, to overcome this problem, this current study adopted the nonlinear models due to the fact that they have required qualities to apprehend nonlinearity in a dataset. In order to predict a regime shifts, a five-day Johannesburg stock exchange allshare index (JSE-ALSI) spanning period from 02 January 2003 to 28 June 2019 was used as an experimental unit. This current study firstly employed Teräsvirta neural network test to detect the presence of nonlinearity and proceeded to estimate a two regime Markov-Switching autoregressive (MS-AR). The results of Teräsvirta neural network test revealed a highly significant nonlinearity with permanent seasonality as demonstrated by Kruskal-Wallis test. The predicted regime shifts by a latent dynamic allowed the autoregressive and variance parameters to promptly react to vital systemic shocks. As a result, this current study allowed volatility to oscillate between high and low volatility regimes that produced an expected duration of high volatility of two year and two months. This was a clear indication that there is a regime shifts in JSE-ALI which are modeled using Markov-Chain (MC) stochastic process. These findings may be used to inform robust policy making with the aim of safeguarding both the JSE and other global stock markets from the episodes of stock market crash. Moreover, other researchers can utilize the results of this study to calculate the risk associated with structural breaks and high volatility periods.
Previous studies generally find mixed empirical evidence on the relationship between government spending and economic growth. This study re-examine the relationship between government expenditure and economic growth in South Africa for the period of 1990 to 2015 using the Vector Error Correction Model and Granger Causality techniques. The time series data included in the model were gross domestic Product (GDP), government expenditure, national savings, government debt and consumer price index or inflation. Results obtained from the analysis showed a negative long-run relationship between government expenditure and economic growth in South Africa. Furthermore, the estimate of the speed of adjustment coefficient found in this study has revealed that 49 per cent of the variation in GDP from its equilibrium level is corrected within of a year. Furthermore, the study discovered that the causality relationship run from economic growth to government expenditure. This implied that the Wagner’s law is applicable to South Africa since government expenditure is an effect rather than a cause of economic growth. The results presented in this study are similar to those in the literature and are also sustained by preceding studies.
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