Thesis (M. Com. (Economics)) -- University of Limpopo, 2019 / Financial markets and quite a diverse number of financial instruments have been growing in a controlled manner in recent decades in terms of value and volume. Brazil, Russia, India, China and South Africa (BRICS) are distinguished as having the fast growing markets in the universe compared to other markets of emerging economies, according to their promising economic prospective and demographic power. This study investigated the effects of government stock on investment activity in BRICS countries. This study used panel autoregressive distributed lag model (PARDL), Engel-Granger causality test, impulse response functions (IRF) and variance decomposition tests. Such techniques were applied to the annual data for the periods 2001 to 2016 in order to determine the effects of government stock on investment activity. The variables (government stock on bonds, government stock on mutual banks, government stock on corporations and government stock on liquid assets), including gross fixed capital formation which is a measure of investment activity, were subjected to panel unit root tests and that confirmed different orders of cointegration. The existence of a long run relationship between investment activity and other macroeconomic variables used in this study was determined by means of the panel cointegration tests, where one lag was used. The PARDL showed that in the long run investment activity was positively influenced by government stock on mutual banks and government stock on liquid assets, and negatively related to government stock on bonds and government stock on corporations. The Engel-Granger causality test revealed existence of unidirectional movement between investment activity and government stock on corporations as well as from government stock on bonds to liquid assets. The impulse response function test showed the impulse percentage of fluctuation that the variables did contribute to each other, from various periods both in the short and long run. While the variance decomposition of investment indicated that Investment was shocked by its own innovations throughout all the periods. A critical evaluation is needed to avoid investment shocks, instability of investment activity, instability of financial markets and the economy as a whole.

Thesis (Ph.D. (Economics)) -- University of Limpopo, 2022 / This study investigated the relationship between economic complexity and the three mac-roeconomic variables in a comparative setting between selected Sub-Saharan African (SSA) and BRICS countries. Economic complexity as a development index reveals how sophisticated a country is as shown by its exports structure through the Product Com-plexity Index (PCI) and Economic Complexity Index (ECI). The three macroeconomic var-iables are gross domestic product per capita (GDP per capita), current account and fixed investment (gross fixed capita formation) for the period 1994 to 2018.The first three set study objectives were investigated on whether there exists a short and long-run relation-ship through a Panel Autoregressive Distributed Lag (PARDL). The the fourth objective was to test for causality through a standard Granger causality, and fifth, to forecast the macroeconomic variables for the foreseeable future utilising the Impulse Response Func-tion (IRF) and the variance decomposition techniques, these are complementary tech-niques. The last two objectives were to draw a comparative analysis upon the findings, and to relate on the product complexities and economic landscape in the selected SSA and BRICS. Reporting on the ECI-GDP per capita nexus, the PARDL estimates revealed a positive and significant association between ECI and GDP per capita in both the se-lected SSA and BRICS in the long-run. There was no Granger causal effect between ECI and GDP per capita for both set of countries. The concern was in relation to forecasting GDP per capita due to a shock in ECI. The selected SSA GDP per capita response to a shock in ECI was neutral when adopting the IRF technique, and the variance decompo-sition also revealed small estimates in both the short and long-run, below 1%. In the BRICS economies, there was a meaningful positive reaction from a shock in ECI when deploying the IRF technique, while the variance decomposition had a 3% response in the long run when seen through the variance decomposition. On the current account-ECI relationship, the PARDL estimates exposed that there was a positive and significant impact from ECI on the current account in both the groups in the long-run significant while short-run results were insignificant. Granger causality could not detect any causal effect between ECI and current account in the selected SSA, while in the BRICS countries there was a unidirectional causal effect from ECI to current account. When forecasting the current account, the selected SSA reacted negatively to a shock in v ECI seen through the IRF, and the variance decomposition also revealed a small reaction in any period. In the BRICS case, current account’s response was a positive and explo-sive reaction from a shock in ECI when applying the IRF technique. The VD revealed a higher change in current account was explained by a shock in ECI. On the ECI-Fixed Investment, the PARDL estimates showed that there was a long-run positive and signifi-cant effect between ECI and fixed investment in bothgroups. However, the Granger causal results revealed no presence of causality in the selected SSA, while there was causal unidirectional effect from ECI to fixed investment. The IRF technique revealed a negative fixed investment reaction from a shock in ECI, and the variance decomposition results revealed a small reaction in fixed investment in the selected SSA. In the BRICS case, there was a positive and explosive fixed investment emanating from a shock in ECI. Utilising the variance decomposition fixed investment in BRICS was explained by inno-vative shocks in ECI in the long run. On the last two objectives, comparatively the selected SSA countries are disadvantaged as they are concentrated in negative ECI as seen in the descriptive statistics, reflecting that they are still much less developed. This tells us that they are less industrialised as compared to the BRICS nations who are better off. These selected SSA economies are not developed enough as compared to the BRICS nations. The SSA region needs to learn from the leading BRICS countries by creating a conducive environment for a better de-velopment of innovation that improves the domestic value chain that produces knowledge-based products for the export market. The rest of the selected SSA region should form part of economic integrations with the more developed countries that offer mutual beneficiation like South Africa to fast track the developmental of their states. There is a need to modernise the agricultural and agro-industries. The region should harness the full potential of its agricultural sector. This will create a large global market share and perhaps increase the current account outlook through trade with more efficient agro-pro-cessed products. Africa needs to scale up investment in many fronts from government to private investment to improve infrastructure, more so that the scale of needs is so much in the continent.