Introduction: The association between daily air pollution exposure and risk of mortality is well established. Few studies have investigated in detail the associations beyond a seven day lag. The aim of this thesis was to investigate the change in risk across longer (30 day) periods post exposure for three specific causes of death: pneumonia, chronic obstructive pulmonary disease (COPD), and ischaemic heart disease (IHD). Methods: Daily Scottish mortality data (1980-2011) was matched to measurements from local fixed site pollution (Black smoke, PM10, PM2.5, SO2, & NO2) and temperature monitors. Exposure on subjects' 'day of death' was compared with control days in a time-stratified case-crossover analysis. Exposure effects on 30 days prior to day of death were modelled using distributed lag non-linear, lag stratified, and cubic distributed lag models. Matching hospital admissions data inferred subject location during exposure, further analyses investigated extreme outliers and missing data using multiple imputation techniques. The analysis accounted for several confounders including accurately modelling temperature relationships unique for each cause of death. Results: Of the 919,301 deaths, 20% were classified as being caused by pneumonia, 9.5% as COPD, and 30% as IHD in the 'any' cause of death field. Non-linear effects for temperature and linear effects for the pollutants were present across all 30 days. Temperature-mortality was observed to be U-shaped at shorter lags. Consistently increased risk occurred for longer in cold temperatures with 1oC increase (30 days lag) = %RR -0.35% Pneumonia, -0.62% COPD, and -0.26% IHD. PM2.5 on all three outcomes, and all pollutants on COPD showed the greatest effect sizes. In general, COPD risk only occurred after a delay, peaking between 12-18 days. COPD risk due to PM2.5 was immediate (%RR (95% C.I.) = 1.05% (0.14%,2.01%)) and lasted the full 30 days. Pneumonia risk often reported the shortest lag of 10-15 days, whereas IHD risk occurred 2 days after exposure but lasted the remaining 30 days. There was some evidence especially for pneumonia of a smaller association between air pollution on mortality when subjects included were present in hospital. A simulation study indicated slight improvement in accuracy when 'multiple imputation' was performed compared to 'complete cases' analysis; though both techniques reported similarly underestimated effect estimates. Extreme outliers in the main analysis of pollution exposure did not appear to have a strong influence on the risk. However, large variability between monitor measurements of pollution exposure was present and appeared to be influencing the results. Conclusion: This study provides additional evidence on the link between air pollution, and temperature, and acute mortality. Particular focus was on three causes of death (pneumonia, COPD, and IHD) that are shown to be influenced by air pollution in subtly different ways. Results also indicated that the 'true' effect of air pollution on mortality might be greater than shown by mortality studies which do not use hospital admission location during exposure into account.

The main purpose of the study is to examine the cointegration relationships among economic growth, foreign direct investment and financial sector development in 4 East Asian countries, namely Korea, Malaysia, the Philippines and Thailand between the years 1971-2008 by autoregressive distributed lag (ARDL) approach. In the existing literature, there is no study examining the causal relationships among economic growth, foreign direct investment and financial sector development by applying ARDL methodology for these East Asian countries. The contribution of this study to the literature, the cointegration relationships are constructed to observe the direct linkage among these variables by ARDL approach. If cointegration relationships exist among these variables, then the effect of each regressor on the dependent variable is also investigated. The results of the study indicate that foreign direct investment and financial sector development could be long run forcing variables of economic growth. Additionally, economic growth and financial sector development could be long run forcing variables of foreign direct investment. However, there is not sufficient evidence that economic growth and foreign direct investment together are long run key determinants of financial sector development in a country as obtained in this study.

This thesis employs the Autoregressive Distributed Lag model (ARDL), Toda-Yamamoto causality analysis, and ordinary least square (OLS for robust estimation) techniques to empirically investigate the impact of economic growth and energy consumption on the environment in Nigeria from 1980 to 2020. The results of cointegration demonstrate a long-term link between the model's input variables. The outcome of the first objective of the study shows that trade and economic development in Nigeria worsen the state of the environment. Environmental quality is accelerated by financial development; nevertheless, FDI is proven to be insignificant in predicting environmental quality. The result demonstrates that FDI and energy use both have the potential to significantly speed up the rate of environmental degradation. Nevertheless, trade has a negligible impact on the environment in the country, and financial development slows down environmental deterioration. The study also finds that the combination between energy and economic development improves Nigeria's environmental quality. The outcome of the fourth objective shows that economic expansion and energy consumption have a favorable impact on the environment. Additionally, environmental degradation, energy use, and economic growth are all causally related. Moreover, the outcome of the robust estimation reveals a positive and significant relationship between economic growth and energy consumption in the environment. Therefore, the study suggests economic policies with environmental control measures. This could be through an emphasis on the use of other alternatives of low-emission energy, that will mitigate the level of C02 and enhance energy utilization for a better environment in the nation.

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The causal relationship between financial development and economic growth is a controversial issue. For developing countries, empirical studies have provided mixed result. This study seeks to empirically explore the relationship and the causal link between financial development and economic growth in two sub-Saharan African countries between 1970 and 2006. The empirical investigation is carried out using time methods and the five most commonly used indicators of financial development in the literature. However, the causal relationship was carried out using two different methods which are the autoregressive distributed lag bounds testing (ARDL) and the vector error correction model (VECM). Using this above methodology the study first found that in both countries there is a positive and long-term relationship between all the indicators of financial development and economic growth which was proxied by the real per capita GDP. With respect to the causality test, the two methods used provide mixed results especially in South Africa. In Cameroon the study found that financial development causes economic growth using the two methods, whereas in South Africa economic growth causes financial development when the VECM method is used, while there is an independence relationship between the two variables in South Africa when using ARDL. / Economics / M.Comm. (Economics)

Liberalisation of trade is deepening, and so have the incentive schemes put in place by a number of countries to promote it. International trade promotion agencies in developing countries are actively promoting their countries as the best, with which to trade. With international trade emerging as a favourite source of revenue and technology transfer for most countries, profound questions about the impact of trade liberalisation to economic growth are addressed in this study. The main purpose of this study is to empirically assess the relationship between trade liberalisation and economic growth in Zimbabwe using annual time series data from 1980 to 2017. Autoregressive distributed lag (ARDL) bounds testing approach to cointegration and Error Correction Mechanism (ECM) are applied in order to investigate the long run and short run impact of trade liberalisation on economic growth. The results proved the existence of a positive long-run relationship between trade liberalisation and economic growth. The study therefore concludes that policy makers and government negotiators in Zimbabwe should introduce policies that promote openness through the removal of barriers to trade and export promotion in order to promote overall growth of the economy. / Economics / M. Com (Economics)

Until very recently, most emerging market economies have achieved higher growth rates for the last decade. It is controversial whether this good economic environment is due to domestic reforms or due to favorable external factors. In this framework, the main aim of this study is to investigate the structure and sources of business cycles in emerging market economies and to determine how these cycles differ than those in developed countries. The role of external and domestic factors on business cycles are analyzed by applying not only the conventional panel data estimations but also common correlated effects panel mean group method which is introduced by Peseran (2006). Besides, the convergence of business cycles in emerging market economies to the business cycles in developed countries is discussed based on factor analysis. The major results indicate the common global factors are the leading source of the business cycles both in emerging market economies and developed countries. However, domestic determinants of fluctuations differ across two groups of countries. In addition, results show that in the last two decades fluctuations in emerging market economies have started to be more dependent on the fluctuations in developed countries.

In this study, the effects of CBRT monetary policy decisions on the consumer, automobile, housing and commercial loans of the banks during the period from the early of 2004 to the middle of 2011 are examined. In order to perform this study, it is benefited from weekly weighted average loan interest rate data of the banks, which is the data having the highest frequency that could be obtained from the electronic data distribution system of CBRT. Monetary policy instruments of Central Bank may change in the course of time or monetary policy could be executed by more than one instrument. Therefore, as the political interest rate would be insufficient in the calculation of the effect of monetary policy on loan interest rates of the banks, Government Dept Securities&rsquo / premiums are used instead of the political interest rates in this study to make it reflect the policies of central bank more clearly as a whole. Among the Government Dept Securities that have different maturity structure, benchmark bonds that are adapted to the expected political interest rate changes and that react to the unexpected interest rate changes at the high rate (reaction coefficient 0.983) are used. In order to weight the cointegration relation between interest rates, unrestricted error correction model is established and it is determined by Bound Test that there is a long-term relation between each interest rate and interest rate of benchmark bond. After a cointegration relation is determined among the serials, autoregressive distributed lag model is used to determine the level of transitivity and it is determined that monetary policy decisions affect the banking interest rate at 77% level and by 13 weeks delay on average.

Tourism is currently the third largest industry within South Africa. Many African countries, including South Africa, have the potential to achieve increased economic growth and development with the aid of the tourism sector. As tourism is a great earner of foreign exchange and also creates employment opportunities, especially low–skilled employment, it is identified as a sector that can aid developing countries to increase economic growth and development. Accurate forecasting of tourism demand is important due to the perishable nature of tourism products and services. Little research on forecasting tourism demand in South Africa can be found. The aim of this study is to forecast tourism demand (international tourist arrivals) to South Africa by making use of different causal models and to compare the forecasting accuracy of the causal models used. Accurate forecasts of tourism demand may assist policy–makers and business concerns with decisions regarding future investment and employment. An overview of South African tourism trends indicates that although domestic arrivals surpass foreign arrivals in terms of volume, foreign arrivals spend more in South Africa than domestic tourists. It was also established that tourist arrivals from Africa (including the Middle East), form the largest market of international tourist arrivals to South Africa. Africa is, however, not included in the empirical analysis mainly due to data limitations. All the other markets namely Asia, Australasia, Europe, North America, South America and the United Kingdom are included as origin markets for the empirical analysis and this study therefore focuses on intercontinental tourism demand for South Africa. A review of the literature identified several determinants of tourist arrivals, including income, relative prices, transport cost, climate, supply–side factors, health risks, political stability as well as terrorism and crime. Most researchers used tourist arrivals/departures or tourist spending/receipts as dependent variables in empirical tourism demand studies. The first approach used to forecast tourism demand is a single equation approach, more specifically an Autoregressive Distributed Lag Model. This relationship between the explanatory variables and the dependent variable was then used to ex post forecast tourism demand for South Africa from the six markets identified earlier. Secondly, a system of equation approach, more specifically a Vector Autoregressive Model and Vector Error Correction Model were estimated for each of the identified six markets. An impulse response analysis was undertaken to determine the effect of shocks in the explanatory variables on tourism demand using the Vector Error Correction Model. It was established that it takes on average three years for the effect on tourism demand to disappear. A variance decomposition analysis was also done using the Vector Error Correction Model to determine how each variable affects the percentage forecast variance of a certain variable. It was found that income plays an important role in explaining the percentage forecast variance of almost every variable. The Vector Autoregressive Model was used to estimate the short–run relationship between the variables and to ex post forecast tourism demand to South Africa from the six identified markets. The results showed that enhanced marketing can be done in origin markets with a growing GDP in order to attract more arrivals from those areas due to the high elasticity of the real GDP per capita in the long run and its positive impact on tourist arrivals. It is mainly up to the origin countries to increase their income per capita. Focussing on infrastructure development and maintenance could contribute to an increase in future tourist arrivals. It is evident that arrivals from Europe might have a negative relationship with the number of hotel rooms available since tourists from this region might prefer accommodation with a safari atmosphere such as bush lodges. Investment in such accommodation facilities and the marketing of such facilities to Europeans may contribute to an increase in arrivals from Europe. The real exchange rate also plays a role in the price competitiveness of the destination country. Therefore, in order for South Africa to be more price competitive, inflation rate control can be a way to increase price competitiveness rather than to have a fixed exchange rate. Forecasting accuracy was tested by estimating the Mean Absolute Percentage Error, Root Mean Square Error and Theil’s U of each model. A Seasonal Autoregressive Integrated Moving Average (SARIMA) model was estimated for each origin market as a benchmark model to determine forecasting accuracy against this univariate time series approach. The results showed that the Seasonal Autoregressive Integrated Moving Average model achieved more accurate predictions whereas the Vector Autoregressive model forecasts were more accurate than the Autoregressive Distributed Lag Model forecasts. Policy–makers can use both the SARIMA and VAR model, which may generate more accurate forecast results in order to provide better policy recommendations. / Thesis (M.Com. (Economics))--North-West University, Potchefstroom Campus, 2011.

Tourism is currently the third largest industry within South Africa. Many African countries, including South Africa, have the potential to achieve increased economic growth and development with the aid of the tourism sector. As tourism is a great earner of foreign exchange and also creates employment opportunities, especially low–skilled employment, it is identified as a sector that can aid developing countries to increase economic growth and development. Accurate forecasting of tourism demand is important due to the perishable nature of tourism products and services. Little research on forecasting tourism demand in South Africa can be found. The aim of this study is to forecast tourism demand (international tourist arrivals) to South Africa by making use of different causal models and to compare the forecasting accuracy of the causal models used. Accurate forecasts of tourism demand may assist policy–makers and business concerns with decisions regarding future investment and employment. An overview of South African tourism trends indicates that although domestic arrivals surpass foreign arrivals in terms of volume, foreign arrivals spend more in South Africa than domestic tourists. It was also established that tourist arrivals from Africa (including the Middle East), form the largest market of international tourist arrivals to South Africa. Africa is, however, not included in the empirical analysis mainly due to data limitations. All the other markets namely Asia, Australasia, Europe, North America, South America and the United Kingdom are included as origin markets for the empirical analysis and this study therefore focuses on intercontinental tourism demand for South Africa. A review of the literature identified several determinants of tourist arrivals, including income, relative prices, transport cost, climate, supply–side factors, health risks, political stability as well as terrorism and crime. Most researchers used tourist arrivals/departures or tourist spending/receipts as dependent variables in empirical tourism demand studies. The first approach used to forecast tourism demand is a single equation approach, more specifically an Autoregressive Distributed Lag Model. This relationship between the explanatory variables and the dependent variable was then used to ex post forecast tourism demand for South Africa from the six markets identified earlier. Secondly, a system of equation approach, more specifically a Vector Autoregressive Model and Vector Error Correction Model were estimated for each of the identified six markets. An impulse response analysis was undertaken to determine the effect of shocks in the explanatory variables on tourism demand using the Vector Error Correction Model. It was established that it takes on average three years for the effect on tourism demand to disappear. A variance decomposition analysis was also done using the Vector Error Correction Model to determine how each variable affects the percentage forecast variance of a certain variable. It was found that income plays an important role in explaining the percentage forecast variance of almost every variable. The Vector Autoregressive Model was used to estimate the short–run relationship between the variables and to ex post forecast tourism demand to South Africa from the six identified markets. The results showed that enhanced marketing can be done in origin markets with a growing GDP in order to attract more arrivals from those areas due to the high elasticity of the real GDP per capita in the long run and its positive impact on tourist arrivals. It is mainly up to the origin countries to increase their income per capita. Focussing on infrastructure development and maintenance could contribute to an increase in future tourist arrivals. It is evident that arrivals from Europe might have a negative relationship with the number of hotel rooms available since tourists from this region might prefer accommodation with a safari atmosphere such as bush lodges. Investment in such accommodation facilities and the marketing of such facilities to Europeans may contribute to an increase in arrivals from Europe. The real exchange rate also plays a role in the price competitiveness of the destination country. Therefore, in order for South Africa to be more price competitive, inflation rate control can be a way to increase price competitiveness rather than to have a fixed exchange rate. Forecasting accuracy was tested by estimating the Mean Absolute Percentage Error, Root Mean Square Error and Theil’s U of each model. A Seasonal Autoregressive Integrated Moving Average (SARIMA) model was estimated for each origin market as a benchmark model to determine forecasting accuracy against this univariate time series approach. The results showed that the Seasonal Autoregressive Integrated Moving Average model achieved more accurate predictions whereas the Vector Autoregressive model forecasts were more accurate than the Autoregressive Distributed Lag Model forecasts. Policy–makers can use both the SARIMA and VAR model, which may generate more accurate forecast results in order to provide better policy recommendations. / Thesis (M.Com. (Economics))--North-West University, Potchefstroom Campus, 2011.