Employing Vector Error Correction Model (VECM), this dissertation aims to explore the principal influential factors of economic growth from external and internal perspectives. After extensive analysis and previous research, trade openness is the external factor considered, while financial markets and institutions are the internal ones. Based on the dataset of four typical fast-growing emerging economies-- China, India, South Africa and Russian Federation, this study found that there is a significant long-term equilibrium among GDP growth, trade openness, financial markets and institutions in China, and bidirectional causality can be observed between trade openness and GDP growth. Regarding the remaining economies, there are two sets of long-term relationships among these variables, where internal factors concerning financial development are more crucial in these countries, which also significantly affect the trade volumes in the long run. Results from this research indicate that the dominant growth-enhancing factors are closely related with a country's policy, history, and the most importantly, the focus of its development strategy.

Different capital regulatory requirements in the bank and insurer markets lead to finding and using of new more complex financial tools linked with capital release and subsequent optimization of the investment objectives, but they are also linked with promises and risk transfers that could cause a collapse or a systemic risk of the financial markets, as evidence by the recent financial crisis. The aim of my work is to examine the behavior of credit default swap spreads on the securitization and reinsurance markets, followed by analyzing arbitrage conditions between securitization and reinsurance markets by cointegration analysis. The thesis focuses on Italy because it is one of four main European players in the securitization market and it has highly developed bank and insurer markets. Moreover, it still faces to consequences of the recent financial crisis that is indicator of strong possible bases for above mentioned complex financial instruments. On the dataset of Top 8 Italian banks and insurer companies in the period 2006 - 2012 I showed by cointegration analysis a presence of just one cointegration relationship between securitization and reinsurance market, therefore I rejected possibility of arbitrage between these markets. But on the other hand, they converge to long term equilibrium slowly...

Η παρούσα μελέτη εξετάζει τη δυναμική σχέση μεταξύ των τιμών των ζωοτροφών και παραγωγού, καταναλωτή για τέσσερα είδη κρέατος: μοσχάρι, χοιρινό, αρνί και κοτόπουλο. Η σχετική βιβλιογραφία δείχνει ότι πολλοί παράγοντες επιδρούν στις τιμές των αγροτικών προϊόντων αλλά οι τιμές των ζωοτροφών είναι ο κυριότερος. Αυτό συμβαίνει γιατί οι ζωοτροφές αποτελούν πρώτη ύλη για την παραγωγή κρέατος και κατ΄επέκταση θα επηρέασουν τις τιμές παραγωγού και καταναλωτή. Τα δεδομένα αποτελούνται από 279 μηνιαίες τιμές που εκτείνονται από τον Ιανουάριο 1990 έως τον Ιανουάριο 2013. Χρησιμοποιώντας Johansen cointegration tests, Granger causality tests και impulse response functions τα εμπειρικά αποτελέσματα επιβεβαιώνουν πως οι τιμές των ζωοτροφών, οι τιμές παραγωγού και οι τιμές καταναλωτή δεν είναι ανεξάρτητες μεταξύ τους. / The present paper studies the relationship among feed prices, producer prices and consumer prices of meat: beef, pork, poultry and lamb. The literature indicates that there are many factors which affect agricultural commodity prices but the feed prices are the main. This is why feed has a principal role in the production of meat and will affect producer and consumer prices. The data consists of 279 monthly observations extending from January 1990 to January 2013. Using Johansen cointegration tests, Granger causality tests and impulse response functions, the empirical findings confirm that feed prices, consumer prices and producer prices are interdependent.

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.

Submitted by Marilene Donadel (marilene.donadel@unioeste.br) on 2018-10-25T20:05:30Z No. of bitstreams: 1 Laudelina_Ribeiro_2018.pdf: 1327358 bytes, checksum: 607edeb8bb5b6744a60b848c643c8920 (MD5) / Made available in DSpace on 2018-10-25T20:05:30Z (GMT). No. of bitstreams: 1 Laudelina_Ribeiro_2018.pdf: 1327358 bytes, checksum: 607edeb8bb5b6744a60b848c643c8920 (MD5) Previous issue date: 2018-08-03 / Coordenação de Aperfeiçoamento de Pessoal de Nível Superior - CAPES / The study of the Rational Expectations Hypotesis (REH) had its beginning after the 1960’s, setting the economic agents’ rationality as the theoretical base. The economic agents settle their expectations according to the current economic outlook; therefore, the economic environment becomes an important factor in shaping the entrepreneurs’ expectations, since great part of the decisions which entrepreneurs take occur in an economic scenario more uncertain. This dissertation aims to analyse the influence of the brazilian entrepreneurs’ expectations index in the transformation industry sector over the private investment, as well as the employment level in this industry. The period taken to be analysed is 2003-2017, and the data is monthly. The econometric model used to calculate the influence was estimated by the Vector Error Correction Model (VECM). The results show that the entrepreneurs’ expectations index in the processing industry sector had a considerable influence over the decisions of private investment and the level of employment in that sector in Brazil. Thus, a stable economic scenario leads to an increase in the level of confidence in the industry sector, which can stimulate the growth in the industrial sector output and in several other sectors throughout the country. / O estudo da Hipótese das Expectativas Racionais (HER) iniciou-se após os anos de 1960, tendo como princípio a racionalidade dos agentes. Os agentes econômicos formulam suas hipóteses e expectativas com base no contexto econômico atual; logo, o ambiente econômico torna-se um fator influente na formação das expectativas empresariais visto que, na maioria das vezes, a tomada de decisão dos empresários ocorre em um ambiente de incerteza. O presente estudo tem a finalidade de analisar a influência do índice de expectativas dos empresários brasileiros da indústria de transformação sobre o investimento privado e o emprego desta indústria. O período compreendido no estudo é de 2003 a 2017, com a base de dados mensal. Para avaliar os resultados, o método econométrico utilizado foi estimado pelo Modelo Vetor de Correção de Erros (VECM). Os resultados apontam que, no período estudado, o índice de expectativas dos empresários da indústria de transformação influenciou as decisões relacionadas com o investimento privado e com o emprego das indústrias de transformação do país. Sendo assim, um cenário econômico estável proporciona um aumento da confiança dos empresários industriais, fazendo crescer sua expectativa em relação a seus negócios futuros e à economia do país e proporcionando um aumento de seus investimentos industriais, que podem impulsionar o crescimento da atividade do setor industrial e das demais atividades econômicas do país.

Submitted by Pedro Bogado (pedrobogado@gmail.com) on 2013-01-10T14:13:41Z No. of bitstreams: 1 PedroBogado_final.pdf: 351157 bytes, checksum: 8e8aaaca8adbafbbe0148af641564a29 (MD5) / Approved for entry into archive by Marcia Bacha (marcia.bacha@fgv.br) on 2013-01-29T16:26:40Z (GMT) No. of bitstreams: 1 PedroBogado_final.pdf: 351157 bytes, checksum: 8e8aaaca8adbafbbe0148af641564a29 (MD5) / Made available in DSpace on 2013-01-29T16:28:57Z (GMT). No. of bitstreams: 1 PedroBogado_final.pdf: 351157 bytes, checksum: 8e8aaaca8adbafbbe0148af641564a29 (MD5) Previous issue date: 2010-11-03 / The identification problem of supply and demand equations for testing the bank lending channel has been discussed in recent decades. This work evaluates the identification strategy carried out in a VECM setting to determine if there is empirical evidence of a bank lending channel in the transmission of monetary policy in Brazil. Monthly aggregate data was used for the period 2001 through 2010. / O problema da identificação de equações de oferta e demanda de crédito para verificação da existência do canal de crédito tem sido sendo bastante discutido nas últimas décadas. Este trabalho avalia a estratégia de identificação via estimação de um modelo de um Modelo Vetorial de Correção de Erros para determinar a relevância do canal de crédito no Brasil. Foram utilizados dados agregados mensais compreendendo o período de 2001 até 2010.

Dividend policy is widely researched in financial management, but determining whether it affects the market price per share is difficult. There has been much published on the subject, which presented theories such as the Modigliani, Miller, Gordon, Lintner, Walter and Richardson propositions and the relevance and irrelevance theories. However, little research has been done on the impact of dividend policy on shareholders’ wealth while considering the short- and long-run effects. The Vector Error Correction Model (VECM) was used to describe the short-run and long-run dynamics or the adjustment of the cointegrated variables towards their equilibrium values in South Africa. This study attempts to explain the effect of dividend policy on the market price per share. A sample of 46 companies listed on the Johannesburg Securities Exchange (JSE) was selected for the period 1995-2010. Three variables were used, namely the market price per share, the dividend per share and the earnings per share. The market price per share was used as a proxy in measuring shareholders’ wealth and the dividend per share was used as a proxy in measuring the dividend policy. Fixed and random effects models were applied to panel data to determine the relation between dividend policy and market price per share. The fixed effects method was used to control the stable characteristics of the companies over a fixed period. The random effects model was applied when the companies’ characteristics differed. Results for both models indicated that dividend yield is positively related to market price per share, while earnings per share do not have a significant impact on the market price per share. To test the strength of the long-run relationship, the VECM was applied. The coefficient for dividend per share in the co-integrating equation was positive, while the coefficient for earnings per share was negative. This confirms previous research findings. The results suggest that there is a long-run relationship between dividend per share and market price per share. The Granger causality test indicates there is bi-directional Granger causality between market price per share and dividend per share in South Africa. Therefore dividend policy does have a significant long-run impact on the share price and therefore provides a signal about the company’s financial success. / Dissertation (MCom)--University of Pretoria, 2012. / Financial Management / Unrestricted

Thesis (M.Sc. Agriculture (Agricultural Economics)) -- University of Limpopo, 2022 / Beef and chicken meat play a very crucial role in providing food to South African consumers. However, the rise of food prices in South Africa is viewed to curtail progress and drives consumers into debt and forgone opportunity to access food. Hence, it is of importance to understand the consumer price index (CPI) of meat and the disaggregate components of beef and chicken meat producer price indexes (PPI) as they give a clear insight into how individual commodities contribute to the general and food price inflation. The study aimed to comparatively analyse the relationship between PPI beef and CPI meat as well as PPI chicken meat and CPI meat in South Africa from 1991 to 2018. The objectives of the study were to compare the indexes’ variability, correlation, and causality between the different PPI and CPI components. The objectives were analysed using the Coefficient of variation (CV), the Pearson coefficient correlation, the Granger causality test, and the Vector Error Correction model. The CV findings highlight that PPI beef had high variability (65%) compared to CPI meat (56.7%), whereas PPI chicken meat had low variability (49.2%) compared to CPI meat(56.7%). There was evidence of a positive correlation (0.99) between PPI beef and CPI meat as well as PPI chicken meat and CPI meat using Pearson coefficient correlation. In addition, a long-run relationship was found between PPI beef and CPI meat as well as between PPI chicken meat and CPI meat by using the VEC model. Granger causality results indicated that there was a unidirectional relationship from PPI chicken meat to CPI meat, and independent relationships were found from PPI beef to CPI meat, CPI meat to PPI beef as well as CPI meat to PPI chicken meat. Based on the findings, the study recommends that policymakers, through evaluation of monetary policies, should continue maintaining a specific inflation target range as that will assist in stabilising meat prices in the economy. At the same time, protect meat producers against input price inflation using instruments such as input subsidies, grants, and the provision of modern technologies. / National Research Foundation (NRF)

No description available.