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This paper examines the relationaship between financial development and economic growth in ghana. This is done using time series econometric procedures by employing four proxy of financial development and applying granger causality test, cointegrating test, vector error correction model. The empirical results show that the direction of causalty is sensitive to the choice of proxy. It was discovered that finance follows in the direction of economic growth but doesnt necessarily lead to it. The empirical cointegration results weakly supprt long run relationship between financial development and economic growth.

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Economic Value Added (EVA) is a residual income that corrects distortion of managerial incentives introduced by standard GAAP accounting. This study attempts to compare EVA with EPS and see which one is better. The difference between literature and this study is that we use not only cross-regression but also Granger causality test to make clear the relationship between stock return and performance measure and find out what is the value of EVA. Our main finding is as follows¡G 1.EVA significantly positively affects the contemporaneous stock return, but EPS is insignificant. This support the existence of EVA. 2.The components unique to EVA --- the cost of capital, significantly negatively affects the contemporaneous stock return, indicating that market does take into consideration the cost of capital when pricing the company. 3.As to Granger causality relationship, there is no lead-lag relationship between stock return and EVA or EPS. This means that performance measure cannot be a predictor of future stock return. In a word, EPS, ignoring equity capital and being distorted by GAAP accounting, neither explains the contemporaneous stock return, nor forecasts the future. However, EVA, considering equity capital and correcting distortion of GAAP accounting, can explain the contemporaneous stock return by representing the intrinsic value of the company. But, EVA, being still on the basis of history, cannot forecast the future. Anyway, EVA can replace EPS in reflecting the operating of the company, that is the contribution of EVA.

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This thesis investigates the monetary sector in Cameroon within an open economy framework. Two main hypotheses: money demand and Granger-causality are investigated. The data used are found to be non-stationary. Consequently, the money demand relationship is tested for the null hypothesis that it is spurious or not co-integrated. This is rejected in all the models put forward. The models are estimated and found to exhibit elasticities that are not unusual. Price homogeneity is found to be data incompatible. Income elasticities are generally found to be significantly less than unity suggesting economies of scale in money holdings. Corresponding dynamic models in the form of error correction are constructed using the familiar general to specific methodology and generally found to exhibit desirable statistical properties. Model preference is in terms of the narrow Ml definition of money with explanatory variables which include a foreign interest rate. For Granger-causality, the non-stationary data are transformed into stationarity where the null hypothesis of noncausality is tested in bivariate and multivariate contexts. Lag length selection is by the Final Prediction Error statistic. Results are mixed but two appear striking: domestic money and prices are found to be independent while domestic prices are Granger-caused by foreign variables but not by domestic ones.

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This paper studies the price fluctuation from 2010 to 2016 of two major assets in China: real estate and stock. Equity price is found to Granger cause stock price while the reverse relationship is significant but less strong. The paper then studies whether the nature of the correlation depends on the type of city under consideration. This is achieved by grouping 25 cities into four city tiers based on their level of economic developments and conducting a linear causality test on each city tier. Housing price in first tier cities is found to be much more significantly correlated with stock price. Larger and more developed cities tend to have a stronger correlation with stock than smaller and less developed ones. In addition, the paper also studies the impact of the Chinese government’s recent home purchase restriction on the relationship between the two asset classes. However, the results are contradictory and are not consistent with expectation. The lack of significant results could be contributed to the inherent limitation of our data, as well as the complicated and sometimes confusing policy announcement mechanisms in China.

<p>The dependence on oil has increased in many nations as a result of increasing industrialization and oil has been the factor of many crises as well as many wars. This paper examines how the price of oil affects the unemployment in Sweden. The case of Sweden is interesting since its politics are very different compared to other industrialized countries when it comes to unemployment and benefits. Our main objective is to see whether a change in the oil price will cause a change in unemployment at a later stage. We perform linear regression analysis relating current changes in the variables and Granger causality tests to conclude if there exists a direct relationship.</p><p>The result we received from our linear regression test on current changes and our Granger causality test showed a relationship between the price of oil and unemployment in Sweden. In the linear regression relating current changes in these variables, a positive relationship was indicated. Due to the fact that some of the coefficient estimates are positive and some are negative in the Granger causality regressions, we can not conclude whether an increase in the price of oil will cause a positive or negative effect on unemployment.</p>

<p>The purpose of this paper is to investigate the predictive power of the variables advanced notice of dismissal (layoffs) and vacancies for the unemployment rate. Based on the Box Jenkins Methodology, the paper makes use of Granger causality and out-of-sample tests to compare the forecast performance of a naïve reference model and the two models extended to include either lagged values of layoffs or vacancies. It is shown that layoffs make up a significant leading variable, exhibiting particularly strong predictive power at forecast horizons of 2-6 months. It is also shown that the predictive power of vacancies is more ambiguous. Vacancies constitute a valuable explanatory variable for the unemployment rate, but does not possess the same leading, predictive qualities as layoffs.</p>