The paper analyses demand for different monetary aggregates (M0, M1, M2 and M3) in Kenya for the period 1997:4-2011:2. Dynamic frameworks are used to estimate and uncover parsimonious and empirically stable demand for money functions. Price, real GDP, nominal 91-Day Treasury bill rate, nominal interbank rate, nominal deposit rate and foreign interest rate affected the long-run demand for money functions to different degrees. The demand for money functions is found to be unstable over the period for the parameter values, implying that the current monetary targeting policy framework is inappropriate. However, there are challenges in adopting an alternative monetary policy framework.

This paper investigates the nexus between government expenditure and government revenue in South Africa within the framework of a vector autoregressive (VAR) approach. It uses the Hylleberg et al. (1990) method to test for seasonal unit roots and finds that government revenue and government expenditure have unit roots at all frequencies. The Johansen procedure test results reveal that these variables are cointegrated. It is further established that revenue and expenditure are linked bidirectionally by Granger causality in the long-run, while there is no evidence of Granger causalityin the short-run in South Africa.

The paper develops a Bayesian vector autoregressive (BVAR) model of the South African economy for the period of 1970:1-2000:4 and forecasts GDP, consumption, investment, short-term and long term interest rates, and the CPI. We find that a tight prior produces relatively more accurate forecasts than a loose one. The out-of-sample-forecast accuracy resulting from the BVAR model is compared with the same generated from the univariate and unrestricted VAR models. The BVAR model is found to produce the most accurate out of sample forecasts. The same is also capable of correctly predicting the direction of change in the chosen macroeconomic indicators.

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The study attempts to empirically identify factors that determine South Africa-US intra-industry trade (IIT) in selected services during the period 1994-2002. The study utilises Liu-DavidsonFlachaire wild bootstrap, which is robust to heteroscedasticity and provides estimates of the degree of parameter bias. The empirical results, in principle, show that South Africa-US IIT in the selected services is determined by factors similar to goods-based "North-South" IIT studies. Specifically, differences in per capita income and differences in market size negatively affect IIT. The study also indicates that US foreign direct investment in South Africa positively contributes to the unaffiliated IIT in services.