This paper investigates the long run relationship between health care expenditure and economic growth, using panel data for 14 Southern African Development Community (SADC) member countries over the period 1995-2012. The non-stationarity and cointegration properties between health expenditure per capita and GDP per capita were examined, controlling for cross section dependence and heterogeneity between countries. Our results suggest that health expenditure and GDP per capita are non-stationary and cointegrated. These findings seem to confirm the notion that health expenditure is non-discretionary-health is a necessary good-in the SADC region. The estimated income elasticity is below unity but higher than what was obtained for the OECD regional grouping. The policy implication of our result is that adequate health care service provision should be a key objective of governmental intervention in the SADC region.

In this paper, we extend the works by [1][2][3][4][5] accounting for autocorrelation both in the time specific effect as well as the remainder error term. Several transformations are proposed to circumvent the double autocorrelation problem in some specific cases. Estimation procedures are then derived.

In this paper we extend the Baillie and Baltagi (1999) paper (Prediction from the regression model with one-way error components. In Analysis of Panels and Limited Dependent Variables Models, Hsiao C, Lahiri K, Lee LF, Pesaran H (eds). Cambridge University Press, Cambridge, UK). In particular, we derive six predictors for the two-way error components model, as well as their associated asymptotic mean squared error (AMSE) of multi-step prediction. In addition, we also provide both theoretical and simulation evidence as to the relative effi ciency of our six alternative predictors. The adequacy of the prediction AMSE formula is also investigated by the use of Monte Carlo methods which indicate that the ordinary optimal predictors perform well for various accuracy criteria.

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High diamond price volatility can have significant impact on Botswana's diamond-driven economy. The global economic crisis of 2008-2009 saw the local economy characterised by heightened commodity price uncertainty, falling stock prices and dwindling international demand for diamonds. In this paper we employ a number of techniques to analyse and assess the effect of diamond price volatility on stock returns in Botswana. Firstly, estimation of a Markov Switching model reveals that high volatility regimes in diamond prices have become more frequent and persistent since the recession. Secondly, a bivariate GARCH-in-Mean VAR model is estimated and the results recognize that diamond price volatility has a positive and significant influence on stock returns in Botswana.

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