Policy makers need accurate forecasts about future values of exchange rates. This is due to the fact that exchange rate volatility is a useful measure of uncertainty about the economic environment of a country. This paper applies univariate nonlinear time series analysis to the daily (TZS/USD) exchange rate data spanning from January 4, 2009 to July 27, 2015 to examine the behavior of exchange rate in Tanzania. To capture the symmetry effect in exchange rate data, the paper applies both ARCH and GARCH models. Also, the paper employs exponential GARCH (EGARCH) model to capture the asymmetry in volatility clustering and the leverage effect in exchange rate. The paper reveals that exchange rate series exhibits the empirical regularities such as clustering volatility, nonstationarity, non-normality and serial correlation that justify the application of the ARCH methodology. The results also suggest that exchange rate behavior is generally influenced by previous information about exchange rate. This also implies that previous day's volatility in exchange rate can affect current volatility of exchange rate. In addition, the estimate for asymmetric volatility suggests that positive shocks imply a higher next period conditional variance than negative shocks of the same sign. The main policy implication of these results is that since exchange rate volatility (exchange-rate risk) may increase transaction costs and reduce the gains to international trade, knowledge of exchange rate volatility estimation and forecasting is important for asset pricing and risk management.

This paper analyzes the effect of foreign direct investment (FDI) on agricultural sector in Tanzania. The paper also examines the declining contribution of agriculture to real GDP growth despite the fact that the sector employs more than 70 percent of the total labour force. Annual time series data spanning from 1990 to 2015 are used to test the significance of the relationship between FDI inflow and agriculture value added-to-GDP ratio on one hand and FDI inflows and economic growth on the other hand. Also, the relationship between agriculture value added and economic growth rate is empirically examined. Variables such as gross fixed capital formation, inflation rate, trade liberalization, real exchange rate and population are considered as control variables. For the purpose of inference, the paper employs classical linear regression model. Ordinary least squares methods are used for estimation. The diagnostic tests including RESET regression errors specification test, Breusch-Godfrey serial correlation LM test, Jacque-Bera-normality test and white heteroskedasticity test reveal that the models have no signs of misspecification and that, the residuals are serially uncorrelated, normally distributed and homoskedastic. Interestingly, empirical results suggest that there is no significant effect of FDI inflows on agriculture value added-to-GDP ratio in Tanzania despite the fact that FDI inflows in economy have been outstanding particularly in past two decades. Unsurprisingly, the results show that FDI inflows-to-GDP ratio and real GDP growth rate are positively correlated. Notwithstanding, agriculture sector, which constitutes the largest proportion of the total labour force, contributes, on average, less than 30 percent, to total GDP. This suggests that the sector is inefficient and therefore, effort towards attracting more FDI aiming at improving productivity in agriculture sector, which in turn may reduce poverty, is much needed.

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In this paper we analyze the effects of institutional variables (corruption and governance), structural variables (per capita income, trade openness, inflation and share of agriculture in GDP), and policy variables (tax rate and tariff rate) on total tax revenues, direct taxes, indirect taxes and trade taxes using panel data set for 30 African countries over the 1996-2016 period. All estimates are based on fixed effects (FE) and random effects (RE) models. Using Hausman test, RE is earmarked to be the more preferred model in this paper. The RE regression results show that corruption and governance are two main determinants of tax revenues in Africa. While corruption has a significant negative effect on tax revenues, good governance measured in terms of government effectiveness, regulatory quality, rule of law and voice and accountability tends to raise tax revenue generation and in particular, indirect taxes. In the same vein, governance in form of political stability tends to have a very significant effect on direct taxes and international trade taxes. The basic intuition behind these results is that higher institutional capacity and lower corruption enhance tax revenue generation in the economy. Intriguingly, empirical results show that tariff rates tend to have a strong negative effect on total tax revenue but at the same time they have a strong positive effect on trade tax revenue. Moreover, trade openness tends to have a strong positive relationship with tax revenue. Overall, results suggest that to raise more tax revenue, governments should reduce corruption, improve tax and customs administration and raise revenues from tax categories that are less susceptible to corruption. They should as well enhance trade openness.

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