Focused Bayesian Prediction

We propose a new method for conducting Bayesian prediction that delivers accurate predictions without correctly specifying the unknown true data generating process. A prior is defined over a class of plausible predictive models. After observing data, we update the prior to a posterior over these models, via a criterion that captures a user-specified measure of predictive accuracy. Under regularity, this update yields posterior concentration onto the element of the predictive class that maximizes the expectation of the accuracy measure. In a series of simulation experiments and empirical examples we find notable gains in predictive accuracy relative to conventional likelihood-based prediction

The Impact of Oil Revenues on the Iranian Economy and the Gulf States

In line with the neoclassical growth model a persistent stream of oil revenues might have a long lasting impact on GDP per capita in oil exporting countries through higher investment activities. This relationship is explored for Iran and the countries of the Gulf Cooperation Council (GCC) using (panel) cointegration techniques. The existence of cointegration between oil revenues, GDP and investment can be confirmed for all countries. While the cointegration vector is found to be unique for Iran, long run equations for GDP and investment per capita are distinguished for the Gulf countries. Both variables respond to deviations from the steady state, while oil income can be treated as weakly exogenous. The long run oil elasticities for the Gulf states exceed their Iranian counterparts. In addition, investment in Iran does not react to oil revenues in the long run. Hence, oil revenues may have been spend less wisely in Iran over the past decades