In a stochastic frontier setting, we examine technical efficiency in the Middle East and North Africa (MENA). Evidence suggests that in addition to economic indicators, political and social ones play a key role in development and frontier technical efficiency profiles. The MENA have been...

In a stochastic frontier setting, we examine technical efficiency in the Middle East and North Africa (MENA). Evidence suggests that in addition to economic indicators, political and social ones play a key role in development and frontier technical efficiency profiles. The MENA have been...

This paper considers Bayesian regression with normal and doubleexponential priors as forecasting methods based on large panels of time series. We show that, empirically, these forecasts are highly correlated with principal component forecasts and that they perform equally well for a wide range...

This paper shows that Vector Autoregression with Bayesian shrinkage is an appropriate tool for large dynamic models. We build on the results by De Mol, Giannone, and Reichlin (2008) and show that, when the degree of shrinkage is set in relation to the cross-sectional dimension, the forecasting...

This paper describes an algorithm to compute the distribution of conditional forecasts, i.e. projections of a set of variables of interest on future paths of some other variables, in dynamic systems. The algorithm is based on Kalman filtering methods and is computationally viable for large...

This paper shows that Vector Autoregression with Bayesian shrinkage is an appropriate tool for large dynamic models. We build on the results by De Mol, Giannone, and Reichlin (2008) and show that, when the degree of shrinkage is set in relation to the cross-sectional dimension, the forecasting...

This paper describes an algorithm to compute the distribution of conditional forecasts, i.e. projections of a set of variables of interest on future paths of some other variables, in dynamic systems. The algorithm is based on Kalman filtering methods and is computationally viable for large...

This paper considers Bayesian regression with normal and double-exponential priors as forecasting methods based on large panels of time series. We show that, empirically, these forecasts are highly correlated with principal component forecasts and that they perform equally well for a wide range...

The Bayesian Estimation, Analysis and Regression toolbox (BEAR) is a comprehensive (Bayesian) (Panel) VAR toolbox for forecasting and policy analysis. BEAR is a MATLAB based toolbox which is easy for non-technical users to understand, augment and adapt. In particular, BEAR includes a...

The Bayesian Estimation, Analysis and Regression toolbox (BEAR) is a comprehensive (Bayesian) (Panel) VAR toolbox for forecasting and policy analysis. BEAR is a MATLAB based toolbox which is easy for non-technical users to understand, augment and adapt. In particular, BEAR includes a...