Opting for structural or reduced form estimation is often hard to justify if one wants to both learn about the structure of the economy and obtain accurate predictions. In this paper, we show that using both structural and reduced form estimates simultaneously can lead to more accurate policy...

Opting for structural or reduced form estimation is often hard to justify if one wants to both learn about the structure of the economy and obtain accurate predictions. In this paper, we show that using both structural and reduced form estimates simultaneously can lead to more accurate policy...

In this paper we analyze the determinants of export sophistication based on a large panel dataset (2001-2014; 101 countries) and using different estimation algorithms. Using Monte Carlo simulations we evaluate the bias properties of estimators and show that GMM-type estimators outperform...

In this paper, we use index number theory to decompose changes in total interest rate due to changes in the interest rate component and the weight component. We discuss the optimal calculation of a binary index using axiomatic index number theory. Based on this theory we compare alternative...

We evaluate the forecasting performance of four competing models for short-term macroeconomic forecasting: the traditional VAR, small scale Bayesian VAR, Factor Augmented VAR and Bayesian Factor Augmented VAR models. Using Armenian quarterly actual macroeconomic time series from 1996Q1 –...

We analyze the performance of a broad range of nowcasting and short-term forecasting models for a representative set of twelve old and six new member countries of the European Union (EU) that are characterized by substantial differences in aggregate output variability. In our analysis, we...

Two model averaging approaches are used and compared in estimating and forecasting dynamic factor models, the well-known Bayesian model averaging (BMA) and the recently developed weighted average least squares (WALS). Both methods propose to combine frequentist estimators using Bayesian weights....