This paper proposes a new method of forecasting euro area quarterly real GDP that uses areawide indicators, which are derived by optimally pooling the information contained in national indicator series. Following the ideas of predictive modeling, we construct the area-wide indicators by...

The use of large datasets for macroeconomic forecasting has received a great deal of interest recently. Boosting is one possible method of using high-dimensional data for this purpose. It is a stage-wise additive modelling procedure, which, in a linear specification, becomes a variable selection...

Over the last decade, the topic of regional economic forecasting has become increasingly prevalent in academic literature. The most striking problem in this context is data availability at a regional level. However, considerable methodological improvements have been made to address this problem....

Recent articles suggest that a Bayesian vector autoregression (BVAR) with shrinkage is a good forecast device even when the number of variables is large. In this paper we evaluate different variants of the BVAR with respect to their forecast accuracy for euro area real GDP growth and HICP...

Critics of international student comparisons argue that results may be influenced by differences in the extent to which countries adequately sample their entire student populations. In this research note, we show that larger exclusion and non-response rates are related to better country average...

We derive monthly and quarterly series of UK GDP for the inter-war period from a set of monthly indicators that were constructed by The Economist at the time. The monthly information is complemented with data for quarterly industrial production, allowing us to employ mixed-frequency methods to...

Most evidence for the resource curse comes from cross-country growth regressions suffers from a bias originating from the high and ever-evolving volatility in commodity prices. This paper addresses these issues by providing new cross-country empirical evidence for the effect of resources in...

In this paper we specify a linear Cliff and Ord-type spatial model. The model allows for spatial lags in the dependent variable, the exogenous variables, and disturbances. The innovations in the disturbance process are assumed to be heteroskedastic with an unknown form. We formulate a multi-step...

This paper proposes a method to implement maximum likelihood estimation of the dynamic panel data type 2 and 3 tobit models. The likelihood function involves a two-dimensional indefinite integral evaluated using "two-step" Gauss-Hermite quadrature. A Monte Carlo study shows that the quadrature...