This paper examines the feasibility of implementing Linear Quadratic Gaussian (LQG) Control in structural cointegrated VAR models and sheds some light on the two major problems generated by such implementation. The first aspect to be taken into account is the effect of the presence of unit roots...

We use a Vector Auto Regression (VAR) analysis to explore the (spill-over) effects of fiscal policy shocks in Europe. To enhance comparability with the existing literature, we first analyse the effects of these shocks at the national level. Here, we employ identification based on Choleski...

In the paper we propose a new methodological approach to core in- flation estimation, based on a frequency domain principal components estimator, suited to estimate systems of fractionally cointegrated processes. The proposed core inflation measure is the scaled common persistent factor in...

Equilibrium correction models of the price level are often used to model inflation. Such models assume that the long-run markup of prices over costs is fixed, but this may not be true for the Euro area economy, which has undergone major structural reforms over the last 25 years. We allow for...

The aim of this paper is twofold. First, for West Germany, France, Italy and US, we econometrically select within a SVAR model some fiscal policy regimes, i.e. a u0094set of rulesu0094 for the implementation of fiscal policies. Second, we identify the fiscal policy shocks related to different...

We propose a method for estimating a subset of the parameters of a structural rational expectations model by exploiting changes in policy. We define a class of models, midway between a vector autoregression and a structural model, that we call the recoverable structure. As an application of our...

This paper analyses the information content of M1 for euro area real GDP since the beginning of the 1980s and reviews theoretical arguments on why real narrow money should help predict real GDP. We find that, unlike in the U.S., in the euro area, M1 has better and more robust forecasting...