This paper addresses the estimation of Phillips curve equations for the euro area while employing less stringent assumptions on the functional correspondence between price inflation, inflation expectations, and marginal costs. Expectations are not assumed to be an unbiased predictor of actual...

This paper addresses the estimation of Phillips curve equations for the euro area while employing less stringent assumptions on the functional correspondence between price inflation, inflation expectations, and marginal costs. Expectations are not assumed to be an unbiased predictor of actual...

In this paper we compare alternative approaches for the construction of time series of macroeconomic variables for Unified Germany prior to 1991, and then use them for the construction of corresponding time series for the euro area. The resulting series for Germany and the euro area are compared...

The accession of ten countries into the European Union makes the forecasting of their key macroeconomic indicators an exercise of some importance. Because of the transition period, only short spans of reliable time series are available, suggesting the adoption of simple time series models as...

This paper illustrates how to handle a sequence of extreme observations-such as those recorded during the COVID-19 pandemic-when estimating a Vector Autoregression, which is the most popular time-series model in macroeconomics. Our results show that the ad-hoc strategy of dropping these...

Temporal aggregation in general introduces a moving average (MA) component in the aggregated model. A similar feature emerges when not all but only a few variables are aggregated, which generates a mixed frequency model. The MA component is generally neglected, likely to preserve the possibility...

We propose a class of prior distributions that discipline the long-run behavior of Vector Autoregressions (VARs). These priors can be naturally elicited using economic theory, which provides guidance on the joint dynamics of macroeconomic time series in the long run. Our priors for the long run...