We study the forecasting performance of three alternative large data forecasting approaches. These three approaches handle the dimensionality problem evoked by a large dataset by aggregating its informational content, yet on different levels. We...

We study the forecasting performance of three alternative large data forecasting approaches. These three approaches handle the dimensionality problem ev

Due to their well-known indeterminacies, factor models require identifying assumptions to guarantee unique parameter estimates. For Bayesian estimation, these identifying assumptions are usually implemented by imposing constraints on certain model...

Due to their indeterminacies, static and dynamic factor models require identifying assumptions to guarantee uniqueness of the parameter estimator. The indeterminacy of the parameter estimator with respect to an orthogonal transformation is known as...

We use the Factor-Augmented Vector Autoregression (FAVAR) approach of Bernanke, Boivin and Eliasz (2005) to estimate the effects of monetary policy shocks on wages and employment in the euro area. The use of a large data set comprising country,...

Due to their indeterminacies, static and dynamic factor models require identifying assumptions to guarantee uniqueness of the parameter estimates. The indeterminacy of the parameter estimates with respect to orthogonal transformations is known as the...