This paper shows that Vector Autoregression with Bayesian shrinkage is an appropriate tool for large dynamic models. We build on the results by De Mol, Giannone, and Reichlin (2008) and show that, when the degree of shrinkage is set in relation to the cross-sectional dimension, the forecasting...

This paper considers Bayesian regression with normal and doubleexponential priors as forecasting methods based on large panels of time series. We show that, empirically, these forecasts are highly correlated with principal component forecasts and that they perform equally well for a wide range...

This paper considers quasi-maximum likelihood estimations of a dynamic approximate factor model when the panel of time series is large. Maximum likelihood is analyzed under different sources of misspecification: omitted serial correlation of the observations and cross-sectional correlation of...

This paper uses a data-set including time series data on macroeconomic variables, loans, deposits and interest rates for the euro area in order to study the features of financial intermediation over the business cycle. We find that stylized facts for aggregate monetary and real variables are re-...

This paper shows consistency of a two-step estimation of the factors in a dynamic approximate factor model when the panel of time series is large (n large). In the first step, the parameters of the model are estimated from an OLS on principal components. In the second step, the factors are...

This paper analyses the impact on the macroeconomy of the ECB’s non-standard monetary policy implemented in the aftermath of the collapse of Lehman Brothers in the Fall of 2008. We study in particular the effect of the expansion of the intermediation of transactions across central bank balance...