Large Datasets, Small Models and Monetary Policy in Europe

Nowadays a considerable amount of information on the behavior of the economy is readily available, in the form of large datasets of macroeconomic variables. Central bankers can be expected to base their decisions on this very large information set, so that it can be difficult to track their decisions using small models, such as standard Taylor rules. Small scale structural VARs can suffer from a similar problem when used to highlight stylized facts or for policy simulation exercises. On the other hand, large scale structural models are hardly manageable, and still suffer from those identification problems that led to the success of VARs. In this paper we combine recent time-series techniques for the analysis of large datasets with more traditional small scale models to analyze monetary policy in Europe. In particular, we model hundreds of macroeconomic variables with a dynamic factor model, and summarize their informational content with a few estimated factors. These factors are then used as instruments in the estimation of forward looking Taylor rules, and as additional regressors in structural VARs. The latter are then used to evaluate the effects of unexpected and systematic monetary policy.