Paper
The role of structural changes in the labor market for monetary policy
The first paper examines the role of labor market institutions (union density, benefit replacement rates and employment protection legislation) as a structural element in shaping the transmission mechanism of monetary policy. Our theoretical framework posits that higher union density flattens the Phillips curve, thereby magnifying the effect on output while attenuating the impact of monetary policy shocks on inflation. This is empirically confirmed by means of an interacted panel vector autoregressive (VAR) model estimated for euro area countries. Conversely, benefit replacement rates and employment protection legislation exhibit limited influence. Our findings identify a structural rather than a cyclical phenomenon for shaping monetary policy effectiveness. They underscore the importance of labor market characteristics in this context, particularly within a monetary union where heterogeneous labor markets can lead to inefficient inflation and output differentials.
Bayesian modelling of VAR precision matrices using stochastic block networks
Commonly used priors for Vector Autoregressions (VARs) induce shrinkage on the autoregressive coefficients. Introducing shrinkage on the error covariance matrix is sometimes done but, in the vast majority of cases, without considering the network structure of the shocks and by placing the prior on the lower Cholesky factor of the precision matrix. In this paper, we propose a prior on the VAR error precision matrix directly. Our prior, which resembles a standard spike and slab prior, models variable inclusion probabilities through a stochastic block model that clusters shocks into groups. Within groups, the probability of having relations across group members is higher (inducing less sparsity) whereas relations across groups imply a lower probability that members of each group are conditionally related. We show in simulations that our approach recovers the true network structure well. Using a US macroeconomic data set, we illustrate how our approach can be used to cluster shocks together and that this feature leads to improved density forecasts.