Ricerc@Sapienza

This research proposes a novel method for estimating Impulse Responses (IRFs) by doing Bayesian inference directly on the parameters of Vector Moving Average (VMA) representation of the large-scale VAR. Small scale VARs are plagued with omitted variable bias in residuals, hence the use of models with many variables, profiting from big data revolution. On the flip-side, estimating sheer number of model parameters and IRF coefficients is a daunting task. We aim at developing an automatic algorithm for eliciting priors on the IRF coefficients directly, improving the precision of estimates. Shrinkage of the parameter space is done using stochastic search variable selection (SSVS), a hierarchical prior dependent on a single hyperparameter, which is to be estimated from data. This allows for higher transparency and more objective shrinkage in the Bayesian context. Algorithm is to be tested on estimating responses from monetary policy disturbance in case of a foresight of policy actions by economic agents.