Kaikki aineistot
Lisää
Abstract It is known that the accuracy of the maximum likelihood-based covariance and precision matrix estimates can be improved by penalized log-likelihood estimation. In this article, we propose a ridge-type operator for the precision matrix estimation, ROPE for short, to maximize a penalized likelihood function where the Frobenius norm is used as the penalty function. We show that there is an explicit closed form representation of a shrinkage estimator for the precision matrix when using a penalized log-likelihood, which is analogous to ridge regression in a regression context. The performance of the proposed method is illustrated by a simulation study and real data applications. Computer code used in the example analyses as well as other supplementary materials for this article are available online.
We assess the performance of optimal Taylor-type interest rate rules, with and without reaction to financial variables, in stabilizing an economy following financial shocks. The analysis is conducted in a DSGE model with loan and bond markets, each featuring financial frictions. This allows for a wide set of financial shocks and transmission mechanisms and can be calibrated to match the bond-to-bank finance ratio featured in the US financial system. Overall, we find that monetary policy that reacts to credit growth, a form of the so-called “leaning against the wind”, improves the ability of the central bank to achieve its mandate in the wake of financial shocks. The specific policy implications depend partly on the origin and the persistence of the financial shock, but overall not on the assignment of a mandate for financial stability in the central bank’s objective function.