Publications

Often, variables are linked to each other via a network. When such a network structure is known, this knowledge can be incorporated into regularized regression settings. In particular, an additional network penalty can be added on top of another penalty term, such as a Lasso penalty. However, when the type of interaction via the network is unknown (that is, whether connections are of an activating or a repressing type), the connection signs have to be estimated simultaneously with the covariate coefficients. This can be done with an algorithm iterating a connection sign estimation step and a covariate coefficient estimation step. We show detailed simulation results of such an algorithm. The algorithm performs well in a variety of settings. We also briefly describe the R-package that we developed for this purpose, which is publicly available.

The views expressed are those of the author(s) and do not necessarily represent those of the Bank of Lithuania.

In this paper we provide an overview of the different approaches identified to capture monetary policy in a period of Zero Lower Bound (ZLB). We focus here on the methods closely linked to interest rates, which include: spreads, synthetic indices from principal component analysis and different shadow rates.
In the second section of this review we calculate these measures for the euro area and also draw comparisons among different approaches and look at the effects on main macroeconomic variables, with a special focus on inflation. The impact of unconventional monetary policy shocks on inflation is found to be significantly positive by the majority of the studies and by using different methods.
Ultimately, we provide a summary of the literature on the Natural Real Rate of Interest, which may be useful for assessing how long low (real) interest rates in a ZLB may stay in place; also suggesting some possible improvement in the estimations which would lead to more accurate policy recommendations.

JEL Codes: E43, E52, E58, F42.

The views expressed are those of the author(s) and do not necessarily represent those of the Bank of Lithuania.