Theoretical and Empirical Contributions to Monetary Policy Analysis

This thesis collects three different contributions to monetary macroeconomics, covering both theoretical and empirical aspects. First chapter builds on the DSGE models of New Keynesian tradition, and studies monetary policy around a non efficient steady state. Using a two-stage approach developed by Levine, McAdam, and Pearlman (2007), I show that in the presence of backward looking firms, the central planner improves social welfare when it allows for a steady state rate of inflation marginally above zero. In the second chapter, I estimate a simple two-country DSGE model to study the behaviour of the Eastern European central banks, obtaining some innovative important results. First, a simple monetary policy rule mimicking an optimal rule together with the assumption about the existence of non-zero steady state rate of inflation deliver a significantly better to the data. Furthermore, the empirical hypothesis that central banks systematically target CPI inflation rather than PPI inflation is rejected for all the investigated Eastern European countries (EEC). In the third chapter, I use a Bayesian VAR with economically interpretable structural restrictions and zero restrictions on lags, to analyse the transmission channels of external shocks to an extended set of EEC. I study to what extent monetary policy shocks originating from the US and from Germany can explain fluctuations on Eastern European markets. To carry out the Bayesian inference, I use a Gibbs sampling approach. I find that the US monetary policy influences the EEC macroeconomic variables at least as much as its German counterpart

Benchmarking real-time monitoring strategies for ethanol production from lignocellulosic biomass

The goal of this paper is to review and critically assess different methods to monitor key process variables for ethanol production from lignocellulosic biomass. Because cellulose-based biofuels cannot yet compete with noncellulosic biofuels, process control and optimization are of importance to lower the production costs. This study reviews different monitoring schemes, to indicate what the added value of real-time monitoring is for process control. Furthermore, a comparison is made on different monitoring techniques to measure the off-gas, the concentrations of dissolved components in the inlet to the process, the concentrations of dissolved components in the reactor, and the biomass concentration. Finally, soft sensor techniques and available models are discussed, to give an overview of modeling techniques that analyze data, with the aim of coupling the soft sensor predictions to the control and optimization of cellulose to ethanol fermentation. The paper ends with a discussion of future needs and developmentsThis work was partially financed by the European Regional Development Fund (ERDF) and Region Zealand (Denmark) through the BIOPRO-SMV project. Furthermore, the work received funding from Innovation Fund Denmark (BIOPRO2 strategic research center, project number 4105-00020B). This project has also been supported partially by the EUDP project ‘Demonstration of 2G ethanol in full scale, MEC’ (Jr. no. 64015–0642). Finally, we wish to acknowledge the support obtained from the European Union's Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement number 713683 (COFUNDfellowsDTU) and from the Danish Council for Independent Research in the frame of the DFF FTP research project GREENLOGIC (grant agreement number 7017-00175A). Miguel Mauricio-Iglesias belongs to the Galician Competitive Research Group GRC2013-032 and the CRETUS strategic partnership (AGRUP2015/02), co-funded by FEDER (EU)S