Technical Appendix to "Optimal Fiscal and Monetary Policy under Sectorial Heterogeneity"

This is the complete technical appendix to "Optimal Fiscal and Monetary Policy under Sectorial Heterogeneity".

Optimal Fiscal and Monetary Policy under Sectorial Heterogeneity

This paper characterizes optimal fiscal and monetary policy in a new keynesian model with sectorial heterogeneity in price stickiness. In particular, we (i) derive a purely quadratic welfare-based loss function from an approximation of the representative agent's utility function and (ii) provide the optimal target rule for fiscal and monetary policy. Differently from the homogeneous case, the loss function includes sectorial inflation variances instead of aggregate inflation, with weights proportional to the degree of price stickiness; and sectorial output gaps instead of aggregate output gap with equal weight in each sector. Optimal policy implies a very strong positive correlation among sectorial output gaps and some dispersion of sectorial inflation in response to shocks. Larger heterogeneity in price stickiness implies larger impact of shocks on aggregate inflation. Optimal taxes are more responsive in sectors with stickier prices.

Healthy, Wealthy, and Wise? Socioeconomic Status and Morbidity/Mortality

No abstract.

Toda a memória das imagens: o ensaio audiovisual

info:eu-repo/semantics/publishedVersio

Tax policy stance over the business cycle: evidence from Europe

A Work Project, presented as part of the requirements for the Award of a Masters Degree in Economics from the NOVA – School of Business and EconomicsWe contribute to the literature addressing the cyclical behavior of tax policy. Most recent studies have relied on tax revenues and adjusted measures of tax collections to analyze this issue. We argue that such methodology is insufficient to characterize tax policy cyclicality, as tax revenues move endogenously with the business cycle. Consequently, this topic is revisited by making use of the policy instrument, tax rate, as opposed to the policy outcome, tax revenues. Using data for 13 European countries, we find that tax policy has mostly been a-cyclical over the last 30 years

Implementation of X-ray reflectivity on the characterization of ultra-thin films for memory devices

In this work X-ray reflectivity (XRR) was implemented as a characterization technique to study very thin films produced at CENIMAT. Three groups of samples were analysed: thin films of Ta2O5, thin films Ta2O5 mixed with SiO2 (TaSiO) and thin films of zinc-tin oxide (ZTO). Spectroscopic ellipsometry was used as a complementary technique and all results were compared with the results of profilometry and Rutherford backscattering spectrometry (RBS). A good agreement was found between the thicknesses obtained by all techniques. XRR revealed to be a better technique than SE to determine the thickness of the native oxide of silicon. In the same way the determination of the roughness was more accurate with XRR. The objective of the characterization of the TaSiO and Ta2O5 samples was to study the influence of the radio frequency (rf) power in the Ta2O5 target during the sputtering process. In the case of the TaSiO samples an increase of the rf power resulted in an increase of the percentage of Ta2O5 and Ar in the films, as well as an increase of the density and dielectric constant. On the other side, the short-range order decreased. With the increase of the rf power the Ta2O5 samples only revealed a lower short-range order. The aim of the characterization of the ZTO was to study of the effect of rf power in the ZTO target during the sputtering process, as well as the influence of the H2 and O2 flows. The increase of the rf power resulted in denser films with lower short-range order. In a less clear way a change in the H2 influenced the films thickness. The increase of theO2 flow had as a result films less dense and less rough

Enhancing the efficiency of electricity utilization through home energy management systems within the smart grid framework

The concept behind smart grids is the aggregation of “intelligence” into the grid, whether through communication systems technologies that allow broadcast/data reception in real-time, or through monitoring and systems control in an autonomous way. With respect to the technological advancements, in recent years there has been a significant increment in devices and new strategies for the implementation of smart buildings/homes, due to the growing awareness of society in relation to environmental concerns and higher energy costs, so that energy efficiency improvements can provide real gains within modern society. In this perspective, the end-users are seen as active players with the ability to manage their energy resources, for example, microproduction units, domestic loads, electric vehicles and their participation in demand response events. This thesis is focused on identifying application areas where such technologies could bring benefits for their applicability, such as the case of wireless networks, considering the positive and negative points of each protocol available in the market. Moreover, this thesis provides an evaluation of dynamic prices of electricity and peak power, using as an example a system with electric vehicles and energy storage, supported by mixed-integer linear programming, within residential energy management. This thesis will also develop a power measuring prototype designed to process and determine the main electrical measurements and quantify the electrical load connected to a low voltage alternating current system. Finally, two cases studies are proposed regarding the application of model predictive control and thermal regulation for domestic applications with cooling requirements, allowing to minimize energy consumption, considering the restrictions of demand, load and acclimatization in the system

Presenting Business Insights on Advanced Pricing Agreements Using a Business Intelligence Framework

Project Work presented as the partial requirement for obtaining a Master's degree in Information Management, specialization in Knowledge Management and Business IntelligenceIn companies that use advanced pricing agreements, pricing managers are responsible for setting the new and adjusted discounts from time to time. These companies are usually of great dimension and so the number of products and customers is extensive, which causes the decision-making to be challenging for the pricing managers. To aid in this process, this project report incorporates a business intelligence framework to model the data into a dimensional model that will provide the pricing managers with business insights by allowing them to have a more targeted and detailed view of the data through multiple contextual perspectives. The data sources used were provided by a client at BI4ALL and consist of two different JDE extracts: an export of the advanced pricing agreements that include all the pricing rules and an export of the sales data following those pricing rules. Both sources of data will be used to implement a business intelligence framework. The final outcome of this project report is presented in a dashboard with multiple visualizations, where the pricing manager can navigate and obtain data in a dynamic way according to the information requested. This will allow for a better analysis, and thus, for better pricing adjustment and optimization