Firms and aggregate dynamics

We investigate the role of permanent and transitory shocks for firms and aggregate dynamics. We find that permanent shocks to productivity and permanent shifts in the composition of output explain at least four-fifths of firms’ dynamics. However, these permanent shocks are almost uncorrelated across firms and are therefore less relevant for aggregate dynamics. Transitory shocks, on the other hand, are not very important at the firm level,but they account for most of the volatility of aggregate hours and output, because they are significantly correlated across firms. Finally, we try to make some progress on the interpretation of the shocks.

DYNAMOD – A dynamic agent based modelling framework for digital businesses

Digital Businesses have become a major driver for economic growth and have seen an explosion of new startups. At the same time, it also includes mature enterprises that have become global giants in a relatively short period of time. Digital Businesses have unique characteristics that make the running and management of a Digital Business much different from traditional offline businesses. Digital businesses respond to online users who are highly interconnected and networked. This enables a rapid flow of word of mouth, at a pace far greater than ever envisioned when dealing with traditional products and services. The relatively low cost of incremental user addition has led to a variety of innovation in pricing of digital products, including various forms of free and freemium pricing models. This thesis explores the unique characteristics and complexities of Digital Businesses and its implications on the design of Digital Business Models and Revenue Models. The thesis proposes an Agent Based Modeling Framework that can be used to develop Simulation Models that simulate the complex dynamics of Digital Businesses and the user interactions between users of a digital product. Such Simulation models can be used for a variety of purposes such as simple forecasting, analysing the impact of market disturbances, analysing the impact of changes in pricing models and optimising the pricing for maximum revenue generation or a balance between growth in usage and revenue generation. These models can be developed for a mature enterprise with a large historical record of user growth rate as well as for early stage enterprises without much historical data. Through three case studies, the thesis demonstrates the applicability of the Framework and its potential applications.

Modeling and forecasting value-at-risk for the Portuguese stock market

The aim of this work project is to find a model that is able to accurately forecast the daily Value-at-Risk for PSI-20 Index, independently of the market conditions, in order to expand empirical literature for the Portuguese stock market. Hence, two subsamples, representing more and less volatile periods, were modeled through unconditional and conditional volatility models (because it is what drives returns). All models were evaluated through Kupiec’s and Christoffersen’s tests, by comparing forecasts with actual results. Using an out-of-sample of 204 observations, it was found that a GARCH(1,1) is an accurate model for our purposes.

The impact of talent management practices on employee retention: The moderator effect of leader-member exchange and role modeling

Organizations are undergoing serious difficulties to retain talent. Authors argue that Talent Management (TM) practices create beneficial outcomes for individuals and organizations. However, there is no research on the leaders’ role in the functioning of these practices. This study examines how LMX and role modeling influence the impact that TM practices have on employees’ trust in their organizations and retention. The analysis of two questionnaires (Nt1=175; Nt2=107) indicated that TM only reduced turnover intentions, via an increase in trust in the organization, when role modeling was high and not when it was low. Therefore, we can say that leaders are crucial in the TM context, and in sustaining a competitive advantage for organizations.

Evolutionary dynamics of Chlamydia trachomatis genome and identification of molecular patterns of hypothetical protein coding genes

The obligate intracellular bacterium Chlamydia trachomatis is a human pathogen of major public health significance. Strains can be classified into 15 main serovars (A to L3) that preferentially cause ocular infections (A-C), genital infections (D-K) or lymphogranuloma venereum (LGV) (L1-L3), but the molecular basis behind their distinct tropism, ecological success and pathogenicity is not welldefined. Most chlamydial research demands culture in eukaryotic cell lines, but it is not known if stains become laboratory adapted. By essentially using genomics and transcriptomics, we aimed to investigate the evolutionary patterns underlying the adaptation of C. trachomatis to the different human tissues, given emphasis to the identification of molecular patterns of genes encoding hypothetical proteins, and to understand the adaptive process behind the C. trachomatis in vivo to in vitro transition. Our results highlight a positive selection-driven evolution of C. trachomatis towards nichespecific adaptation, essentially targeting host-interacting proteins, namely effectors and inclusion membrane proteins, where some of them also displayed niche-specific expression patterns. We also identified potential "ocular-specific" pseudogenes, and pointed out the major gene targets of adaptive mutations associated with LGV infections. We further observed that the in vivo-derived genetic makeup of C. trachomatis is not significantly compromised by its long-term laboratory propagation. In opposition, its introduction in vitro has the potential to affect the phenotype, likely yielding virulence attenuation. In fact, we observed a "genital-specific" rampant inactivation of the virulence gene CT135, which may impact the interpretation of data derived from studies requiring culture. Globally, the findings presented in this Ph.D. thesis contribute for the understanding of C.trachomatis adaptive evolution and provides new insights into the biological role of C. trachomatishypothetical proteins. They also launch research questions for future functional studies aiming toclarify the determinants of tissue tropism, virulence or pathogenic dissimilarities among C. trachomatisstrains.

US fiscal dynamics in political perspective

Given the importance of fiscal balance for ensuring a sustainable fiscal policy, we conduct an empirical examination of fiscal dynamics in the United States in response to unsustainable budget deviations. We concentrate on the role of political factors, namely the Republican - Democrat presidential divide, in determining the fiscal response to budget disequilibria. Making use of an asymmetric cointegration framework, we explore politically motivated fiscal asymmetries in the US, from Eisenhower to Obama. We conclude that political factors such as the government’s political quadrant and the timing of elections are important determinants of the fiscal response to unsustainable budget deviations.

Fiscal dynamics and electoral behavior in the Eurozone

Abstract: The Stability Growth Pact and the 3% rule did not prevent countries from running large deficits. Countries in the EMU administrate fiscal policies differently, despite the existence of a common quantitative goal. The main focus of this work project is to study differences in the fiscal dynamics of eight EMU countries and assess the role of political variables in shaping those dynamics. We find that elections negatively affect government revenue in Austria, Belgium, Portugal, Spain and Germany. Expenditure, on the other hand, responds positively to incoming elections in Portugal, Italy, France and Netherlands, and negatively in the case of Germany.

Modeling and optimization of extracellular polysaccharides production by Enterobacter A47

Polysaccharides are gaining increasing attention as potential environmental friendly and sustainable building blocks in many fields of the (bio)chemical industry. The microbial production of polysaccharides is envisioned as a promising path, since higher biomass growth rates are possible and therefore higher productivities may be achieved compared to vegetable or animal polysaccharides sources. This Ph.D. thesis focuses on the modeling and optimization of a particular microbial polysaccharide, namely the production of extracellular polysaccharides (EPS) by the bacterial strain Enterobacter A47. Enterobacter A47 was found to be a metabolically versatile organism in terms of its adaptability to complex media, notably capable of achieving high growth rates in media containing glycerol byproduct from the biodiesel industry. However, the industrial implementation of this production process is still hampered due to a largely unoptimized process. Kinetic rates from the bioreactor operation are heavily dependent on operational parameters such as temperature, pH, stirring and aeration rate. The increase of culture broth viscosity is a common feature of this culture and has a major impact on the overall performance. This fact complicates the mathematical modeling of the process, limiting the possibility to understand, control and optimize productivity. In order to tackle this difficulty, data-driven mathematical methodologies such as Artificial Neural Networks can be employed to incorporate additional process data to complement the known mathematical description of the fermentation kinetics. In this Ph.D. thesis, we have adopted such an hybrid modeling framework that enabled the incorporation of temperature, pH and viscosity effects on the fermentation kinetics in order to improve the dynamical modeling and optimization of the process. A model-based optimization method was implemented that enabled to design bioreactor optimal control strategies in the sense of EPS productivity maximization. It is also critical to understand EPS synthesis at the level of the bacterial metabolism, since the production of EPS is a tightly regulated process. Methods of pathway analysis provide a means to unravel the fundamental pathways and their controls in bioprocesses. In the present Ph.D. thesis, a novel methodology called Principal Elementary Mode Analysis (PEMA) was developed and implemented that enabled to identify which cellular fluxes are activated under different conditions of temperature and pH. It is shown that differences in these two parameters affect the chemical composition of EPS, hence they are critical for the regulation of the product synthesis. In future studies, the knowledge provided by PEMA could foster the development of metabolically meaningful control strategies that target the EPS sugar content and oder product quality parameters.

Dynamics of serotonergic neurons revealed by fiber photometry

This work was developed in the context of the MIT Portugal Program, area of Bioengineering Systems, in collaboration with the Champalimaud Research Programme, Champalimaud Center for the Unknown, Lisbon, Portugal. The project entitled Dynamics of serotonergic neurons revealed by fiber photometry was carried out at Instituto Gulbenkian de Ciência, Oeiras, Portugal and at the Champalimaud Research Programme, Champalimaud Center for the Unknown, Lisbon, Portugal

A minimal version of a Protein Tyrosine Phosphatase

Phosphatase and tensin homologue (PTEN) protein belongs to the family of protein tyrosine phos-phatase. Mutations on the phosphatase and tensin homologue (PTEN) protein are highly observed in diverse types of human tumors, being mostly identified on the phosphatase domain of the protein. Although PTEN is a modular protein composed by a phosphatase domain and a C2 domain for mem-brane anchoring, this work aimed at developing a minimal version of PTEN´s phosphatase domain. The minimal version (Small Domain) comprises a 28 residue peptide, with the PTEN 8-mer catalytic peptide accommodated between a α-helix and β-turn as observed in PTEN native structure. Firstly, a de novo prediction of the Small Domain´s secondary structure was carried out by molecular modeling tools. The stability of the predicted structures were then evaluated by Molecular Dynamics. Automated molecular docking of PTEN natural substrate PIP3, its analogue (Inositol) and a PTEN inhibitor (L-tar-tare) were performed with the modeled structure, and PTEN used as a positive control. The gene en-coding for Small Domain was designed and cloned into an expression vector at N-terminal of Green Fluorescence Protein (GFP) encoding gene. The fusion protein was then expressed in Escherichia coli cells. Different expression conditions have been explored for the production of the fusion protein to minimize the formation of inclusion bodies.