APOBEC3 (A3) proteins are a family of host-encoded cytidine deaminases that protect against retroviruses and other viral intruders. Retroviruses, unlike other viruses, are able to integrate their genomic proviral DNA within hours of entering host cells. A3 proteins hinder retroviral infectivity by editing retroviral replication intermediates, as well as by inhibiting retroviral replication and integration through deamination-independent methods. These proteins thus constitute the first line of immune defense against endogenous and exogenous retroviral pathogens. The overall goal of my Master's project was to better understand the critical role A3 proteins play in restricting inter- and intra-host transmission of retroviruses. There are two specific aspects that I focused on: first, investigating the role of mouse APOBEC3 (mA3) in limiting the zoonotic transmission of murine leukemia retroviruses (MLVs) in a rural environment; second, to identify the molecular features in MLVs that confer susceptibility or resistance to deamination by mA3. For the first part of my project, we collected blood samples from dairy and production cattle from four different geographical locations across Canada. We then designed a novel PCR screening strategy targeting conserved genetic regions in MLVs and Mouse Mammary Tumor Virus (MMTV) and MMTV-like betaretroviruses. Our results indicate that 4% of animals were positive for MLV and 2% were positive for MMTV. Despite crossing the species barrier by gaining entry into bovine cells, our study also demonstrates that the bovine A3 protein is able to potently inhibit the spread of these murine retroviruses in vitro. The next question we asked was whether mA3 could also mutate and restrict murine endogenous retroviruses and thereby partake in limiting zoonotic transmission. Moloney MLV and AKV MLV are two highly homologous murine gammaretroviruses with opposite sensitivities to restriction by mA3: MoMLV is resistant to restriction and deamination while AKV is sensitive to both. Design of MoMLV/AKV hybrid viruses enabled us to map the region of mA3 resistance to the region encoding the glyco-Gag accessory protein. Site-directed mutagenesis then allowed us to correlate the number of N-linked glycosylation sites with the level of resistance to deamination by mA3. Our results suggest that Gag glycosylation is a possible viral defence mechanism that arose to counteract the evolutionary pressure imposed by mA3. Overall, my projects show the important role A3 proteins play in intrinsic immunity, whether defending the host from foreign retroviral invaders or endogenous retroviral foes.

Värme står för det största energibehovet inom hushåll och andra byggnader i samhället och olika tekniker används för att kunna reducera mängden energi som går åt för att spara på både miljö och pengar. Ett angreppssätt på detta problem är genom informatiken, där maskininlärning kan användas för att analysera och förutspå värmebehovet. I denna studie används maskininlärning för att prognostisera framtida energiförbrukning för fjärrvärme utifrån historisk fjärrvärmedata från ett fjärrvärmebolag tillsammans med exogena variabler i form av väderdata från Sveriges meteorologiska och hydrologiska institut. Studien är skriven på svenska och utforskar effekter av preprocessering hos prediktionsmodeller som använder tidsseriedata för att prognostisera framtida datapunkter. Stegen som utförs i studien är normalisering, interpolering, hantering av numeric outliers och missing values, datetime feature engineering, säsongsmässighet, feature selection, samt korsvalidering. Maskininlärningsmodellen som används i studien är Multilayer Perceptron som är en subkategori av artificiellt neuralt nätverk. Forskningsfrågan som besvaras fokuserar på effekter av preprocessering och feature selection för prediktiva modellers prestanda inom olika datamängder och kombinationer av preprocesseringsmetoder. Modellerna delades upp i tre olika datamängder utifrån datumintervall: 2009, 2007–2011, samt 2007–2017, där de olika kombinationerna utgörs av preprocesseringssteg som kombineras inom en iterativ process. Procentuella ökningar på R2-värden för dessa olika intervall har uppnått 47,45% för ett år, 9,97% för fem år och 32,44% för 11 år. I stora drag bekräftar och förstärker resultatet befintlig teori som menar på att preprocessering kan förbättra prediktionsmodeller. Ett antal mindre observationer kring enskilda preprocesseringsmetoders effekter har identifierats och diskuterats i studien, såsom DateTime Feature Engineerings negativa effekter på modeller som tränats med ett mindre antal iterationer. / Heat accounts for the greatest energy needs in households and other buildings in society. Effective production and distribution of heat energy require techniques for minimising economic and environmental costs. One approach to this problem is through informatics where machine learning is used to analyze and predict the heating needs with the help of historical data from a district heating company and exogenous variables in the form of weather data from Sweden's Meteorological and Hydrological Institute (SMHI). This study is written in Swedish and explores the importance of preprocessing practices before training and using prediction models which utilizes time-series data to predict future energy consumption. The preprocessing steps explored in this study consists of normalization, interpolation, identification and management of numerical outliers and missing values, datetime feature engineering, seasonality, feature selection and cross-validation. The machine learning model used in this study is Multilayer Perceptron which is a subcategory of artificial neural network. The research question focuses on the effects of preprocessing and feature selection for predictive model performance within different datasets and combinations of preprocessing methods. The models were divided into three different data sets based on date ranges: 2009, 2007–2011, and 2007–2017, where the different combinations consist of preprocessing steps that are combined within an iterative process. Percentage increases in R2 values for these different ranges have reached 47,45% for one year, 9,97% for five years and 32,44% for 11 years. The results broadly confirm and reinforce the existing theory that preprocessing can improve prediction models. A few minor observations about the effects of individual preprocessing methods have been identified and discussed in the study, such as DateTime Feature Engineering having a detrimental effect on models with very few training iterations.

No description available.

Le monde est actuellement confronté à une transition du marché de l'énergie qui est influencée notamment par la dynamique de la croissance économique globale, les négociations relatives aux changements climatiques et des prix de plus en plus volatils. Cette évolution rapide des réglementations et de la macro-économie transformera les conditions de la demande d'énergie, obligeant les gouvernements à acquérir un ensemble croissant d'outils quantitatifs pour mieux évaluer les résultats de leurs politiques fiscales. Cette thèse aborde cette problématique en analysant, par une approche basée sur les élasticités, les différentes facettes de la demande d'énergie dans le but d'achever une consommation énergétique durable. Cette approche est complémentée par l'analyse par grappes, la décomposition structurelle ainsi que par diverses outils économétriques appliques conjointement à l'échelle mondiale et nationale. Une attention particulière est faite sur la modélisation de la demande des marchés subsidiés notamment des pays du Conseil de Coopération du Golfe Arabique / The world is currently witnessing a transition in the energy scene that is significantly characterized by global economic growth dynamics, climate change negotiations and volatile energy prices. Rapidly evolving regulatory and macro-economic environments heavily impact on the demand-side of energy, forcing governments to acquire an ever-increasing set of quantitative tools to better assess the results of their taxation policies.This thesis addresses some of these issues by analyzing various facets of energy demand in order to generate sensible demand and price elasticities with real-life applications in sustainable energy management. For that purpose, a combination of cluster, decomposition and multiple econometric analysis is undertaken at global, regional and country-specific levels for households complemented by a policy analysis. A special focus is made on modeling consumer demand behavior for resource-rich economies of the Gulf Cooperation Countries, and the potential impact of removing residential electricity subsidies on the net societal welfare of Saudi Arabia.

Der tatsächliche Erfolg einer Immobilieninvestition wird maßgeblich von der zukünftigen Entwicklung des wirtschaftlichen Umfeldes bestimmt. Im Rahmen einer Immobilieninvestition sind für Investoren z. B. die zukünftigen Mieteinnahmen oder die allgemeine Wertentwicklung der Immobilie entscheidend. Da jedoch Entscheidungen in der Immobilienwirtschaft langfristiger Natur sind, kommt der Zukunftsorientiertheit und des damit verbundenen Risikos eine große Bedeutung zu. Die Entstehung von Immobilienzyklen kann nicht nur mit realen und monetären Fundamentaldaten (z. B. Bruttoinlandsprodukt, Zinsentwicklung) erklärt werden, sondern auch mit psychologischen Faktoren, wie beispielsweise Erwartungen und Einstellungen der Marktteilnehmer. Da mathematisch-statistische Prognoseverfahren diese Komponente nur unzureichend abbilden können, soll die Dissertation einen Beitrag zur Erforschung der Expertenbefragung als Prognoseinstrument darstellen. Ein weiterer Grund besteht darin, dass in der bisher veröffentlichten Fachliteratur der Expertenbefragung als Prognoseverfahren nur eine geringe oder gar keine Bedeutung beigemessen wurde. Ziel ist es herauszustellen, ob und unter welchen Voraussetzungen und Bedingungen Expertenbefragungen zur Prognose von Immobilienwerten geeignet sind und ob die Kombination der Ergebnisse der Expertenbefragungen mit den Ergebnissen mathematisch-statistischer Prognoseverfahren eine Erhöhung der Prognosegenauigkeit ermöglicht. Hierzu wird die zukünftige Entwicklung verschiedener Immobilienwerte für 2 bis 3 Jahre und für 5 Jahre durch Expertenumfragen und mit Hilfe ausgewählter mathematisch-statistischer Prognoseverfahren prognostiziert. Um die Güte der Expertenschätzungen beurteilen zu können, werden die Prognoseergebnisse mit der tatsächlichen Entwicklung und mit den Ergebnissen der mathematisch-statistischen Prognoseverfahren verglichen. In einer abschließenden Gegenüberstellung sollen Aussagen darüber getroffen werden, ob Expertenbefragungen für Prognosezwecke geeignet sind. Ein besonderer Schwerpunkt liegt dabei auch auf psychologischen Aspekten bzw. endogenen und exogenen Einflussgrößen, welche sich auf das Antwortverhalten der Experten auswirken können. Ziel ist es deshalb weiterhin, eine Handlungsempfehlung für die Durchführung von Expertenbefragungen - speziell für die Abgabe von mehrjährigen Trends und auch für Zwecke der Verkehrswertermittlung - zu geben.

Ghana has experienced a remarkable degradation and depletion of its forest resources over the last 100 years. This process has undermined the socio-economic and socio-cultural importance of the forests for millions of rural people who depend on the resource to support their livelihood. Many rural households have over the past three decades developed strategies to minimize the effects of forest depletion on their livelihood. The establishment of smallholder forest plantation on agricultural land has emerged as an important form of land-use for households to diversify their sources of income and also improve their socio-economic well-being. The main objective of the study was to identify and analyze the endogenous and exogenous factors inducing farm household’s decision to establish farm forest plantation and to analyze its financial contribution to household’s income and livelihood strategies. The study involved a survey of 280 randomly selected farm households from five communities in the Offinso district in Ghana. The multi-stage stratified random sampling technique was used to select as many as 165 households with farm forest plantation as well as 115 without farm forest plantation. A mixture of tools including semi-structured questionnaire, focus group discussions, wealth ranking, forest inventory and market surveys were used to collect the required data. Results from logistic regression analysis revealed that the age of the household head, the number of years of education of the household head, the amount of household labor, the size of household landholding, the ownership of permanent land, the availability of non-agricultural land and household’s participation in past forest plantation development projects are the most important endogenous factors influencing the farm household’s decision to establish farm forest plantation. On the other hand, exogenous factors such as the availability of market and buyers for farm forest products and farm household’s satisfaction with market prices for farm forest products positively influenced the household’s decision to establish farm forest plantation. Prohibitive rules and regulations relating to the harvesting of trees and transportation of timber from private lands and uncertainty in tree tenure as a result of ambiguous policy framework, however, negatively influenced the decision to establish smallholder forest plantation on their agricultural land. The results from household income portfolio analysis show that cash income from selling farm forest products contributed an average of $273.6 to total household’s income in one agricultural season. This amount accounted for 17.6% of total household’s income and represented the second most important source of income after agriculture. The profitability of different land-uses practiced by the households was analyzed using a conventional economic method (Net Present Value). The results from a comparative financial analysis show that the establishment of teak plantation on agricultural land inter-cropped with food crops is the most profitable form of land use for the households compared to pure teak plantation and maize-plantain cultivation. The results of the study underscore the potential contribution of smallholder farm forest plantation to increase the overall household’s income and thereby improve household’s well-being. / Ghana hat während der letzten 100 Jahre eine bemerkenswerte Degradation und Verminderung seines Waldvorkommens erlebt. Dieser Prozess hat die sozio-ökonomische und sozial-kulturelle Bedeutung des Waldes als Einkommensquelle zur Unterstützung des Lebensunterhalts für Millionen ländlicher Einwohner geschwächt. Während der letzen 30 Jahre haben viele Kleinbauern Haushalte Strategien entwickelt um den Effekt, den die Verminderung des Waldvorkommens auf ihren Lebensunterhalt hat, zu minimieren. Die Anlage kleinflächiger Forstplantagen auf Ackerland hat sich dabei als wichtige Form der Landnutzung erwiesen, da sie eine Einkommensquelle zusätzlich zu den vorhandenen bedeuten. Sie haben das Potential, die sozio-ökonomiche Situation der Bevölkerung zu verbessern. Ziel der Studie war die Identifizierung von internen und externen Faktoren, die bedeutend zur Entscheidung von Haushalten über die Errichtung kleinflächiger Forstplantagen beitragen. Desweiteren sollten der finanzielle Beitrag der Forstplantagen zum Einkommen und zu Strategien der Kleinbauern analysiert werden. Für die Sudie werden Datensätze von 280 zufällig ausgewählten landwirtschaftlichen Haushalten aus fünf Gemeinden im Offinso Distrikt in Ghana erfasst. Die mehrstufig aufgebaute zufällige Auswahltechnik wurde benutzt, um die 165 Haushalte mit Forstplantagen und 115 Haushalten ohne Forstplantagen für die Studie auszuwählen. Mehrere Instrumente, wurden genutzt um die benötigten Daten zu sammeln darunter vor allem semi-strukturierte Befragungen, fokusierte Gruppendiskussionen, Wohlstandsranking der Haushalte und eine Forstinventur. Ergebnisse einer logistischen Regressionsanalyse ergaben, dass das Alter des Haushaltsvorstands, die Anzahl der Ausbildungsjahre des Haushaltsvorstands, die Anzahl der im Haushalt vorhandenen Arbeitskräfte, die Größe des dem Haushalt zur Verfügung stehenden Ackerlandes, das Eigentum an Ackerland, verfügbare nicht-landwirtschaftlich nutzbare Flächen und die Teilnahme der Haushalte an Projekten zur Forstplantagenentwicklung die wichtigsten internen Faktoren für die Entscheidung der Kleinbauern zur Errichtung von kleinflächiger Forstpantagen darstellen. Andererseits beeinflussten externe Faktoren wie das Vorhandensein von Markt und Käufern für Produkte der Forstplantagen und die Zufriedenheit der Haushalte mit den gebotenen Marktpreisen für diese Produkte die Entscheidung der Kleinbauern zur Errichtung von Forstplantagen positiv. Demgegenüber beeinflussten Verbote und einschränkende Regelungen zur Ernte und zum Transport von Bäumen auf Privatland und die Unsicherheit bezüglich des Eigentums an den Bäumen als Ergebniss unklarer politischer Vorgaben die Entscheidung zur Errichtung von kleinflächiger Forstplantagen auf Ackerland negativ. Das Ergebniss der Analyse verschiedene Haushaltseinkommensquellen zeigt, dass das Jahreseinkommen der Haushalte mit Forstplantagen höher ist als das der Haushalte ohne Forstplantagen. Der Beitrag zum Jahreseinkommen aus dem Verkauf von Produkten der kleinflächiger Forstplantagen betrug im Durchschnitt 273,6 USD in einer landwirtschaftlichen Saison. Dies entsprach 17,6 % des gesamten Haushaltseinkommens und stellte somit die zweitwichtigste Einkommensquelle nach der Landwirtschaft dar. Die Rentabilität der verschiedenen Landnutzungsarten wurde mit der Kapitalwertmethode (Net Present Value) ermittelt. Diese vergleichende Analyse zeigte, dass kleinflächiger Forstplantagen auf Agrarland bei gleichzeitigem Anbau von Nahrungsmittel die profitabelste Art der Landnutzung für die Haushalte im Vergeich zu ausschließlichem Teakanbau und zum Anbau von Mais mit Kochbanane ist. Die Ergebnisse der Studie unterstreichen das Potential kleinflächiger Forstplantagen, einen Beitrag zur Steigerung des gesamten Haushaltseinkommens und zur Verbesserung des Lebensstandards der Haushalte leisten zu können.

Corruption is a social evil that resonates far and deep in societies, eroding trust in governance, weakening the rule of law, impairing economic development, and exacerbating poverty, social tension, and inequality. It is a multidimensional and complex societal malady that occurs in various forms and contexts. As such, any effort to combat corruption must be accompanied by a thorough examination of the attributes that might play a key role in exacerbating or mitigating corrupt environments. This dissertation identifies a number of attributes that influence corruption, using machine learning techniques, neural network analysis, and time series causal relationship analysis and aggregated data from 113 countries from 2007 to 2017. The results suggest that improvements in technological readiness, human development index, and e-governance index have the most profound impacts on corruption reduction. This dissertation discusses corruption at each phase of infrastructure systems development and engineering ethics that serve as a foundation for corruption mitigation. The dissertation then applies novel analytical efficiency measurement methods to measure infrastructure inefficiencies, and to rank infrastructure administrative jurisdictions at the state level. An efficiency frontier is developed using optimization and the highest performing jurisdictions are identified. The dissertation’s framework could serve as a starting point for governmental and non-governmental oversight agencies to study forms and contexts of corruption and inefficiencies, and to propose influential methods for reducing the instances. Moreover, the framework can help oversight agencies to promote the overall accountability of infrastructure agencies by establishing a clearer connection between infrastructure investment and performance, and by carrying out comparative assessments of infrastructure performance across the jurisdictions under their oversight or supervision.

In this study I examine voter response at an interview level using a dataset of 7562 voter contacts (including responses and nonresponses) in the 2004 Utah Colleges Exit Poll. In 2004, 4908 of the 7562 voters approached responded to the exit poll for an overall response rate of 65 percent. Logistic regression is used to estimate factors that contribute to a success or failure of each interview attempt. This logistic regression model uses interviewer characteristics, voter characteristics (both respondents and nonrespondents), and exogenous factors as independent variables. Voter characteristics such as race, gender, and age are strongly associated with response. An interviewer's prior retail sales experience is associated with whether a voter will decide to respond to a questionnaire or not. The only exogenous factor that is associated with voter response is whether the interview occurred in the morning or afternoon.

[pt] Nos últimos anos, observa-se crescente penetração da energia eólica na matriz energética mundial e brasileira. Em 2015, ela já representava (seis por cento) da capacidade total de geração de energia do país, colocando-o na (décima) posição entre os países com capacidade eólica instalada. A crescente penetração dessa fonte de energia e suas características de intermitência e forte sazonalidade, passaram a demandar modelos de otimização capazes de auxiliar tanto a gestão dos sistemas elétricos com geração intermitente de energia eólica, quanto a comercialização dessa energia. Avançaram, assim, os estudos de previsões de médias a cada (dez) minutos, horárias e diárias de geração eólica, para atender a sua inserção na programação dos sistemas elétricos e a sua comercialização em mercados diários e horários. Contudo, poucos estudos deram atenção à previsão e simulação de médias mensais de geração eólica, imprescindíveis para gestão e otimização da comercialização dessa energia no Brasil, visto que esta ocorre essencialmente em base mensal. Neste contexto, insere-se esta tese, que busca avaliar a otimização comercial de um parque eólico no mercado livre de energia brasileiro, considerando diferentes modelos de simulação da incerteza de geração eólica e níveis de aversão ao risco do gestor. Para representar diferentes níveis de aversão ao risco do gestor, desenvolveu-se uma nova função de preferência, capaz de modelar a variação do nível de aversão ao risco de um mesmo gestor, para diferentes faixas de preferência, definidas a partir de percentis αs de VaRα. A função de preferência desenvolvida é uma ponderação entre o valor esperado e níveis de CVaR dos resultados. De certo modo, ela altera as probabilidades dos resultados, de acordo as preferências do gestor, similar ao efeito dos pesos de decisão na Teoria do Prospecto. Para simulação da geração eólica são adotados modelos autorregressivos com sazonalidade representada por dummies mensais (ARX-11) e periódicos (PAR). Considera-se, ainda, a inclusão de variáveis climáticas exógenas no modelo ARX-11, com ganho de capacidade preditiva. Observou-se que, para um gestor neutro ao risco, as diferentes simulações de geração eólica não alteraram a decisão ótima. O mesmo não é válido para um gestor avesso ao risco, especialmente ao ser considerado o modelo de simulação com variáveis climáticas exógenas. Portanto, é importante a definição de um único modelo de simulação a ser considerado pelo gestor avesso ao risco ou, a adoção de alguma técnica multicritério para ponderação de diferentes modelos. O perfil de risco também altera as decisões ótimas do gestor, observando-se redução do desvio-padrão e da média da distribuição dos resultados e, aumento dos CVaRs e prêmio de risco, à medida que aumenta a aversão ao risco. Assim, é importante a especificação de uma única função de preferência, que represente adequadamente o perfil de risco do gestor ou da empresa, para otimização da comercialização. A flexibilidade da função de preferência desenvolvida, ao permitir a definição de diferentes níveis de aversão ao risco do gestor, para diferentes faixas de preferência, contribui para essa especificação. / [en] In recent years, we have seen an increased penetration of wind power in the Brazilian energy matrix and also worldwide. In 2015, wind power already accounted for (six percent) of the Brazilian total power capacity and the country was the (tenth) in the world raking of wind power installed capacity. Due to the growing penetration of the source, its intermittency and strong seasonality, optimization models able to deal with the management of wind power, both in electrical systems operation and in trading environment, are necessary. Thus, we see the growth in the number of studies concerned about wind power forecasts for every (10) minutes, hours and days, meeting the electrical systems and international trading schedules. However, few studies have given attention to the forecasting and simulation of wind power monthly averages, which are essential for the management and optimization of energy trading in Brazil, since its occurs essentially on a monthly basis. In this context, we introduce this thesis, which seeks to assess the commercial optimization of a wind farm in the Brazilian energy free market, considering different simulation models for the wind power production uncertainty and different levels of manager s risk aversion. In order to represent the manager s different levels of risk aversion, we developed a new preference function, which is able to model the variation of risk aversion level of the same manager, for different preference groups. These groups are defined by α s percentiles of VaRα. The developed preference function is a weighted average between expected value of results and CVaR levels. In a way, it changes the odds of the results, according to the manager s preference, similar to the effect of the decision weights on Prospect Theory. We adopted autoregressive models to simulate wind power generation, with seasonality represented by monthly dummies (ARX -11) or periodic model (PAR). Furthermore, we consider the inclusion of climate exogenous variables in the ARX-11 model and obtain predictive gain. We observed that for a risk neutral manager, different simulations of wind power production do not change the optimal decision. However, this does not apply for risk averse managers, especially when we consider the simulation model with climate exogenous variables. Therefore, it is important that the risk averse manager establishes a single simulation model to consider or adopts some multi-criteria technique for weighting different models. The risk profile also changes the manager optimal decision. We observed that increasing risk aversion, the standard deviation and mean of the results distribution decrease, while risk premium and CVaRs increase. Therefore, to proceed the optimization, it is important to specify a single preference function, which represents adequately the manager or company risk profile. The flexibility of the developed preference function, allowing the definition of different manager s risk aversion levels for different preference groups, contributes to this specification.

La plupart des études en gestion sur la performance des entreprises sont centréessur l'explication de la relation entre les facteurs internes ou des caractéristiquesintrinsèques de l'entreprise (niveau d'endettement, diversification des produits, lastratégie concurrentielle, etc.) et son performance. Cette thèse vise à déterminerquels sont les facteurs de risque exogènes qui ont un impact sur la performance desentreprises en République Dominicaine? Ces facteurs, affectent-ils différemment lesmicro, petites et moyennes entreprises en fonction de leur secteur d'activité. Quelest le risque pour chacun des plus représentatifs sous-secteurs des entreprisesDominicaines? Nous avons constaté que les facteurs de risque les plusstatistiquement significatifs sont les dépenses de consommation des ménages, letaux d'intérêt des banques commerciales, l'investissement total, le taux de changede DOP à USD et le déficit de la balance commerciale. La composition etl'importance des facteurs varient considérablement en fonction de la taille desentreprises et le sous-secteur auquel ils appartiennent. Les grandes entreprises sonten moyenne moins risqué que des moyennes, petites et micro entreprises, n’importequel que soit le sous-secteur auquel ils appartiennent. / Many of the management studies on the performance of the company are focusedon explaining the relationship between the internal factors or intrinsic characteristicsof the firm (debt level, diversification of products, competitive strategy, etc.) and itsperformance. This thesis seeks to determine, what are the exogenous risk factorsthat impact the performance of all companies in the Dominican Republic? Thesefactors differentially affect the micro, small and medium enterprises according to theirbusiness sector. What is the risk on each of the most representative sub-sectors ofthe Dominican companies? We found that the most statistically significant riskfactors are the household consumption expenditure, the interest rate of commercialbanks, the total investment, the DOP to USD exchange rate and the deficit on thetrade balance. The composition and importance of the factors significantly variesdepending on the size of the company and the sub-sector to which it belongs. Also,large firms are on average less risky than medium, small and micro regardless of thesub-sector they belong. / Muchos de los estudios de gestión sobre el performance de la empresa se enfocanen explicar la relación que existe entre los factores o características intrínsecas de laempresa (nivel de endeudamiento, diversificación de productos, estrategiacompetitiva, etc.) y el performance de la misma. Esta tesis busca determinar¿cuáles son los factores exógenos de riesgo que impactan el performance de lasempresas de la República Dominicana? Si estos factores afectan de forma diferentea la micro, pequeña y mediana empresa según su actividad empresarial. ¿Cuál es elriesgo que tiene cada uno de los sub-sectores más representativos de las empresasdominicanas? Encontramos que los factores de riesgo estadísticamente mássignificativos son el consumo de los hogares, la tasa de interés de los bancoscomerciales, la inversión total, la tasa de cambio de DOP a USD y el déficit en labalanza comercial. La importancia y composición de los factores varíasignificativamente según el tamaño de la empresa y el sub-sector al que pertenece.También, en promedio, las empresas grandes tienen menos riesgos que lasmedianas, pequeñas y micro sin importar al sub-sector que pertenezcan.