Essays on Business Cycles

The topic of my dissertation is to understand the sources of business cycles. In particular, using structural estimation, I quantitatively investigate different types of shocks that propagate within a country (Chapter One) and that cause business cycle comovement across countries (Chapter Two and Three). In the first chapter, Wataru Miyamoto and I propose the use of data on expectations to identify the role of news shocks in business cycles. News shocks are defined as information about future fundamentals that agents learn in advance. Our approach exploits the fact that news shocks cause agents to adjust their expectations about the future even when current fundamentals are not affected. Using data on expectations, we estimate a dynamic, stochastic, general equilibrium model that incorporates news shocks for the U.S. between 1955Q1 and 2006Q4 using Bayesian estimation. We find that the contribution of news shocks to output is about half of that estimated without data on expectations. The precision of the estimated role of news shocks also greatly improves when data on expectations are used. Although news shocks are important in explaining the 1980 recession and the 1993-94 boom, they do not explain much of other business cycles in our sample. Moreover, the contribution of news shocks to explaining short run fluctuations is negligible. These results arise because data on expectations show that changes in expectations are not large and do not resemble actual movements of output. Therefore, news shocks cannot be the main driver of business cycles. Chapters Two and Three focus on the driving forces of business cycles in open economies. We start Chapter Two with an observation that business cycles are strongly correlated across countries. We document that this pattern is also true for small open economies between 1900 and 2006 using a novel data set for 17 small developed and developing countries. Furthermore, we provide a new evidence about the role of common shocks in business cycles for small open economies in a structural estimation of a real small open economy model featuring a realistic debt adjustment cost and common shocks. We find that common shocks are a primary source of business cycles, explaining nearly 50\% of output fluctuations over the last 100 years in small open economies. The estimated common shocks capture important historical episodes such as the Great depression, the two World Wars and the two oil price shocks. Moreover, these common shocks are important for not only small developed countries but also developing countries. We point out the importance of our structural approach in identifying several types of common shocks and their sizable role in small open economies. The reduced form dynamic factor model approach in the previous literature, which often assumes one type of common component, would predict only a third of the contribution estimated in the structural model. Chapter Three further our understanding of the business cycle comovement across countries by investigating the transmission mechanism of shocks across countries. Our reading of the literature indicates that even though business cycles are correlated across countries, existing models are not able to generate substantial transmission through international trade. To the extent that business cycles are correlated across countries, it is because shocks are correlated across countries. We show that the nature of such transmission depends fundamentally on the features determining the responsiveness of labor supply and labor demand to international relative prices. We augment a standard international macroeconomic model to incorporate three key features: a weak short run wealth effect on labor supply, variable capital utilization, and imported intermediate inputs for production. This model can generate large and significant endogenous transmission of technology shocks through international trade. We demonstrate this by estimating the model using data for Canada and the United States. We find that this model can account for the substantial transmission of permanent U.S. technology shocks to Canadian aggregate variables such as output and hours documented in a structural vector autoregression. Transmission through international trade is found to explain the majority of the business cycle comovement between the United States and Canada while exogenous correlation of technology shocks is not important

Essays on Macroeconomics

This dissertation is a collection of three essays on macroeconomics, examining the sources of business cycles. In particular, we are interested in understanding how shocks propagate over the business cycle in both closed economy and open economy settings. The common approach we take in these chapters is to use both theory and data in a structural estimation based on a dynamic stochastic general equilibrium model. In the first chapter, motivated by the correlation of business cycles across countries, we provide a new empirical evidence about the role of common shocks in business cycles for small open economies. Specifically, we conduct a structural estimation of a small open economy real business cycle model featuring a realistic debt adjustment cost and common shocks. Using a novel dataset for 17 small developed and developing countries between 1900 and 2006, we find that common shocks are a primary source of business cycles, explaining nearly 50% of the output fluctuations over the last 100 years in small open economies. The estimated common shocks capture important historical episodes such as the Great depression, the two World Wars and the two oil price shocks. Moreover, these common shocks are important for not only small developed countries but also developing countries. We point out the importance of our structural approach in identifying the sizable role of both productivity and other common shocks such as interest rate premium shocks. The reduced form dynamic factor model approach in the previous literature, which often assumes one type of common component, would predict only a third of the contribution estimated in the structural model. In the second chapter, we focus on the transmission from one country to another through international trade. First, we argue that while we observe substantial business cycle correlation across countries, especially among developed economies, most existing models are not able to generate strong transmission of shocks endogenously through international trade. In the framework of structural model, we show that the nature of such transmission depends fundamentally on the features determining the responsiveness of labor supply and labor demand to international relative prices. We augment a standard international macroeconomic model to incorporate three key features: a weak short run wealth effect on labor supply, variable capital utilization, and imported intermediate inputs for production. This model can generate large and significant endogenous transmission of technology shocks through international trade. We demonstrate this by estimating the model using data for Canada and the United States with quasi-Bayesian methods. We find that this model can account for the substantial transmission of permanent U.S. technology shocks to Canadian aggregate variables such as output and hours documented in a structural vector autoregression. Transmission through international trade is found to explain the majority of the business cycle comovement between the United States and Canada while exogenous correlation of technology shocks is not important. In the third chapter, we turn to the sources of business cycles in a closed economy setting and analyzes the effects of news shocks, which are found to be an important driver of business cycles in the U.S. in the recent literature. The innovation of this chapter is that we use data on expectations to inform us about the role of news shocks. This approach exploits the fact that news shocks cause agents to adjust their expectations about the future even when current fundamentals are not affected, therefore, data on expectations are particularly informative about the role of news shocks. Using data on expectations, we estimate a dynamic, stochastic, general equilibrium model that incorporates news shocks for the U.S. between 1955Q1 and 2006Q4. We find that the contribution of news shocks to output is about half of that estimated without data on expectations. The precision of the estimated role of news shocks also greatly improves when data on expectations are used. Moreover, the contribution of news shocks to explaining short run fluctuations is negligible. These results arise because data on expectations show that changes in expectations are not large and do not resemble actual movements of output. Therefore, news shocks cannot be the main driver of business cycles

Space-time statistical analysis of malaria morbidity incidence cases in Ghana: A geostatistical modelling approach

Malaria is one of the most prevalent and devastating health problems worldwide. It is a highly endemic disease in Ghana, which poses a major challenge to both the public health and socio-economic development of the country. Major factors accounting for this situation include variability in environmental conditions and lack of prevention services coupled with host of other socio-economic factors. Ghana’s National Malaria Control Programme (NMCP) risk assessment measures have been largely based on household surveys which provided inadequate data for accurate prediction of new incidence cases coupled with frequent incomplete monthly case reports. These raise concerns about annual estimates on the disease burden and also pose serious threats to efficient public health planning including the country’s quest of reducing malaria morbidity and mortality cases by 75% by 2015. In this thesis, both geostatistical space-time models and time series seasonal autoregressive integrated moving average (SARIMA) predictive models have been studied and applied to the monthly malaria morbidity cases from both district and regional health facilities in Ghana. The study sought to explore the spatio-temporal distributions of the malaria morbidity incidence and to account for the potential influence of climate variability, with particular focus on producing monthly spatial maps, delimiting areas with high risk of morbidity. This was achieved by modelling the morbidity cases as incidence rates, being the number of new reported cases per 100,000 residents, which together with the climatic covariates were considered as realisations of random processes occurring in space and/or time. The SARIMA models indicated an upward trend of morbidity incidence in the regions with strong seasonal variation which can be explained primarily by the effects of rainfall, temperature and relative humidity in the month preceding incidence of the disease as well as the morbidity incidence in the previous months. The various spacetime ordinary kriging (STOK) models showed varied spatial and temporal distributions of the morbidity incidence rates, which have increased and expanded across the country over the years. The space-time semivariogram models characterising the spatio-temporal continuity of the incidence rates indicated that the occurrence of the malaria morbidity was spatially and temporally correlated within spatial and temporal ranges varying between 30 and 250 km and 6 and 100 months, respectively. The predicted incidence rates were found to be heterogeneous with highly elevated risk at locations near the borders with neighbouring countries in the north and west as well as the central parts towards the east. The spatial maps showed transition of high risk areas from the north-west to the north-east parts with climatic variables contributing to the variations in the number of morbidity cases across the country. The morbidity incidence estimates were found to be higher during the wet season when temperatures were relatively low whilst low incidence rates were observed in the warm weather period during the dry seasons. In conclusion, the study quantified the malaria morbidity burden in Ghana to produce evidence-based monthly morbidity maps, illustrating the risk patterns of the morbidity of the disease. Increased morbidity risk, delimiting the highest risk areas was also established. This statistical-based modelling approach is important as it allows shortterm prediction of the malaria morbidity incidence in specific regions and districts and also helps support efficient public health planning in the country

Categorical Modelling of Logic Programming: Coalgebra, Functorial Semantics, String Diagrams

Logic programming (LP) is driven by the idea that logic subsumes computation. Over the past 50 years, along with the emergence of numerous logic systems, LP has also grown into a large family, the members of which are designed to deal with various computation scenarios. Among them, we focus on two of the most influential quantitative variants are probabilistic logic programming (PLP) and weighted logic programming (WLP). In this thesis, we investigate a uniform understanding of logic programming and its quan- titative variants from the perspective of category theory. In particular, we explore both a coalgebraic and an algebraic understanding of LP, PLP and WLP. On the coalgebraic side, we propose a goal-directed strategy for calculating the probabilities and weights of atoms in PLP and WLP programs, respectively. We then develop a coalgebraic semantics for PLP and WLP, built on existing coalgebraic semantics for LP. By choosing the appropriate functors representing probabilistic and weighted computation, such coalgeraic semantics characterise exactly the goal-directed behaviour of PLP and WLP programs. On the algebraic side, we define a functorial semantics of LP, PLP, and WLP, such that they three share the same syntactic categories of string diagrams, and differ regarding to the semantic categories according to their data/computation type. This allows for a uniform diagrammatic expression for certain semantic constructs. Moreover, based on similar approaches to Bayesian networks, this provides a framework to formalise the connection between PLP and Bayesian networks. Furthermore, we prove a sound and complete aximatization of the semantic category for LP, in terms of string diagrams. Together with the diagrammatic presentation of the fixed point semantics, one obtain a decidable calculus for proving the equivalence between propositional definite logic programs

Discovering Causal Relations and Equations from Data

Physics is a field of science that has traditionally used the scientific method to answer questions about why natural phenomena occur and to make testable models that explain the phenomena. Discovering equations, laws and principles that are invariant, robust and causal explanations of the world has been fundamental in physical sciences throughout the centuries. Discoveries emerge from observing the world and, when possible, performing interventional studies in the system under study. With the advent of big data and the use of data-driven methods, causal and equation discovery fields have grown and made progress in computer science, physics, statistics, philosophy, and many applied fields. All these domains are intertwined and can be used to discover causal relations, physical laws, and equations from observational data. This paper reviews the concepts, methods, and relevant works on causal and equation discovery in the broad field of Physics and outlines the most important challenges and promising future lines of research. We also provide a taxonomy for observational causal and equation discovery, point out connections, and showcase a complete set of case studies in Earth and climate sciences, fluid dynamics and mechanics, and the neurosciences. This review demonstrates that discovering fundamental laws and causal relations by observing natural phenomena is being revolutionised with the efficient exploitation of observational data, modern machine learning algorithms and the interaction with domain knowledge. Exciting times are ahead with many challenges and opportunities to improve our understanding of complex systems.Comment: 137 page

Disentangling ecological networks in marine microbes

There is a myriad of microorganisms on Earth contributing to global biogeochemical cycles, and their interactions are considered pivotal for ecosystem function. Previous studies have already determined relationships between a limited number of microorganisms. Yet, we still need to understand a large number of interactions to increase our knowledge of complex microbiomes. This is challenging because of the vast number of possible interactions. Thus, microbial interactions still remain barely known to date. Networks are a great tool to handle the vast number of microorganisms and their connections, explore potential microbial interactions, and elucidate patterns of microbial ecosystems. This thesis locates at the intersection of network inference and network analysis. The presented methodology aims to support and advance marine microbial investigations by reducing noise and elucidating patterns in inferred association networks for subsequent biological down-stream analyses. This thesis’s main contribution to marine microbial interactions studies is the development of the program EnDED (Environmentally-Driven Edge Detection), a computational framework to identify environmentally-driven associations inside microbial association networks, inferred from omics datasets. We applied the methodology to a model marine microbial ecosystem at the Blanes Bay Microbial Observatory (BBMO) in the North-Western Mediterranean Sea (ten years of monthly sampling). We also applied the methodology to a dataset compilation covering six global-ocean regions from the surface (3 m) to the deep ocean (down to 4539 m). Thus, our methodology provided a step towards studying the marine microbial distribution in space via the horizontal (ocean regions) and vertical (water column) axes.Hi ha una infinitat de microorganismes a la Terra que contribueixen als cicles biogeoquímics mundials i les seves interaccions es consideren fonamentals pel funcionament dels ecosistemes. Estudis previs ja han determinat les relacions entre un nombre limitat de microorganismes. Tot i això, encara hem d’entendre un gran nombre d’interaccions per augmentar el nostre coneixement dels microbiomes complexos. Això és un repte a causa del gran nombre d'interaccions possibles. Per això, les interaccions microbianes encara són poc conegudes fins ara. Les xarxes són una gran eina per tractar el gran nombre de microorganismes i les seves connexions, explorar interaccions microbianes potencials i dilucidar patrons d’ecosistemes microbians. Aquesta tesi es situa a la intersecció de la inferència de xarxes i l’anàlisi de la xarxes. La metodologia presentada té com a objectiu donar suport i avançar en investigacions microbianes marines reduint el soroll i dilucidant patrons en xarxes d’associació inferides per a posteriors anàlisis biològiques. La principal contribució d’aquesta tesi als estudis d’interaccions microbianes marines és el desenvolupament del programa EnDED (Environmentally-Driven Edge Detection), un marc computacional per identificar associacions impulsades pel medi ambient dins de xarxes d’associació microbiana, inferides a partir de conjunts de dades òmics. S’ha aplicat la metodologia a un model d’ecosistema microbià marí a l’Observatori Microbià de la Badia de Blanes (BBMO) al mar Mediterrani nord-occidental (deu anys de mostreig mensual). També s’ha la metodologia a una recopilació de dades que cobreix sis regions oceàniques globals des de la superfície (3 m) fins a l'oceà profund (fins a 4539 m).Hay una gran cantidad de microorganismos en la Tierra que contribuyen a los ciclos biogeoquímicos globales, y sus interacciones se consideran fundamentales para la función del ecosistema. Estudios previos ya han determinado relaciones entre un número limitado de microorganismos. Sin embargo, todavía necesitamos comprender una gran cantidad de interacciones para aumentar nuestro conocimiento de los microbiomas más complejos. Esto representa un gran desafío debido a la gran cantidad de posibles interacciones. Por lo tanto, las interacciones microbianas son aun poco conocidas. Las redes representan una gran herramienta para analizar la gran cantidad de microorganismos y sus conexiones, explorar posibles interacciones y dilucidar patrones en ecosistemas microbianos. Esta tesis se ubica en la intersección entre la inferencia de redes y el análisis de redes. La metodología presentada tiene como objetivo avanzar las investigaciones sobre interacciones microbianas marinas mediante la reducción del ruido en las inferencias de redes y elucidar patrones en redes de asociación permitiendo análisis biológicos posteriores. La principal contribución de esta tesis a los estudios de interacciones microbianas marinas es el desarrollo del programa EnDED (Environmentally-Driven Edge Detection), un marco computacional para identificar asociaciones generadas por el medio ambiente en redes de asociaciones microbianas, inferidas a partir de datos ómicos. Aplicamos la metodología a un modelo de ecosistema microbiano marino en el Observatorio Microbiano de la Bahía de Blanes (BBMO) en el Mar Mediterráneo Noroccidental (diez años de muestreo mensual). También, aplicamos la metodología a una compilación de conjuntos de datos que cubren seis regiones oceánicas globales desde la superficie (3 m) hasta las profundidades del océano (hasta 4539 m). Por lo tanto, nuestra metodología significa un paso adelante hacia de los patrones temporales microbianos marinos y el estudio de la distribución microbiana marina en el espacio a través de los ejes horizontal (regiones oceánicas) y vertical (columna de agua). Para llegar a hipótesis de interacción precisas, es importante determinar, cuantificar y eliminar las asociaciones generadas por el medio ambiente en las redes de asociaciones microbianas marinas. Además, nuestros resultados subrayaron la necesidad de estudiar la naturaleza dinámica de las redes, en contraste con el uso de redes estáticas únicas agregadas en el tiempo o el espacio. Nuestras nuevas metodologías pueden ser utilizadas por una amplia gama de investigadores que investigan redes e interacciones en diversos microbiomas.Postprint (published version

Safety and Reliability - Safe Societies in a Changing World

The contributions cover a wide range of methodologies and application areas for safety and reliability that contribute to safe societies in a changing world. These methodologies and applications include: - foundations of risk and reliability assessment and management - mathematical methods in reliability and safety - risk assessment - risk management - system reliability - uncertainty analysis - digitalization and big data - prognostics and system health management - occupational safety - accident and incident modeling - maintenance modeling and applications - simulation for safety and reliability analysis - dynamic risk and barrier management - organizational factors and safety culture - human factors and human reliability - resilience engineering - structural reliability - natural hazards - security - economic analysis in risk managemen

The process of conserving biodiversity: From planning to evaluating conservation actions on private land in the Cape Lowlands, South Africa

Conservation can be conceptualised as a process of linked phases that contribute to bringing about effective biodiversity protection: (i) a conservation assessment that identifies spatially explicit conservation priorities to provide strategic guidance on where best to invest conservation resources; (ii) a planning phase that takes the spatial priorities forward into implementation processes by setting out a strategy and schedule for undertaking conservation action; (iii) an implementation phase during which conservation interventions are executed; and (iv) an evaluation phase to investigate whether conservation has been successful. In practice, conservation is rarely conducted in this way. The interrelated phases are often undertaken separately, links are neglected, and conservation science to date has focused primarily on the conservation assessment. This has led to the development of highly sophisticated principles and techniques for locating priority conservation areas, but planning and evaluation have received limited research attention: few published studies demonstrate collaborative planning processes that assist with putting conservation assessments into practice, or show on-theground conservation success linked to effective conservation planning and implementation processes. My PhD research aimed to address these knowledge gaps by conducting a conservation assessment and collaborative planning phase that would lead to effective conservation action as determined by an evaluation. The study area was in the critically endangered Cape Lowlands, a conservation priority area in the Cape Floristic Region, South Africa. The highly transformed agricultural production landscape is mostly privately owned; formal biodiversity protection is low; and remnants of natural vegetation (< 9% is left) harbour an exceptionally diverse flora. Strategic conservation interventions coordinated across the Cape Floristic Region (CFR) provided the overall implementation context in the Cape Lowlands. My research was conducted in this real-world practical situation and addresses the whole conservation process, from assessment to evaluation of conservation actions. I first developed a conservation assessment guided by three key questions: âWhat are feasible, efficient, defensible and efficacious solutions for (i) deriving a surrogate layer that represents biodiversity in a region which is characterised by exceptional plant species richness and endemism ; and (ii) considering the connectivity of natural areas in an ecosystem that is highly transformed, fragmented and largely unprotected?â; and âHow can a selection method be developed for identifying and prioritising key biodiversity areas in a landscape identified as 100% irreplaceable?â To answer these questions I identified feasible, efficient, defensible methods focusing on three key aspects: (i) producing a biodiversity surrogate map of original vegetation cover using two alternative approaches: simple expert mapping and statistical modelling integrating plant species and environmental data; (ii) designing selection units based on vegetation connectivity in a simple technique to include spatial attributes of conservation areas before identifying key biodiversity areas; (iii) developing a prioritisation method based on a simple scoring system and verifying results with MARXAN-selected priority areas. In all vi three cases I found that the simple conservation assessment methods produced suitable outputs for further integration in the assessment and in decision-making during planning. (i) The expert map was as effective as the vegetation model and required fewer resources to be produced since the model relied on resource-intensive species data collection. (ii) In comparison with commonly used cadastre-based units, connectivity-based selection units captured connected vegetation more effectively and area-efficiently in units that served as the basis for priority area selection. (iii) Scoring provided a feasible, defensible mechanism for prioritising key biodiversity areas in the Cape Lowlands where all remaining vegetation has been identified as 100% irreplaceable. The planning phase complemented the assessment. Key guiding questions here were âHow can collaborative planning be used to translate the conservation assessmentâs technical outputs into timebased conservation goals and into useful products for implementation?â and âWhat constitutes effective planning in the conservation process? Through a collaborative scheduling process, I developed timebased conservation goals for action in the Cape Lowlands. This was undertaken in two work sessions with scientists, planners and conservation practitioners from the implementing agency, CapeNature. Scheduling was guided by (i) scoring-derived biodiversity-driven spatial priorities that made intuitive sense to implementers; and (ii) conservation opportunities and constraints (including resources) identified by the practitioners. Scheduling was conducted with reference to the on-going development of a private land conservation strategy for the CFR to be piloted in the Cape Lowlands. The scheduling process was an effective platform for taking spatial priorities from the assessment towards implementation: the discourse-based collaborative planning was constructive and led to consensusbased final products, including a 20-year and 5-year conservation plan setting out spatially explicit goals for conservation interventions in the Cape Lowlands. The main limitation of the process was that resource planning was not integrated explicitly enough to identify realistic goals. This highlighted the importance of integrating detailed resource considerations in future planning. Finally, to address the question âTo what extent has the Cape Lowlands conservation plan been implemented after five years of off-reserve conservation interventions in the region?â I developed a protocol for evaluating the effectiveness of conservation action in the Cape Lowlands. I assessed (i) the extent to which the goals conservation plans produced in the planning phase had been implemented; and (ii) the achievements of incentive-based conservation stewardship interventions on private land in the Cape Lowlands and CFR. Achievements were measured as hectares of vegetation protected through voluntary and legally-binding contractual conservation agreements between landowners and conservation organisations. The evaluation revealed that (i) CapeNatureâs stewardship interventions in the Cape Lowlands focused on priority areas identified in the 5- and 20-year conservation plans, thus demonstrating effective execution of the plans; (ii) private land conservation interventions have been remarkably successful and cost-effective: 68604ha priority vegetation were protected in the CFR under conservation agreements by end 2007, rivalling private land biodiversity conservation in the U.S.A. and Latin America, and more than 8000ha in the critically endangered Cape Lowlands at a cost of R 6.8 vii Million (< 1 million US$). The evaluation identified the long-term financial sustainability of current implementation programmes as the most significant threat to future success in private land conservation interventions in the Cape Lowlands and CFR. There is significant scope to design future monitoring and evaluation systems to measure ecological gains due to specific conservation actions, not done in the Cape Lowlands study, and to tailor approaches to suit specific programme stages. This PhD provides a rare overview of an entire conservation cycle with linked phases that has led to biodiversity protection. The study highlights that an effective long-term process demands significant investment in (i) a diverse (growing) set of skills and expertise to solve complex conservation situations; (ii) time, especially for visible implementation success; and (iii) well-allocated resources (money, time, skills, research attention) across all phases in the conservation process. This is necessary as each phase is needed to achieve the ultimate conservation goal: I show in the Cape Lowlands that a simple conservation assessment with limited funds (R1.8 million over 3 years) can be highly effective in guiding action towards priority areas. Important here is to develop rapid, defensible methods for cost-effective assessments and linking these with in-depth planning processes. Planning and evaluation in the Cape Lowlands were essential connecting phases that continue to support implementation success. In the context of on-going conservation action, planning and evaluation need to become part of a cyclical conservation process geared towards improved practices. I suggest that significantly greater investment in planning and evaluation research is essential to move conservation science forward in fulfilling its fundamental goal of strategically guiding where, when and how to invest optimally in conservation interventions. This will be exceptionally beneficial for undertaking effective conservation interventions and will help to clearly demonstrate the value of the research for conservation practice