Dividend policy is widely researched in financial management, but determining whether it affects the market price per share is difficult. There has been much published on the subject, which presented theories such as the Modigliani, Miller, Gordon, Lintner, Walter and Richardson propositions and the relevance and irrelevance theories. However, little research has been done on the impact of dividend policy on shareholders’ wealth while considering the short- and long-run effects. The Vector Error Correction Model (VECM) was used to describe the short-run and long-run dynamics or the adjustment of the cointegrated variables towards their equilibrium values in South Africa. This study attempts to explain the effect of dividend policy on the market price per share. A sample of 46 companies listed on the Johannesburg Securities Exchange (JSE) was selected for the period 1995-2010. Three variables were used, namely the market price per share, the dividend per share and the earnings per share. The market price per share was used as a proxy in measuring shareholders’ wealth and the dividend per share was used as a proxy in measuring the dividend policy. Fixed and random effects models were applied to panel data to determine the relation between dividend policy and market price per share. The fixed effects method was used to control the stable characteristics of the companies over a fixed period. The random effects model was applied when the companies’ characteristics differed. Results for both models indicated that dividend yield is positively related to market price per share, while earnings per share do not have a significant impact on the market price per share. To test the strength of the long-run relationship, the VECM was applied. The coefficient for dividend per share in the co-integrating equation was positive, while the coefficient for earnings per share was negative. This confirms previous research findings. The results suggest that there is a long-run relationship between dividend per share and market price per share. The Granger causality test indicates there is bi-directional Granger causality between market price per share and dividend per share in South Africa. Therefore dividend policy does have a significant long-run impact on the share price and therefore provides a signal about the company’s financial success. / Dissertation (MCom)--University of Pretoria, 2012. / Financial Management / Unrestricted

La caractérisation des tremblements de terre est un domaine de recherche primordial en sismologie, où l'objectif final est de fournir des estimations précises d'attributs de la source sismique. Dans ce domaine, certaines questions émergent, par exemple : quand un tremblement de terre s’est-il produit? quelle était sa taille? ou quelle était son évolution dans le temps et l'espace? On pourrait se poser d'autres questions plus complexes comme: pourquoi le tremblement s'est produit? quand sera le prochain dans une certaine région? Afin de répondre aux premières questions, une représentation physique du phénomène est nécessaire. La construction de ce modèle est l'objectif scientifique de ce travail doctoral qui est réalisé dans le cadre de la modélisation cinématique. Pour effectuer cette caractérisation, les modèles cinématiques de la source sismique sont un des outils utilisés par les sismologues. Il s’agit de comprendre la source sismique comme une dislocation en propagation sur la géométrie d’une faille active. Les modèles de sources cinématiques sont une représentation physique de l’histoire temporelle et spatiale d’une telle rupture en propagation. Cette modélisation est dite approche cinématique car les histoires de la rupture inférées par ce type de technique sont obtenues sans tenir compte des forces qui causent l'origine du séisme.Dans cette thèse, je présente une nouvelle méthode d'inversion cinématique capable d'assimiler, hiérarchiquement en temps, les traces de données à travers des fenêtres de temps évolutives. Cette formulation relie la fonction de taux de glissement et les sismogrammes observés, en préservant la positivité de cette fonction et la causalité quand on parcourt l'espace de modèles. Cette approche, profite de la structure creuse de l’histoire spatio-temporelle de la rupture sismique ainsi que de la causalité entre la rupture et chaque enregistrement différé par l'opérateur. Cet opérateur de propagation des ondes connu, est différent pour chaque station. Cette formulation progressive, à la fois sur l’espace de données et sur l’espace de modèle, requiert des hypothèses modérées sur les fonctions de taux de glissement attendues, ainsi que des stratégies de préconditionnement sur le gradient local estimé pour chaque paramètre du taux de glissement. Ces hypothèses sont basées sur de simples modèles physiques de rupture attendus. Les applications réussies de cette méthode aux cas synthétiques (Source Inversion Validation Exercise project) et aux données réelles du séisme de Kumamoto 2016 (Mw=7.0), ont permis d’illustrer les avantages de cette approche alternative d’une inversion cinématique linéaire de la source sismique.L’objectif sous-jacent de cette nouvelle formulation sera la quantification des incertitudes d’un tel modèle. Afin de mettre en évidence les propriétés clés prises en compte dans cette approche linéaire, dans ce travail, j'explore l'application de la stratégie bayésienne connue comme Hamiltonian Monte Carlo (HMC). Cette méthode semble être l’une des possibles stratégies qui peut être appliquée à ce problème linéaire sur-paramétré. Les résultats montrent qu’elle est compatible avec la stratégie linéaire dans le domaine temporel présentée ici. Grâce à une estimation efficace du gradient local de la fonction coût, on peut explorer rapidement l'espace de grande dimension des solutions possibles, tandis que la linéarité est préservée. Dans ce travail, j'explore la performance de la stratégie HMC traitant des cas synthétiques simples, afin de permettre une meilleure compréhension de tous les concepts et ajustements nécessaires pour une exploration correcte de l'espace de modèles probables. Les résultats de cette investigation préliminaire sont encourageants et ouvrent une nouvelle façon d'aborder le problème de la modélisation de la reconstruction cinématique de la source sismique, ainsi, que de l’évaluation des incertitudes associées. / The earthquake characterization is a fundamental research field in seismology, which final goal is to provide accurate estimations of earthquake attributes. In this study field, various questions may rise such as the following ones: when and where did an earthquake happen? How large was it? What is its evolution in space and time? In addition, more challenging questions can be addressed such as the following ones: why did it occur? What is the next one in a given area? In order to progress in the first list of questions, a physical description, or model, of the event is necessary. The investigation of such model (or image) is the scientific topic I investigate during my PhD in the framework of kinematic source models. Understanding the seismic source as a propagating dislocation that occurs across a given geometry of an active fault, the kinematic source models are the physical representations of the time and space history of such rupture propagation. Such physical representation is said to be a kinematic approach because the inferred rupture histories are obtained without taking into account the forces that might cause the origin of the dislocation.In this PhD dissertation, I present a new hierarchical time kinematic source inversion method able to assimilate data traces through evolutive time windows. A linear time-domain formulation relates the slip-rate function and seismograms, preserving the positivity of this function and the causality when spanning the model space: taking benefit of the time-space sparsity of the rupture model evolution is as essential as considering the causality between rupture and each record delayed by the known propagator operator different for each station. This progressive approach, both on the data space and on the model space, does require mild assumptions on prior slip-rate functions or preconditioning strategies on the slip-rate local gradient estimations. These assumptions are based on simple physical expected rupture models. Successful applications of this method to a well-known benchmark (Source Inversion Validation Exercise 1) and to the recorded data of the 2016 Kumamoto mainshock (Mw=7.0) illustrate the advantages of this alternative approach of a linear kinematic source inversion.The underlying target of this new formulation will be the future uncertainty quantification of such model reconstruction. In order to achieve this goal, as well as to highlight key properties considered in this linear time-domain approach, I explore the Hamiltonian Monte Carlo (HMC) stochastic Bayesian framework, which appears to be one of the possible and very promising strategies that can be applied to this stabilized over-parametrized optimization of a linear forward problem to assess the uncertainties on kinematic source inversions. The HMC technique shows to be compatible with the linear time-domain strategy here presented. This technique, thanks to an efficient estimation of the local gradient of the misfit function, appears to be able to rapidly explore the high-dimensional space of probable solutions, while the linearity between unknowns and observables is preserved. In this work, I investigate the performance of the HMC strategy dealing with simple synthetic cases with almost perfect illumination, in order to provide a better understanding of all the concepts and required tunning to achieve a correct exploration of the model space. The results from this preliminary investigation are promising and open a new way of tackling the kinematic source reconstruction problem and the assessment of the associated uncertainties.

Le domaine des problèmes inverses est une discipline qui se trouve à la frontière des mathématiques appliquées et de la physique et qui réunit les différentes solutions pour résoudre les problèmes d'optimisation mathématique. Dans le cas de la déconvolution 1D, ce domaine apporte un formalisme pour proposer des solutions avec deux grands types d'approche: les problèmes inverses avec régularisation et les problèmes inverses bayésiens. Sous l'effet du déluge de données, les géosciences et la planétologie nécessitent des algorithmes de plus en plus plus complexe pour obtenir des informations pertinentes. Dans le cadre de cette thèse, nous proposons d'apporter des connaissances dans trois problèmes de déconvolution 1D sous contrainte avec régularisation dans le domaine de l'hydrologie, la sismologie et de la spectroscopie. Pour chaque problème nous posons le modèle direct, le modèle inverse, et nous proposons un algorithme spécifique pour atteindre la solution. Les algorithmes sont définis ainsi que les différentes stratégies pour déterminer les hyper-paramètres. Aussi, des tests sur des données synthétiques et sur des données réelles sont exposés et discuté du point de vue de l'optimisation mathématique et du point de vue du domaine de l'application choisi. Finalement, les algorithmes proposés ont l'objectif de mettre à portée de main l'utilisation des méthodes des problèmes inverses pour la communauté des Géosciences. / The inverse problem field is a domain at the border between applied mathematics and physics that encompasses the solutions for solving mathematical optimization problems. In the case of 1D deconvolution, the discipline provides a formalism to designing solutions in the frames of its two main approaches: regularization based inverse problems and bayesian based inverse problems. Under the data deluge, geosciences and planetary sciences require more and more complex algorithms for obtaining pertinent information. In this thesis, we solve three 1D deconvolution problems under constraints with regularization based inverse problem methodology: in hydrology, in seismology and in spectroscopy. For every of the three problems, we pose the direct problem, the inverse problem, and we propose a specific algorithm to reach the solution. Algorithms are defined but also the different strategies to determine the hyper-parameters. Furthermore, tests on synthetic data and on real data are presented and commented from the point of view of the inverse problem formulation and that of the application field. Finally, the proposed algorithms aim at making approachable the use of inverse problem methodology for the Geoscience community.

京都大学 / 0048 / 新制・課程博士 / 博士(医学) / 甲第18506号 / 医博第3926号 / 新制||医||1005(附属図書館) / 31392 / 京都大学大学院医学研究科医学専攻 / (主査)教授 河野 憲二, 教授 髙橋 良輔, 教授 福山 秀直 / 学位規則第4条第1項該当 / Doctor of Medical Science / Kyoto University / DFAM

Thesis (Ph.D. (Economics)) -- University of Limpopo, 2022 / This study investigated the relationship between economic complexity and the three mac-roeconomic variables in a comparative setting between selected Sub-Saharan African (SSA) and BRICS countries. Economic complexity as a development index reveals how sophisticated a country is as shown by its exports structure through the Product Com-plexity Index (PCI) and Economic Complexity Index (ECI). The three macroeconomic var-iables are gross domestic product per capita (GDP per capita), current account and fixed investment (gross fixed capita formation) for the period 1994 to 2018.The first three set study objectives were investigated on whether there exists a short and long-run relation-ship through a Panel Autoregressive Distributed Lag (PARDL). The the fourth objective was to test for causality through a standard Granger causality, and fifth, to forecast the macroeconomic variables for the foreseeable future utilising the Impulse Response Func-tion (IRF) and the variance decomposition techniques, these are complementary tech-niques. The last two objectives were to draw a comparative analysis upon the findings, and to relate on the product complexities and economic landscape in the selected SSA and BRICS. Reporting on the ECI-GDP per capita nexus, the PARDL estimates revealed a positive and significant association between ECI and GDP per capita in both the se-lected SSA and BRICS in the long-run. There was no Granger causal effect between ECI and GDP per capita for both set of countries. The concern was in relation to forecasting GDP per capita due to a shock in ECI. The selected SSA GDP per capita response to a shock in ECI was neutral when adopting the IRF technique, and the variance decompo-sition also revealed small estimates in both the short and long-run, below 1%. In the BRICS economies, there was a meaningful positive reaction from a shock in ECI when deploying the IRF technique, while the variance decomposition had a 3% response in the long run when seen through the variance decomposition. On the current account-ECI relationship, the PARDL estimates exposed that there was a positive and significant impact from ECI on the current account in both the groups in the long-run significant while short-run results were insignificant. Granger causality could not detect any causal effect between ECI and current account in the selected SSA, while in the BRICS countries there was a unidirectional causal effect from ECI to current account. When forecasting the current account, the selected SSA reacted negatively to a shock in v ECI seen through the IRF, and the variance decomposition also revealed a small reaction in any period. In the BRICS case, current account’s response was a positive and explo-sive reaction from a shock in ECI when applying the IRF technique. The VD revealed a higher change in current account was explained by a shock in ECI. On the ECI-Fixed Investment, the PARDL estimates showed that there was a long-run positive and signifi-cant effect between ECI and fixed investment in bothgroups. However, the Granger causal results revealed no presence of causality in the selected SSA, while there was causal unidirectional effect from ECI to fixed investment. The IRF technique revealed a negative fixed investment reaction from a shock in ECI, and the variance decomposition results revealed a small reaction in fixed investment in the selected SSA. In the BRICS case, there was a positive and explosive fixed investment emanating from a shock in ECI. Utilising the variance decomposition fixed investment in BRICS was explained by inno-vative shocks in ECI in the long run. On the last two objectives, comparatively the selected SSA countries are disadvantaged as they are concentrated in negative ECI as seen in the descriptive statistics, reflecting that they are still much less developed. This tells us that they are less industrialised as compared to the BRICS nations who are better off. These selected SSA economies are not developed enough as compared to the BRICS nations. The SSA region needs to learn from the leading BRICS countries by creating a conducive environment for a better de-velopment of innovation that improves the domestic value chain that produces knowledge-based products for the export market. The rest of the selected SSA region should form part of economic integrations with the more developed countries that offer mutual beneficiation like South Africa to fast track the developmental of their states. There is a need to modernise the agricultural and agro-industries. The region should harness the full potential of its agricultural sector. This will create a large global market share and perhaps increase the current account outlook through trade with more efficient agro-pro-cessed products. Africa needs to scale up investment in many fronts from government to private investment to improve infrastructure, more so that the scale of needs is so much in the continent.

Thesis (M.Sc. (Agricultural Economics)) -- University of Limpopo, 2022 / Agricultural production measures the performance and efficiency of a country’s agricultural sector. The state of agricultural production can be assessed through the value of agricultural production, which is a product of agricultural gross production and output prices in monetary terms. The study examines the relationship between the value of agricultural production, government spending on agriculture, and other selected variables. Annual data for the value of agricultural production, government expenditure in agriculture, consumer price index, average annual rainfall, food import value, and population from 1983 to 2019 were collected from different sources and were used in the analysis for this study. The Johansen cointegration test was used to determine the existence of a long-run relationship between the value of agricultural production and selected variables by using both the trace and eigenvalue tests. The results indicated that there is a long run relationship among the variables. The study further used the Granger causality test to check the causality between the value of agricultural production and government expenditure in agriculture. The results show that there is no causal effect between the two variables. Lastly, the study used a Vector autoregressive (VAR) model to determine the relationship between the value of agricultural production and selected variables. The results of the VAR model indicated that government expenditure on agriculture, average annual rainfall, food import value, and population positively affect the value of agricultural production. The study also found that the consumer price index negatively affects the value of agricultural production. The study recommends that the government increase its spending on the agricultural sector, which could be in the form of research investment in technologies such as climate-smart agricultural technologies. Additionally, the study recommends that policymakers should review the monetary policy of South Africa to ensure price stability and prevent inflation. Lastly, the study recommends that the South African government should discourage imports and encourage South African agricultural producers to produce more major imported food products.

Thesis (M.Com. (Economics)) -- University of Limpopo, 2022 / The primary goal of this study was to investigate the effects of budget deficit on fixed investment using annual data for the period 1990-2017 in selected African countries namely, Cameroon, Namibia, Ghana, Egypt, Seychelles, Mauritius, Botswana, Lesotho and South Africa. The study employed panel unit root tests including the Augmented Dickey-Fuller test, Philips Perron test and Levin Lin and chu test. The tests revealed that all the variables are integrated at 1st difference. The study further employed the Panel ARDL bounds test to examine the relationship between budget deficit, fixed investment, money supply and inflation. The empirical findings indicated that a long run relationship exists between the variables of interest. Furthermore, the results revealed that the budget deficit has a negative and statistically significant effect on fixed investment. A one percent increase in the budget deficit, ceteris paribus, leads to a reduction in fixed investment by 44 percent in the long run. The findings further postulated a bidirectional causal relationship between budget deficit and fixed investment, between money supply and fixed investment and between fixed investment and inflation. It was evident in the research that indeed the budget deficit is a problematic macroeconomic policy in African countries. Policy makers should limit high government expenditures as they contribute to increased and persistent budget deficits which crowd out private investment.

Bakgrund:  Balanserat styrkort är en ledningsmetod och en styrfilosofi för att styra organisationer. Det fungerar som ett ramverk och verktyg för att mäta, följa upp och hantera prestationer. Enligt Kaplan och Norton innefattar balanserat styrkort fyra perspektiv, det finansiella perspektivet, kundperspektivet, processperspektivet samt utvecklingsperspektivet. Perspektiven utgör en hierarkisk ordning med ett kausalt samband mellan respektive perspektiv. Få studier har fastställt kausalitet i samband med användning av balanserat styrkort som Kaplan och Norton (1992) tidigare beskrivit vara en stor del av ett väl balanserat styrkort. Genom att undersöka en förvaltning i offentlig sektor erhålls därför en uppfattning kring hur det balanserade styrkortet är uppbyggt i dess verksamhet och om kausalitet råder mellan de fyra perspektiven. Studien fokuserar på en förvaltnings introduktion av det balanserade styrkortet och hur detta används och dess effekter, samt om offentliga sektorn innehar kausalitet mellan de fyra perspektiven.  Syfte:  Syftet med studien är att skapa en förståelse för hur det uppställda balanserade styrkortet används inom omsorgsförvaltningens aktiviteter och utreda om det råder kausalitet mellan perspektiven.  Metodval:  Forskningsstudiens tillvägagångssätt utgår från en kvalitativ metod, där en fallstudie har genomförts i Kristianstad kommuns omsorgsförvaltning. Insamlingen av det empiriska materialet har erhållits genom åtta semistrukturerade intervjuer, med fyra verksamhetsutvecklare, tre medarbetare samt en planeringschef för förvaltningen. Empirin har kompletterats med sex dokument från kommunen. Den teoretiska referensramen syftar till att skapa en förståelse för ämnet balanserat styrkort. Slutligen kopplas empirin till teorin i ett analysavsnitt.  Slutsats:  Det balanserade styrkortet tillämpas i omsorgsförvaltningens genom att i första hand fastställa kvalitetsmålen attraktivitet, trygghet, hållbarhet och ekonomi. Sedan tillämpas balanserat styrkort för att styra och följa upp verksamheternas aktiviteter genom att etablera nyckeltal med avseende på de fyra kvalitetsmålen. Styrkortet fungerar som en strategisk ram för hela förvaltningen som hjälper till att specificera och kommunicera målen samt koppla dem till konkreta indikatorer, det vill säga nyckeltal. Aktiviteterna i respektive perspektiv ger upphov till effekter i de andra dimensionerna i den hierarkiska ordningen, ett samband som tyder på kausalitet, en kedja aktiviteter som kan kopplas till en orsak-verkan-effekt. Genom att beakta kausaliteten mellan de fyra perspektiven som förekommer till följd av tillämpningen av balanserat styrkort kan förvaltningen uppnå sina mål på ett mer effektivt och strategiskt sätt. Det främjar en helhetssyn på verksamheten och hjälper till att skapa en balans mellan de olika dimensionerna. / Background: Balanced scorecard is a management method and a management philosophy for managing organizations. It serves as a framework and tool for measuring, tracking and managing performance. According to Kaplan and Norton, the balanced scorecard includes four perspectives, the financial perspective, the customer perspective, the internal business process perspective and the learning and growth perspective. The perspectives constitute a hierarchical order with a causality between them. Few studies have established causality associated with the use of the balanced scorecard that Kaplan and Norton (1992) previously described as a major component of a well-balanced scorecard. By examining an administration in the public sector, an idea is therefore obtained about how the balanced scorecard is structured in its operations and whether causality prevails between the four perspectives. The study will focus on an administration's introduction of the balanced scorecard and how this is used and its effects, as well as whether the public sector holds causality between the four perspectives.  Purpose:  The purpose of this study is to create an understanding of how a balanced scorecard is used within the activities of the care administration and to investigate whether there is causality between the perspectives.  Method:  The study's approach is based on a qualitative method, where a case study has been carried out in Kristianstad municipality's care administration. The collection of the empirical material has been obtained through eight semi-structured interviews, with four business developers, three employees and a planning manager for the administration. The empiricism has been supplemented with six documents from the municipality. The theoretical frame of reference aims to create an understanding of the subject of the balanced scorecard. Finally, the empirical evidence is linked to the theory in an analysis section.  Conclusion:  The balanced scorecard is applied in care management by primarily determining the quality goals of attractiveness, safety, sustainability and economy. A balanced scorecard is then applied to manage and follow up the operations' activities by establishing key performance indicators with regard to the four quality objectives. The scorecard functions as a strategic framework for the entire administration that helps specify and communicate the goals and link them to concrete indicators, i.e key performance indicators. The activities in each perspective give rise to effects in the other dimensions of the hierarchical order, a connection that indicates causality and a chain of activities that can be linked to a cause-effect-effect. By considering the causality between the four perspectives that occur as a result of the application of the balanced scorecard, management can achieve its goals in a more efficient and strategic way. It promotes a holistic view of the business and helps to create a balance between the different dimensions, such as the financial perspective, the citizen perspective, the employee perspective and the development perspective.

This study will be investigating the relationship between short-term and long-term interest rates with the OMX30 stock return expressed in percentage, as well as the effect that the interest rates have on the stock return. The data used in this study has been collected from the dataprogram Datastream with monthly observations from January 2003 until December 2022 resulting in 240 different variables within all three factors over a period of 20 years. While performing OLS estimation, the result estimated by using R-studio shows a negative correlation between the interest rates and the percentage return of OMX30. Furthermore, the Granger causality test shows that the short-term interest rate does have an impact on the market whilst the long-term interest rate does not have any direct effect on the stock market in Sweden.

Performing causal reasoning in a scene is an inherent mechanism in human cognition; however, the majority of approaches in the causality literature aiming for this task still consider constrained scenarios, such as simple physical systems or stationary time-series data. In this work we aim for causal discovery in videos concerning realistic scenarios. We gather motivation for causal discovery by acknowledging this task to be core at human cognition. Moreover, we interpret the scene as a composition of time-series that interact along the sequence and aim for modeling the non-stationary behaviors in a scene. We propose State-dependent Causal Inference (SDCI) for causal discovery in conditional stationary time-series data. We formulate our problem of causal analysis by considering that the stationarity of the time-series is conditioned on a categorical variable, which we call state. Results show that the probabilistic implementation proposed achieves outstanding results in identifying causal relations on simulated data. When considering the state being independent from the dynamics, our method maintains decent accuracy levels of edge-type identification achieving 74.87% test accuracy when considering a total of 8 states. Furthermore, our method correctly handles regimes where the state variable undergoes complex transitions and is dependent on the dynamics of the scene, achieving 79.21% accuracy in identifying the causal interactions. We consider this work to be an important contribution towards causal discovery in videos. / Att utföra kausala resonemang i en scen är en medfödd mekanism i mänsklig kognition; dock betraktar fortfarande majoriteten av tillvägagångssätt i kausalitetslitteraturen, som syftar till denna uppgift, begränsade scenarier såsom enkla fysiska system eller stationära tidsseriedata. I detta arbete strävar vi efter kausal upptäckt i videor om realistiska scenarier. Vi samlar motivation för kausal upptäckt genom att erkänna att denna uppgift är kärnan i mänsklig kognition. Dessutom tolkar vi scenen som en komposition av tidsserier som interagerar längs sekvensen och syftar till att modellera det icke-stationära beteendet i en scen. Vi föreslår Tillståndsberoende kausal inferens (SDCI) för kausal upptäckt i villkorlig stationär tidsseriedata. Vi formulerar vårt problem med kausalanalys genom att anse att tidsseriens stationäritet är villkorad av en kategorisk variabel, som vi kallar tillstånd. Resultaten visar att det föreslagna probabilistiska genomförandet uppnår enastående resultat vid identifiering av orsakssambandet på simulerade data. När man överväger att tillståndet är oberoende av dynamiken, upprätthåller vår metod anständiga noggrannhetsnivåer av kanttypsidentifiering som uppnår 74, 87% testnoggrannhet när man överväger totalt 8 tillstånd. Dessutom hanterar vår metod korrekt regimer där tillståndsvariabeln genomgår komplexa övergångar och är beroende av dynamiken på scenen och uppnår 79, 21% noggrannhet för att identifiera kausala interaktioner. Vi anser att detta arbete är ett viktigt bidrag till kausal upptäckt i videor.