京都大学 / 0048 / 新制・課程博士 / 博士(情報学) / 甲第20026号 / 情博第621号 / 新制||情||108(附属図書館) / 33122 / 京都大学大学院情報学研究科社会情報学専攻 / (主査)教授 田中 克己, 教授 吉川 正俊, 教授 黒橋 禎夫 / 学位規則第4条第1項該当 / Doctor of Informatics / Kyoto University / DFAM

In the aspect of electric-ship grounding, the time-domain behavior of the ship hull is needed. The grounding scheme impacts the nature of voltage transients during switching events and faults, identifiability and locatability of ground faults, fault current levels, and power quality. Due to the large size of ships compared with the wavelengths of the desired signals, time-domain measurement or simulation is a time-consuming process. Therefore, it is preferred that the behavior be studied in the frequency-domain. In the frequency-domain one can break down the whole ship hull into small blocks and find the frequency behavior of each block (scattering parameters) in a short time and then connect these blocks and find the whole ship hull scattering parameters. Then these scattering pa- rameters should be transferred to the time-domain. The problem with this process is that the measured frequency-domain data (or the simulated data) is band-limited so, while calculating time-domain solutions, due to missing DC and low frequency content the time-domain response encounters causality, passivity and time-delay problems. Despite availability of several software and simulation packets that convert frequency-domain information to time-domain, all are known to suffer from the above mentioned problems. This dissertation provides a solution for computing the Time-Domain Impulse-Response for a system by using its measured or simulated scattering parameters. In this regard, a novel wavelet computational approach is introduced.

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Foreign direct investment flows are an important theme in the analysis of global capital flows. The study aims to analyse the effect a withdrawal of FDI has on the economy of Brazil. This is based on the sharp decrease FDI inflows have seen during 2020. The study is based on the theory of David Ricardo and microeconomic and macroeconomic FDI theories. A vector autoregressive model (VAR) was applied using the Toda andYamamoto methodology. The findings show that FDI inflows are positively affected by an increase in GDP and negatively affected by an increase in real average monthly wages. Furthermore, no effect of FDI on any of the considered variables could be determined. This can be attributed to the complexity of the Brazilian economy during the considered period of March 2012 – October 2021, as well as a possible omitted variable bias. This thesis is intended to serve as an avenue for further research on the effect FDI withdrawals have on the Brazilian economy.

This thesis aims to use statistical methods to analyze wind tunnel data generated in automotive aerodynamics testing to understand the properties of aerodynamic force and pressure in a vehicle's working environment. The data used for analysis are visualized, clustered and finally analyzed for Granger causality to see whether a causal link exists between different variables. Then, the pressure measurements taken from the scaled vehicle model is visualized with heat maps and further quantified with K-means and K-medoids clustering. Using the reduced-dimension pressure data derived from cluster analysis, combined with aerodynamic force data, a VAR model is fitted, and the causal relationships between the variables in the data set is explored using Granger causality testing.

This thesis investigates the impact of financial intermediation on economic growth in two regions: the East African Community (EAC) countries (Burundi, Kenya, Tanzania, Rwanda, and Uganda) and North African countries (Algeria, Egypt, Morocco, and Tunisia). The study analyzes the regions employing a Granger causality test and explores if financial intermediation influences economic growth. An index that measures financial intermediation is created using Principal Component Analysis (PCA) and is used to capture the effect it has on economic growth in the two regions. The data used in the study is from 1990 to 2018. The results show that there is a short-run unidirectional relationship between financial intermediation and economic growth in EAC countries while financial intermediation does not Granger cause economic growth in North African countries. The result also shows that inflation has a short-run impact on growth in the North African countries.

Ever since Ghana gained independence, its policy makers have identified education as a tool to foster economic growth and development. In recognition of the vast potential for national development that education presents Ghana, various governments have invested considerably in the sector. These investments have been in the form of educational sector reforms, as well as yearly reoccurring expenditure. Despite these massive investments however, very little work has been done to empirically investigate the impact of such expenditure on the nation’s economy. This paper uses data from Ghana to empirically assess the nature of the relationship between education expenditure (a proxy for human capital development) and GDP growth (a proxy for economic growth). The Granger Causality Test is applied to education expenditure and GDP growth data, from 2003 to 2018. Using data from this same time frame, separate Granger Causality tests are also implemented to test the relationship between Gross Enrollment Rates/ Total Completion Rates, at some levels of education, and GDP growth.   Interestingly enough, the analysis shows no Granger causal relationship between our main variables of interest (Total Education Expenditure and GDP growth). Results also show that none of the education variables Granger cause GDP growth, if the test uses 1 lag and also if the test uses 3 lags. Additionally, results show that whether the test uses 1 lag or 2 lags, GDP growth Granger causes the percentage of total government expenditure that is dedicated to education. Results for tests that use 2 lags also shows that the only education variable that Granger causes GDP growth is enrolment rate at the primary level, with GDP growth also not Granger causing any education variable apart from the percentage of government expenditure dedicated to education. In the case of the test using 3 lags, results show that GDP growth Granger causes only one education variable which is expenditure on the Senior High School level. / Ända sedan Ghana blev självständigt har dess beslutsfattare identifierat utbildning som ett verktyg för att främja ekonomisk tillväxt och utveckling. Som ett erkännande av den enorma potential för nationell utveckling som utbildning erbjuder Ghana, har olika regeringar investerat avsevärt i sektorn. Dessa investeringar har varit i form av reformer av utbildningssektorn, såväl som årliga återkommande utgifter. Trots dessa massiva investeringar har dock mycket lite arbete gjorts för att empiriskt undersöka effekterna av sådana utgifter på landets ekonomi. Denna artikel använder data från Ghana för att empiriskt bedöma karaktären av sambandet mellan utbildningsutgifter (en proxy för utveckling av mänskligt kapital) och BNP-tillväxt (en proxy för ekonomisk tillväxt). Granger Causality Test tillämpas på utbildningsutgifter och BNP-tillväxtdata, från 2003 till 2018. Med hjälp av data från samma tidsram implementeras även separata Granger Causality-tester för att testa sambandet mellan bruttoinskrivningsfrekvenser/Totala slutförandefrekvenser, på vissa nivåer utbildning och BNP-tillväxt. Intressant nog visar analysen inget Granger-kausalt samband mellan våra huvudsakliga intressevariabler (Total Education Expenditure och BNP-tillväxt). Resultat visar också att ingen av utbildningsvariablerna Granger orsakar BNP-tillväxt, om testet använder 1 tidstidsfördröjning och även om testet använder 3 tidsfördröjningar. Dessutom visar resultaten att oavsett om testet använder 1 tidstidsfördröjning eller 2 tidsfördröjningar, Granger orsakar BNP-tillväxt andelen av de totala offentliga utgifterna som är dedikerade till utbildning. Resultat för tester som använder 2 tidsfördröjningar visar också att den enda utbildningsvariabeln som Granger orsakar BNP-tillväxt är inskrivningsgraden på primärnivå, där BNP-tillväxten inte heller Granger orsakar någon utbildningsvariabel förutom procentandelen av de statliga utgifterna som är avsatta till utbildning. I fallet med testet med 3 tidsfördröjningar visar resultaten att BNP-tillväxt Granger orsakar endast en utbildningsvariabel, vilken är utgifter på gymnasienivå.

<p>Causality is central to scientific inquiry across many disciplines including epidemiology, medicine, and economics, to name a few. Researchers are usually interested not only in knowing how two events are correlated, but also in whether one causes the other and, if so, how. In general, the scientific practice seeks not just a surface description of the observed data, but rather deeper explanations, such as predicting the effects of interventions. The answer to such questions does not lie in the data alone and requires a qualitative understanding of the underlying data-generating process; a knowledge that is articulated in a causal diagram.</p> <p>And yet, delineating the true, underlying causal diagram requires knowledge and assumptions that are usually not available in many non-trivial and large-scale situations. Hence, this dissertation develops necessary theory and algorithms towards realizing a data-driven framework for causal inference. More specifically, this work provides fundamental treatments of the following research questions:</p> <p><br></p> <p><strong>Effect Identification under Markov Equivalence.</strong> One common task in many data sciences applications is to answer questions about the effect of new interventions, like: 'what would happen to <em>Y</em> while observing <em>Z=z</em> if we force <em>X</em> to take the value <em>x</em>?'. Formally, this is known as <em>causal effect identification</em>, where the goal is to determine whether a post-interventional distribution is computable from the combination of an observational distribution and assumptions about the underlying domain represented by a causal diagram. In this dissertation, we assume as the input of the task a less informative structure known as a partial ancestral graph (PAG), which represents a Markov equivalence class of causal diagrams, learnable from observational data. We develop tools and algorithms for this relaxed setting and characterize identifiable effects under necessary and sufficient conditions.</p> <p><br></p> <p><strong>Causal Discovery from Interventions.</strong> A causal diagram imposes constraints on the corresponding generated data; conditional independences are one such example. Given a mixture of observational and experimental data, the goal is to leverage the constraints imprinted in the data to infer the set of causal diagrams that are compatible with such constraints. In this work, we consider soft interventions, such that the mechanism of an intervened variable is modified without fully eliminating the effect of its direct causes, and investigate two settings where the targets of the interventions could be known or unknown to the data scientist. Accordingly, we introduce the first general graphical characterizations to test whether two causal diagrams are indistinguishable given the constraints in the available data. We also develop algorithms that, given a mixture of observational and interventional data, learn a representation of the equivalence class.</p>

This study examines the lagged short run and long-term relationships between output growth and related index returns of the industrial and financial sectors of the G-7 economies. This study examines this relationship using quarterly data for a maximum time period of 22 years ranging from 1994(Q4) to 2017(Q4). The relationship between sector specific output growth and related index returns of the G-7 is investigated within this study, in order to determine whether passive investors should incorporate expected growth prospects into their decision making in order to earn superior returns. In order to examine the relationship between sector specific output growth and the related index returns of the G-7, this study uses correlation, cointegration as well as causality testing. This study finds weak non-lagged correlation relationships between output growth and related index returns of the industrial and financial sectors of the G-7 economies, with the correlation relationships becoming stronger in all cases when lags are incorporated within the correlations analysis. This study also finds cointegrating relationships between financial sector output growth and related index returns of Italy and the United Kingdom and that financial index return data of the United Kingdom serves as a leading indicator for financial sector growth within the United Kingdom. The overall Implication of these results is that investors should not incorporate growth prospects into their decision making of which passive funds to invest in, of which these passive funds examined track the performance of industrial and the financial firms within the G-7 economies.

Even before the recent surge in remote work as a result of COVID-19 (US Bureau of Labor Statistics, 2020), there has been a growing trend of employees working from home either entirely (i.e., remote) or working from home a couple of days per week (i.e., telecommuting) (Minton-Eversole, 2012). The goal of the current research is to investigate what type of individuals prefer these types of flexible working arrangements. Specifically, how individual autonomy causality orientation (i.e., ACO, the desire to individuals to act based on their internal volitions) interacts with workplace variables (i.e., workplace location and task interdependence) to impact individual’s perceptions of how the job fulfills their needs (i.e., needs supplies fit, NS fit). Further, how will needs supplies fit (i.e., NS fit) impact important behaviors as part of the job search process such as job attraction. To provide a sample that is more representative of a working population, all participants were employed for an average of at least 20 hours a week. In the current study, individuals were randomly assigned to one of six vignettes in a 2 (i.e., high/low task interdependence) x 3 (i.e., traditional office/telecommute/remote) experimental between-subjects design. Data was collected using an online survey via MTurk. Correlational analysis and hierarchical regression analysis were used to assess this model and compare the relationships between autonomy causality orientation, needs supplies fit, and job attraction in relation to the experimental conditions for workplace location and task interdependence. The current study results suggest those scoring low on ACO tend to perceive moderate levels of NS fit regardless of the job environment conditions. Yet those high on ACO may be more likely to perceive NS fit when presented with job environment conditions that allow high interactions with others as part of their work (i.e., high task interdependence) or virtual work environments (i.e., telecommuting and remote). The positive relationship between job attraction and NS fit individual perceptions of having individual needs supplied by may help to attract more applicants.