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.

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.

No description available.

No description available.

This study aims to investigate if there is any causal relationship between human rights and economic growth. The causality is tested in both directions, from human rights to economic growth and from economic growth to human rights, using a panel data Granger Causality test. The variable used to represent human rights is a human rights score and the variable used to represent economic growth is annual growth of real GDP per capita. Both of these variables are retrieved from Our World in Data. There is a total number of 81 countries included in this study with yearly observations from 1962 until 2017 on both variables. To achieve a greater depth the 81 countries were categorized into three different categories: low-income, middle-income and high-income countries. Previous studies and theories indicate that it is possible to expect a two-way causal relationship between economic growth and human rights. However, the results in this study indicate that there is no statistically significant causal relationship in any direction for any of the income categories.

This thesis aims to analyze some of the available methods that aid in risk estimation based on econometric models, as well as to propose some new ones. Some of the questions that are expected to be answered include which distribution to choose to obtain better risk estimates for series with abnormal behaviours, how to determine whether the distribution in parametric conditional models is a Student’s t, and how to assess whether an asset’s risk helps predict the risk of another asset. In Chapter 1, we estimate several cryptocurrencies’ Expected Shortfall using different error distributions and GARCH-type models for conditional variance. ur goal is to examine which distributions perform better and to check which component of the specification plays a more crucial role in estimating Expected Shortfall. The performance of the estimations is conducted using a backtesting technique with a rolling-window approach. Results show that, in the case of Bitcoin, it is important to use a distribution with at least two parameters that control its shape and an extension of the GARCH model, whether it be the NGARCH or the CGARCH model. On the other hand, other smaller cryptocurrencies yield good enough risk predictions with the Student’s t distribution and a GARCH model. The fact that the main measures of financial risk are focused on the tail of the distribution of returns highlights the importance of the choice of an appropriate distribution model. Chapter 2 develops a procedure for consistently testing the specification of a Student’s t distribution for the innovations of a dynamic model. This contributes to the existing literature by providing a test for Student’s t distributions in conditional mean and variance models with a parameter-free test statistic and, thus, a known asymptotic distribution, avoiding the use of more computationally costly resampling techniques such as bootstrapping. The specific expressions needed for the computation of the test statistic are obtained by adapting the generic test of Bai (2003), which is based on the Khmaladze (1988) transformation of the model residuals. Finally, in Chapter 3, the concept of Granger causality in Expected Shortfall (ES) is introduced, along with a testing procedure to detect this type of predictive relationship between return series. Granger causality in Expected Shortfall is here defined as the predictive ability of tail values of a series over future tail values of another series on average. This definition may help in analyzing whether past values of an asset in extreme risk affect future extreme risk values of another asset. The main contribution of this chapter is a test for detecting this type of causality, based on the test for Granger causality in VaR by Hong et al. (2009). An empirical application on financial institutions from different industries (banking, insurance, and diversified financials) is presented to analyze the risk spillovers in the US financial market. The contribution of this thesis to the field of financial econometrics focuses on the market risk of financial assets, both in its modeling through the metric known as Expected Shortfall suggested in the Basel III Accords and in its utility beyond capital requirements. The results highlight the importance of a good specification of the chosen distribution model for risk estimation - especially in high-risk assets such as cryptocurrencies - and a test is proposed to verify if the conditional distribution in parametric models used for risk predictions is or is not a Student’s t distribution. Finally, a Granger causality test in Expected Shortfall is proposed, which allows for studying risk propagation in tails of return distributions. The proposed test can be used to investigate interconnections within and between markets as a complement when evaluating systemic risk. Other potential applications include improving Expected Shortfall forecasts by including causing variables as regressors in estimations, studying the inclusion of certain asset pairs in the same portfolio based on how they interact in the riskiest situations, or constructing networks of extreme risk propagation. / Esta tesis doctoral ha sido financiada mediante una ayuda FPU por el Ministerio de Educación, Cultura y Deporte (FPU17/06227).

Yes / This study contributes to the existing literature on the relationship between oil market shocks and the green bond market by investigating the impact of the COVID-19 pandemic on their dynamic correlation. We first decompose the oil market shocks into components using a time-frequency framework. Then, we combine wavelet decomposition and quantile coherence and causality methods to discuss changes during the COVID-19 era. We observe positive effects of both supply-driven and demand-driven oil shocks on the green bond market at most quantile levels. However, supply-driven oil price changes play a major role. The results also indicate that long-term changes have a greater impact than short-term changes on the connection between oil and green bond markets. Nevertheless, the COVID-19 pandemic changed the nature of the causal relationship, as we observed no relationship under extreme market conditions during the pandemic era. We argue that the economic and social impacts of the COVID-19 pandemic have left investors focusing on the short-term substitution between oil and green bond markets. / This research was supported by the Major Projects of the National Natural Science Fund of China [NO. 71991483], the Natural Science Fund of Hunan Province [NO. 2022JJ40647] and the Fundamental Research Funds for the Central Universities of Central South University [NO. 2022ZZTS0353].

Civil aviation demands punctual and efficient commercial flights. Flight delays adversely affect passengers, airlines, airports, and the environment (Cook and Tanner, 2015; Cook, Tanner, and Lawes, 2012). Flight delays are typically characterized as the time difference between the actual departure/arrival time of an aircraft and its scheduled departure/arrival time (EUROCONTROL, 2018). Air transportation functions within a complex system and delays are influenced by a multitude of factors. At its core, delays arise due to an imbalance between demand and capacity, where the demand exceeds the available capacity (EUROCONTROL, 2018; Technology Assessment, 1984; Wells and Young, 2004). Air Traffic Flow Management (ATFM) can adjust the demand and balance the imbalance between demand and capacity to achieve a better equilibrium (EUROCONTROL, 2023; Odoni, 1987; Ball et al., 2003; Bertsimas, Lulli, and Odoni, 2011; Murca, 2018; Xu et al., 2020). This dissertation encompasses applications of statistical methods in air transport, such as landing time predictions and weather variable interpolations to enhance ATFM, as well as delay propagation inferences among airports to comprehend patterns of delay transmission, all aiming to understand and mitigate flight delays. Efficient ATFM requires accurate monitoring and prediction of the current capacity and demand imbalance status. Accurate prediction of flight delay helps airports to monitor better, make more informed decisions and increase airport efficiency (Fricke and Schultz, 2009; Lordan, Sallan, and Valenzuela-Arroyo, 2016; Wang et al., 2021). Besides delay prediction, landing time prediction also improves resource monitoring. Many machine learning methods are available to make predictions of landing time. Chapter 2 compares the accuracy of different machine learning methods to predict landing time at Zurich Airport by cross- validation errors. Important factors contributing to the landing time prediction are also identified. The results showcase the effectiveness of the decision tree methods in accurately predicting landing times, which helps improve the management of runways and resources at the local airport. Besides a warning of delays, rerouting can prevent delays by exploring alternative flight routes, which involves re-planning trajectories to bypass congested airspace and hotspots. Weather information serves as a critical input for trajectory planners. The question pertains to choosing interpolation methods to extend the weather data available at 1-degree grid points defined by latitudes, longitudes, and pressure levels with high accuracy. Chapter 3 explores different interpolation techniques for crucial weather variables such as temperature, wind speed, and wind direction. These methods, including Ordinary Kriging, the radial basis function method, neural networks, and decision trees, are compared using cross-validation interpolation errors. A Monte Carlo simulation of a trajectory from Prague to Tunis is conducted to examine the impact of input weather data and the interpolation method (Ordinary Kriging) on planned trajectories. Even though errors in GFS data and Ordinary Kriging are inevitable, the inaccuracy of the data has a minor impact on the planned trajectory. Flight delays negatively affect passengers, airlines, airports, and the environment. Besides mitigating delays at individual airports and for specific flights, considering the potential propagation of delays from other airports is necessary. Assessing delay propagation among airports in the network contributes to understanding the systemic impact of delays. Analyzing delay propagation assists in understanding the patterns of delay transmission and identifying potential strategies for mitigation. Graph network theory has enabled the construction of delay propagation networks to understand the delay transmission pattern using time series data (Belkoura and Zanin, 2016; Zanin, Belkoura, and Zhu, 2017; Du et al., 2018; Mazzarisi et al., 2020b; Xiao et al., 2020; Wang et al., 2020; Jia et al., 2022). However, inferring connections from time series data using statistical methods can introduce biases resulting from excluding airports (Belkoura and Zanin, 2016; Zanin, Belkoura, and Zhu, 2017; Du et al., 2018) or false positives by inappropriate statistical methods (Mazzarisi et al., 2020b), consequently overestimating propagation. Overestimation of delay propagation can undermine the credibility of the reported results, as it becomes dubious to discern whether inaccurate inferences drive the observed delay propagation. Chapter 4 infers Granger causality among airports by avoiding the overestimation of propagation from excluding airports and false positives. The “one-standard-error” rule (Hastie et al., 2009) is recommended to mitigate a high false positive rate during parameter tuning. It is found that the choice of data inputs for model training influences the delay propagation inference results. When early arrivals and punctual flights are included, the observed delay propagation among airports can stem from correlations among punctual and early arrivals rather than delayed flights. In contrast to recent research (Xiao et al., 2020; Jia et al., 2022), this study unveils that large airports exert a substantial influence on the delay propagation network. In summary, this work aims to enhance our understanding of and mitigate flight delays. Chapters 2 and 3 focus on delay mitigation, while Chapter 4 contributes to our understanding of delay interactions.

自從中國實施經濟改革之後，其經濟快速成長。從1978到2002年之間，中國的年平均成長率為8.07﹪。但同時期，中國的不平等卻顯示不同的變動趨勢。在1980年代，經濟改革似乎同時促進經濟成長與不平等程度的下降。然而1990年代之後，不平等卻呈現向上爬升的趨勢。本論文的研究目的，即是探討改革後，中國經濟成長與不平等間的因果關係。 根據之前的文獻，經濟成長和不平等之間可能相互影響，但影響方向卻不確定。本論文研究方法採用Granger因果檢定，估計成長與分配的因果關係以及影響方向。本研究採用Toda and Yamamoto (1995)所提出的向量自迴歸程序，對Granger因果模型進行卡方檢定的統計推論。Toda和Yamamoto證明，研究者能夠估計一個k+dmax階的向量自迴歸模型，其中dmax是時間數列變數最大整合階數，k為落差期數。然而，進行統計推論時，研究者只需利用卡方統計量檢驗前k階的迴歸係數是否顯著，而不需檢驗最後的dmax階迴歸係數。利用此研究方法，本論文發現以下結果：一、經濟成長會正面且顯著地影響不平等；二、不平等不會影響到經濟成長；三、實證結果是穩健的，其不因使用不同的所得不平等指數或落差期數而有所變化。 會產生第一種結果的主要原因，在於中央政府傾斜政策的實施。與先前文獻完全集中研究自由經濟或計畫經濟不同，中國經濟正處於轉型過程，可能是本研究不能發現所得不均等對經濟成長的主要因素。 本研究的政策意涵為，由於經濟持續增長，使得不均等的情況更加惡化。因此，中央政府應該取消向東部沿海傾斜的政策，並且增加對中西部地區進行投資的意願。但是，將資源投入在中西部，使得到的收益遠遠小於投入在東部者。因此為了促進持續經濟成長，不建議採取某些能迅速降低不平等的政策，例如財政移轉。 / Ever since economic reform has been carried out in China, its economic growth rate has been remarkable. Its annual growth rate of per capita GDP was about 8.07% for the period 1978-2002, but its income inequality level presented a different moving trend during this time. In the 1980s, it seemed that economic reform decreased this inequality successfully, but the situation of income distribution started to deteriorate beginning in 1990. The purpose of this study is to research if the relationship between economic growth and inequality exists in post-reform China. According to previous literature, economic growth and inequality can influence each other, but their influential directions are uncertain. This study adopts the Granger-causality test as a methodology to estimate their relationship and influential directions. This study tests Granger-Causality with the chi-square statistic, which was proposed by Toda and Yamamoto (1995). They wrote that researchers could estimate a (k+dmax)th-order VAR where dmax is the maximal order of integration. Only the first k coefficients have to be jointly tested with the chi-square statistic, and the last dmax coefficients are ignored. This study finds the following results: (i) growth positively influences inequality; (ii) inequality does not influence growth; and (iii) the results are sturdy no matter what inequality index or what lag lengths are used in the empirical test. The result (i) can be attributed to the biased central government policy. Differencing from previous studies, which focused on democratic or undemocratic economies, this study researches a transitional economy. It could be the reason why this study finds no evidence of the effect of inequality on growth. The policy implications of this study are that China’s government has to give up biased policies and increase the incentives of investing in inland regions. Besides, in order to promote sustainable economic growth, some policies like fiscal transfers, which can reduce inequality quickly, are not recommended.