Submitted by Marlene Santos (marlene.bc.ufg@gmail.com) on 2016-09-09T14:31:10Z No. of bitstreams: 2 Dissertação - Daniela Vieira de Oliveira - 2016.pdf: 1510275 bytes, checksum: 679036a6f09af67bf9c77fda52973e59 (MD5) license_rdf: 0 bytes, checksum: d41d8cd98f00b204e9800998ecf8427e (MD5) / Approved for entry into archive by Luciana Ferreira (lucgeral@gmail.com) on 2016-09-09T14:35:00Z (GMT) No. of bitstreams: 2 Dissertação - Daniela Vieira de Oliveira - 2016.pdf: 1510275 bytes, checksum: 679036a6f09af67bf9c77fda52973e59 (MD5) license_rdf: 0 bytes, checksum: d41d8cd98f00b204e9800998ecf8427e (MD5) / Made available in DSpace on 2016-09-09T14:35:00Z (GMT). No. of bitstreams: 2 Dissertação - Daniela Vieira de Oliveira - 2016.pdf: 1510275 bytes, checksum: 679036a6f09af67bf9c77fda52973e59 (MD5) license_rdf: 0 bytes, checksum: d41d8cd98f00b204e9800998ecf8427e (MD5) Previous issue date: 2016-08-31 / Fundação de Amparo à Pesquisa do Estado de Goiás - FAPEG / This study analyzes the relationship between municipal spending on education and average educational performance of municipal schools students in Goiás. The analize evaluate how the municipal public spending on primary education per student influences the average scores of municipalities in Prova Brazil in Portuguese and Mathematics. This study use 4th series / 5th year of Goiás municipal public schools datas, among 25% of municipalities with the worst result and 25% of municipalities with best results, using methods of quantile regressions for 2007, 2009 2011 and 2013. The model of quantile regression estimated’s results indicates that the expenditure per student has a positive and significant impact on municipal average scores in all quantiles for both grades, in Portuguese and Mathematics, for municipal schools in Goiás. Moreover, it’s observed that municipalities that have better average (quantile 0.50 and 0.75), positive influence of spending per student tends to be higher. It is noteworthy that the group with 25% better grades has the highest number of municipalities with expenses for students above the state average and Municipal Human Development Index (HDI), literacy rate and GDP per capita above rates presented to state in both disciplines. / O presente trabalho investiga a relação entre gastos públicos municipais em educação e o desempenho escolar médio da rede municipal de ensino goiana. Tal análise é realizada avaliando o quanto os gastos públicos municipais em educação fundamental por aluno influenciam as notas médias dos municípios na Prova Brasil em Língua Portuguesa e em Matemática. São utilizados os dados da Prova a 4º série/5º ano da rede pública municipal goiana, dentre os 25% dos municípios com pior resultado e os 25% dos municípios com melhor resultado, por meio do uso dos métodos de Regressões Quantílicas para os anos 2007, 2009, 2011 e 2013. Os resultados encontrados para o modelo de Regressão Quantílica estimado apontam que o gasto por aluno tem impacto positivo e significativo nas notas médias municipais em todos os quantis tanto para as notas em Língua Portuguesa, quanto para Matemática da rede municipal de ensino fundamental goiana. Além disso, é observado que, nos municípios que apresentam melhores médias (quantil 0,50 e 0,75), a influência positiva do gasto por aluno tende a ser maior. Ressalta-se ainda que, o grupo com as 25% melhores notas apresenta a maior quantidade de municípios com Gastos por alunos acima da média estadual e Índice de Desenvolvimento Humano Municipal (IDH-M), Taxa de Alfabetização e PIB per capita acima dos índices apresentados para o Estado em ambos os anos e disciplinas.

The drivers of CO2 emissions are a widely studied subject of great importance to both individual countries and the global community. However, the inclusion of a quantitative measure of political uncertainty, national and global, has until now been largely overlooked. We investigate how geopolitical uncertainty (GPU) and income interact with CO2 emissions using a panel quantile regression approach for a set of 63 nations over the period 1985-2014. Our key findings are; (i) a consistent negative (positive) relation between global (local) uncertainty and the different CO2 emission distribution levels, (ii) the relation between uncertainty and emissions is heterogeneous across different income groups, (iii) clear and consistent evidence for the Environmental Kuztnet Curve hypothesis with respect to uncertainty, (iiii) when deciding on environmental policy, it is of great importance to consider political uncertainty and whether to use a local or global measure.

本文採用1989年10月2日至2017年4月12日的台灣股市加權指數日資料，並分為漲跌幅限制為7%的區間一，以及放寬為10%的區間二。接著使用〔(最高價-最低價)/昨日收盤價〕以及〔(收盤價-開盤價)/昨日收盤價〕兩個不同變數來衡量台灣股市單日的波動與報酬，然後也運用了週轉率、成交金額、5日均值比三種方法來估算股市成交量。藉此探討台灣股市波動與成交量的關係。使用的方法是分量迴歸模型，更細部的研究股市上漲或下跌時，每個分量之下不同的價量關係。 實證結果顯示，台灣股市普遍存在「價漲量增」與「價跌量增」的現象，且在波動越大的時候也就是分量尾端的部分，其關係更加的明顯。另外，使用三種變數來衡量成交量，在區間二大致得出相同的結論，但是區間一因為週轉率與成交金額的歷史走勢具有差異，所以結果也不盡相同。但是使用週轉率在歷史樣本中更具有相同的比較基礎，因此得出的結論也較一致，所以認為台灣股市仍是以「價漲量增」與「價跌量增」為普遍現象。 / This paper used the Taiwan stock market index daily data from October 2, 1989 to April 12, 2017, which divided into a range of 7% of the price limit, and a range of 10%. There are two different variables to measure the volatility and return: [(the highest price - the lowest price) / yesterday's closing price] and [(closing price - opening price) / yesterday's closing price], and three different variables: turnover, dealing amount, 5-day average ratio to estimate the stock market volume. The method used is quantile regression model, and that allows us to observe different relationship between volatility and volume under every single quantile. Empirical results show that there are two phenomena exist in the stock market of Taiwan: "rising values increase in volume" and "falling values increase in volume." In addition, the use of three variables to measure the volume, in the interval 2 roughly come to the same conclusion, but in the interval 1 because the historical trend of turnover rate and dealing amount are different, so the results are not the same. But the use of turnover in the history sample has the same comparison basis, so the conclusions are more consistent, so that the Taiwan stock market is still the " rising values increase in volume " falling values increase in volume" as a common phenomenon.

This descriptive study examines the gender pay gap across the income distribution in metropolitan and nonmetropolitan areas in the United States in two periods in the 2000’s. In metro areas, the raw gender pay gapgrows larger in the top of the income distribution. In non-metro areas however, the raw gender pay gap isrelatively even in the upper tail of the distribution and does not show this accelerating pattern. Moreover,the study takes a quantile regression approach to measure the adjusted gender pay gaps conditional onhuman capital variables. Comparisons show that the raw gender pay gap has decreased over time, while thecorresponding adjusted gender pay gap has increased over time. This seems to be explained mainly by theincrease in women’s educational attainment, but also convergence of men’s and women’s work experience.In non-metro areas, this generates an adjusted gap that is substantially bigger in the top quantiles in thelatter period. In metro areas, the pattern of a successively widening gap in the top of the distribution persists.

Soil salinization is of great concern in the irrigated arid and semi-arid western United States due to its threat to sustainable agricultural productivity and thus is closely monitored. A widely accepted and traditional standard method for estimating soil salinity is the electrical conductivity of the saturated paste extracts (ECe). However, this method underestimates salinity due to ion pair formation in high ionic strength solution. Numerous studies have recommended the use of an electromagnetic induction (EMI) sensing technique to monitor field-scale soil salinity due to rapidness and non-destructiveness of the sampling. However, because the EMI measurement (ECa) is related to a host of soil properties, calibrating ECa to salinity in a non-homogeneous setting is particularly challenging. The main objective of this study is to understand the dynamics and interacting factors controlling soil salinity using an EMI sensor. Specifically, a correction is made for the underestimation of soil salinity from saturated paste extracts, and a calibration model is developed that is capable of predicting salinity directly from ECa despite the non-homogeneity of potential perturbing factors. A comparison is made of salinity measurement methods based on soil saturated pastes with respect to specific soil management goals. Results show that ion pairing exists even in low ionic strength solution and by diluting the saturated paste extracts to conductivities ≤ 0.03 dS m -1 (ECed), ion pairing is minimized. An improved salinity estimate is obtained by computing total dissolved solids (TDS, in mM) from the ECed values, and then multiplying the TDS by the dilution factor. We also developed a calibration model using quantile regression, which makes no assumption about the distribution of the errors, and which is capable of predicting low range soil salinity (such as that in calcareous soils) from ECa depth-weighted measurements (ECH25ECe). A comparison of ECe, ECed, ECH25ECe, and direct measurement of EC in soil pastes (“ Bureau of Soils Cup ” method, ECcup) across six depths, three texture groups, and the combinations of EC method and depth or texture groups, supports the use of the ECH25ECe method to rapidly and reliably monitor salinity in calcareous soils of arid and semiarid regions.

To resolve the wage-risk trade off relationship on the labor market in Czech Republic, we introduce multiple hedonic wage regressions. Empirical theory ad- mits an income and age heterogeneity in value of a statistical life (VSL). This thesis employs a quantile regression along with age-dependent non-fatal and fa- tal on-the-job risk rates to estimate the age and income variation in VSL within a unified framework. Our results, based on EU-SILC 2018 data, implicate an inverted-V-shaped development of VSL with respect to age. The estimates of age-VSL peak for workers within the age cohort 42-47 across most real wage quantile levels and once reaching the maximum point the VSL proceeds to de- cline with age. In order to infer any effects of the global pandemic on VSL, we propose a set of novel COVID-19 control variables. Additionally, we annuitize the VSL estimates, which yields the value of a statistical life year (VSLY). The measures of VSLY correspond to the age and income varying trend of VSL. In conclusion, this thesis offers applicable varying VSL estimates across cohorts and wage distribution to policy-makers and respective authorities. JEL Classification J17, J24, J28, J31, J33 Keywords hedonic wage, compensating wage differential, quantile regression, VSL, income elasticity Title Measuring the...

To avoid the worst effects of climate change, we need to end our reliance on fossil fuels and invest in alternative, renewable sources. Despite making up only 25% of total renewable energy investments, public investment is still vital due to its ability to encourage investments through policy measures and programs. In this paper we investigate how institutional quality, together with other financial, economic and policy variables, affects public renewable energy investments in middle-income countries. We account for the non-normal distribution of our data by conducting a panel quantile regression analysis for 64 middle-income countries. We present the results for the 0.25, 0.50 and 0.75 quantiles, representing countries with low, moderate, or high levels of public renewable energy investments. Our main finding is that absence of corruption is a vital factor for public renewable energy investments, regardless of which stage of the renewable energy transition a middle-income country is in. In the light of corruption, potential market failures are discussed as a consequence which can create a downward spiral for further renewable energy investments. Furthermore, better financial stability will increase these investments as well. We also find that public investors seem to be unaffected by economic policy uncertainty, indicating that these investors play an important role in uncertain times when private investors refrain from investing due to the high uncertainty connected to the irreversibility of these investment projects.

The gender wage gap in the labour market has long been a topic of study, highlighting the disadvantages faced by women in terms of earningscompared to men. This study aims to investigate if the Great Recession had additional impacts on women's earnings differentials. Using census data from 2006 to 2012 in the US, two different quantile regressions were conducted for various income quantiles. One regression excluded variables, while the other included socio-demographic characteristics. The results indicate clear wage differences for women before, during, and after the Great Recession.The first regression shows statistically significant negative correlations between logarithmic income and gender. The quantile regressions also reveal decrease in the gender wage gap during the recession, with education returns favouring women in 2008 and 2009 before returning to pre-recession levels. Additionally, the results suggest that married women and women with children tend to have lower earnings compared to their male counterparts.These findings provide evidence of a glass ceiling in the US labour market, which may have been exacerbated by the exogenous shock of the Great Recession.

With a background in EU regulations and an increased interest in Environmental, Social, and Governence (ESG) policies in companies when investing, this thesis considers the individual contributions to market risk in portfolios by different ESG parameters. It explores two different methods to examine if there are effects consistent across the whole Nordic markets, and the possibility to express any effects within portfolios in a clear way. It uses the OMXNORDIC index as the market index and two different fund portfolios as example portfolios, one of which is an article 9 fund. The quantile regression approach does not show any consistent effects across the whole Nordic market from any ESG parameter explored. It does however make for a clear way to present the effects on the portfolio level for each ESG parameter. The employed Copula approach does show some consistent difference between the ESG parameters for the market and in portfolios, as well as differences between the portfolios. Both of the explored methods should allow for comparisons between, and reports on, fund portfolios which would improve the ESG analyses of funds. / Mot bakgrund av EU-lagstiftning och ett ökat intresse i företags förhållning till Environmental, Social, och Governence (ESG) frågor, så utforskar den här uppsatsen ESG-faktorers bidrag till marknadsrisk i fondportföljer och på den nordiska marknaden. Uppsatsen använder två olika metoder för att undersöka om det finns potentiella konsekventa effekter på den Nordiska aktiemarknaden, och möjligheten att presentera resultat på portföljnivå på ett tydligt sätt. OMXNORDIC används som marknadsindex, och två olika fondportföljer används som exempelportföljer, varav en är en artikel 9 fondportfölj. Quantile regression-metoden visar inte på några konsekventa effekter över hela den nordiska marknaden, för någon av ESG-parametrarna. Däremot så resulterar metoden i ett tydligt sätt att presentera påverkan av ESG-parametrarna på portföljnivå. Copula-metoden som används visar på några konsekventa skillnader mellan ESG-parametrar, både för marknaden och i fondportföljerna, samt skillnader mellan portföljerna i sig. Båda metoderna lämpar sig till att jämföra och bygga rapporter på fondportföljer, vilket borde leda till bättre ESG-analyser av fonder.

Time series anomaly detection receives increasing research interest given the growing number of data-rich application domains. Recent additions to anomaly detection methods in research literature include deep learning algorithms. The nature and performance of these algorithms in sequence analysis enable them to learn hierarchical discriminating features and time-series temporal nature. However, their performance is affected by the speed at which the time series arrives, the use of a fixed threshold, and the assumption of Gaussian distribution on the prediction error to identify anomalous values. An exact parametric distribution is often not directly relevant in many applications and it’s often difficult to select an appropriate threshold that will differentiate anomalies with noise. Thus, implementations need the Prediction Interval (PI) that quantifies the level of uncertainty associated with the Deep Neural Network (DNN) point forecasts, which helps in making a better-informed decision and mitigates against false anomaly alerts. To achieve this, a new anomaly detection method is proposed that computes the uncertainty in estimates using quantile regression and used the quantile interval to identify anomalies. Similarly, to handle the speed at which the data arrives, an online anomaly detection method is proposed where a model is trained incrementally to adapt to the concept drift that improves prediction. This is implemented using a window-based strategy, in which a time series is broken into sliding windows of sub-sequences as input to the model. To adapt to concept drift, the model is updated when changes occur in the new arrival instances. This is achieved by using anomaly likelihood which is computed using the Q-function to define the abnormal degree of the current data point based on the previous data points. Specifically, when concept drift occurs, the proposed method will mark the current data point as anomalous. However, when the abnormal behavior continues for a longer period of time, the abnormal degree of the current data point will be low compared to the previous data points using the likelihood. As such, the current data point is added to the previous data to retrain the model which will allow the model to learn the new characteristics of the data and hence adapt to the concept changes thereby redefining the abnormal behavior. The proposed method also incorporates feature extraction to capture structural patterns in the time series. This is especially significant for multivariate time-series data, for which there is a need to capture the complex temporal dependencies that may exist between the variables. In summary, this thesis contributes to the theory, design, and development of algorithms and models for the detection of anomalies in both static and evolving time series data. Several experiments were conducted, and the results obtained indicate the significance of this research on offline and online anomaly detection in both static and evolving time-series data. In chapter 3, the newly proposed method (Deep Quantile Regression Anomaly Detection Method) is evaluated and compared with six other prediction-based anomaly detection methods that assume a normal distribution of prediction or reconstruction error for the identification of anomalies. Results in the first part of the experiment indicate that DQR-AD obtained relatively better precision than all other methods which demonstrates the capability of the method in detecting a higher number of anomalous points with low false positive rates. Also, the results show that DQR-AD is approximately 2 – 3 times better than the DeepAnT which performs better than all the remaining methods on all domains in the NAB dataset. In the second part of the experiment, sMAP dataset is used with 4-dimensional features to demonstrate the method on multivariate time-series data. Experimental result shows DQR-AD have 10% better performance than AE on three datasets (SMAP1, SMAP3, and SMAP5) and equal performance on the remaining two datasets. In chapter 5, two levels of experiments were conducted basis of false-positive rate and concept drift adaptation. In the first level of the experiment, the result shows that online DQR-AD is 18% better than both DQR-AD and VAE-LSTM on five NAB datasets. Similarly, results in the second level of the experiment show that the online DQR-AD method has better performance than five counterpart methods with a relatively 10% margin on six out of the seven NAB datasets. This result demonstrates how concept drift adaptation strategies adopted in the proposed online DQR-AD improve the performance of anomaly detection in time series. / Petroleum Technology Development Fund (PTDF)