This study examines the potential effects of categorical increases in production expenditures on farm income performance according to farm standing. The objective of this study is to expose differences in anticipated net farm income return from production expenditure investments and the optimal expense allocation strategy for each performance level. Studying farm performance through segregation by utilizing a two-tier analysis and quantile regression acknowledges the possibility that managerial strategy can differ based on managerial ability. Study outcomes are useful to farm managers because they offer more prescription style results and interpretations than found in other farm performance studies. Study findings show that as managerial proficiency increases so does a manager’s ability to extract higher returns from additional expenditures in certain input categories. Additionally, better managers are able to produce higher returns from more investment sources than their lower performing peers. Overall, study results and interpretations point to the importance of farm management ability as the key input for improving farm performance.

Establishing a causal relationship between health and productivity is not straightforward. On one hand, higher income individuals invest more in health: as their income grows, they invest in better diets and health care. On the other, if a worker is healthier and more energetic, then she will probably be more productive. This paper focuses on the second pathway and examines the effect of one dimension of health, height and body mass index (BMI), on wages. Data comes from a longitudinal study conducted in Guatemala, a low-income country, during 1969-1977 and followed-up in 2002-2004. The estimates suggest a very non-linear relationship between height, BMI and wages; however, the evidence is stronger for males than for females. While diminishing returns are operating at higher quantiles of the conditional wage distribution, increasing returns appear at lower quantiles, implying that height and BMI might have an increasing payoff for the poorer workers.

La literatura empírica ha encontrado evidencia de una tendencia hacia el envejecimiento de la población en América Latina. Este documento analiza el impacto de los cambios demográficos sobre la pobreza utilizando las proyecciones demográficas de la Organización de las Naciones Unidas junto con distintos escenarios en la estructura educativa. La metodología utilizada en este trabajo es la de microsimulaciones econométricas. Su principal innovación consiste en proponer el método de máxima verosimilitud empírica como estrategia de simulación de ponderadores. Bajo todos los supuestos del modelo de simulación, los resultados sugieren que si la dinámica poblacional se mantiene los niveles de pobreza se verán reducidos. Sin embargo el efecto cuantitativo es muy débil, dejando un amplio margen para la planificación de políticas económicas orientadas a la reducción de la pobreza.

This dissertation focuses on (river) forecasting, but also includes a study on stormwater treatment. Using forecasts for decision-making is complicated by their inherent uncertainty. An interview-based study qualitatively and a survey empirically investigate forecast use in emergency management. Emergency managers perceive uncertainty as a given rather than as a problem. To cope with the uncertainty, decision-makers gather as much information as possible; forecasts are only one piece of information among many. For decision-making, emergency managers say that they rely more on radar than on river forecasting. However, forecasts play an important role in communication with the public, because they are the official interpretation of the situation. Emergency managers can add a lot of value to those forecasts by combining them with local knowledge, but might not do so because of accountability concerns. Forecasts must have value to emergency managers, because those with more work experience rely more on them than those without. Another study further develops the application of quantile regression to generate probabilistic river forecasts. Compared to existing research, this study includes a larger number of river gages; includes more independent variables; and studies longer lead times. Additionally, it is the first to apply this method to the U.S. American context. It was found that the model has to be customized for each river gage for extremely high event thresholds. For other thresholds and across lead times, a one-size-fits-all model suffices. The model performance is robust to the size of the training dataset, but depends on the year, the river gage, lead time and event threshold that are being forecast. An additional study considers the robustness of stormwater management to the amount of runoff. Impervious surfaces, such as roads and parking lots, can increase the amount of runoff and lead to more pollution reaching streams, rivers, and lakes. Best Management Practices (BMPs) reduce the peak discharge into the storm sewer system and remove pollutants such as sediments, phosphorus and nitrogen from the stormwater runoff. Empirically, it is found that BMP effectiveness decreases sooner, steeper and deeper with increasing sizes of storm events than assumed in current computer models.

Learning from data, especially ‘Big Data’, is becoming increasingly popular under names such as Data Mining, Data Science, Machine Learning, Statistical Learning and High Dimensional Data Analysis. In this dissertation we propose a new related field, which we call ‘United Nonparametric Data Science’ - applied statistics with “just in time” theory. It integrates the practice of traditional and novel statistical methods for nonparametric exploratory data modeling, and it is applicable to teaching introductory statistics courses that are closer to modern frontiers of scientific research. Our framework includes small data analysis (combining traditional and modern nonparametric statistical inference), big and high dimensional data analysis (by statistical modeling methods that extend our unified framework for small data analysis). The first part of the dissertation (Chapters 2 and 3) has been oriented by the goal of developing a new theoretical foundation to unify many cultures of statistical science and statistical learning methods using mid-distribution function, custom made orthonormal score function, comparison density, copula density, LP moments and comoments. It is also examined how this elegant theory yields solution to many important applied problems. In the second part (Chapter 4) we extend the traditional empirical likelihood (EL), a versatile tool for nonparametric inference, in the high dimensional context. We introduce a modified version of the EL method that is computationally simpler and applicable to a large class of “large p small n” problems, allowing p to grow faster than n. This is an important step in generalizing the EL in high dimensions beyond the p ≤ n threshold where the standard EL and its existing variants fail. We also present detailed theoretical study of the proposed method.

Title: Statistical inference based on saddlepoint approximations Author: Radka Sabolová Abstract: The saddlepoint techniques for M-estimators have proved to be very accurate and robust even for small sample sizes. Based on these results, saddle- point approximations of density of regression quantile and saddlepoint tests on the value of regression quantile were derived, both in parametric and nonpara- metric setup. Among these, a test on the value of regression quantile based on the asymptotic distribution of averaged regression quantiles was also proposed and all these tests were compared in a numerical study to the classical tests. Finally, special case of Kullback-Leibler divergence in exponential family was studied and saddlepoint approximations of the density of maximum likelihood estimator and sufficient statistic were also derived using this divergence. 1

This thesis is an attempt to bridge some research gaps in the area of behavioural finance and investment through adopting the three essays scheme of PhD dissertations. There is a widespread belief that the traditional finance theory failed to provide a sufficient and plausible explanation for (1) what motivates individual investors to trade, (2) the pattern of their trading and the formation of their portfolios, (3) the determinants of cross section of expected returns other than risk. Behavioural Finance, however, offers more realistic assumptions based on two building blocks; behavioural biases of irrational investors and the limits of arbitrage that prevent the arbitrageurs from correcting mispricing and pushing prices back to fundamental values. This dissertation is structured as follows: In the first essay, the disposition effect is defined as the propensity of investors to realize gains too early while being loath to realize losses. Capital gains overhang is a measure of unrealized capital gains and losses that is associated with the disposition effect and the trading activities of behaviourally biased investors. We discover that firm characteristics can play a role in explaining variations in the capital gains overhang that is consistent with the activities of behaviourally biased and disposition investors. Specifically, we find that capital gains overhang is increasing in firm attributes that attract behaviourally biased investors, namely, earnings per share, leverage, growth and size. Capital gains overhang is also declining in market liquidity, possibly because liquidity allows behaviourally biased investors to excessively trade shares and beta and corporate earnings, probably because when high risk and inefficient firms experience losses, disposition investors experience capital losses that they are reluctant to realize. In the second essay, quantile regressions are employed to analyse the relationship between the unrealized capital gains overhang and expected returns. The ability of the disposition effect to generate momentum is also considered for the extreme expected return regions (0.05th) and (0.95th) quantiles. To do so, 450,617 observations belonging to 5176 US firms are employed, covering a time span from January 1998 to June 2015. Following the methodology of Grinblatt and Han (2005), the findings show significant differences across various quantiles in terms of signs and magnitudes. These findings indicate a nonlinear relationship between capital gains overhang and expected returns since the impact of capital gains overhang as a proxy for disposition effect on expected returns vary across the expected return distribution. More precisely, the coefficients of capital gains overhang are significantly positive and decline as the expected returns quantiles increase from the lowest to the median expected return quantiles. However, they become significantly negative and rise with the increase in expected returns quantiles above median expected returns quantiles. The findings also suggest that the disposition effect is not a good noisy proxy for momentum at the lowest expected return quantile (0.05th). However, interestingly it seems to generate contrarian in returns at the highest expected returns quantile (0.95th). In the third essays, we try to discover systematic disagreements in momentum, asymmetric volatility and the idiosyncratic risk momentum return relationship between high-tech stocks and low-tech stocks. We develop several hypotheses that suggest greater momentum profits, fainter asymmetric volatility and weaker idiosyncratic risk-momentum return relation in the high-tech stocks relative to the low tech stocks. To this end, we divide 5795 stocks that are listed in the Russell 3000 index from January 1995 to December 2015 into two samples SIC code and analysed them using the Fama French with GJR-GARCH-M term. The results show that the high-tech stocks provide greater momentum profits especially for portfolios that have holding and ranking periods of less than 12 months. In most cases momentum returns in the high-tech stocks explain a symmetric response to good and bad news while the momentum returns in the low-tech stocks show an asymmetric response. Finally, the idiosyncratic risk-momentum return relation is insignificant for high-tech stocks while it is significant and negative for low-tech stocks. That is, as idiosyncratic risk increases, momentum decreases for low-tech stocks. These findings are robust to different momentum strategies and to different breakpoints.

vi Abstract Proper understanding of the dependence between assets is a crucial ingredient for a number of portfolio and risk management tasks. While the research in this area has been lively for decades, the recent financial crisis of 2007-2008 reminded us that we might not understand the dependence properly. This crisis served as catalyst for boosting the demand for models capturing the dependence structures. Reminded by this urgent call, literature is responding by moving to nonlinear de- pendence models resembling the dependence structures observed in the data. In my dissertation, I contribute to this surge with three papers in financial econo- metrics, focusing on nonlinear dependence in financial time series from different perspectives. I propose a new empirical model which allows capturing and forecasting the conditional time-varying joint distribution of the oil - stocks pair accurately. Em- ploying a recently proposed conditional diversification benefits measure that con- siders higher-order moments and nonlinear dependence from tail events, I docu- ment decreasing benefits from diversification over the past ten years. The diver- sification benefits implied by my empirical model are, moreover, strongly varied over time. These findings have important implications for asset allocation, as the benefits of...

This thesis analyses financial contagion between a reference EU market - Germany and markets of five countries which are actively seeking to become a part of European Union - Montenegro, Serbia, Turkey, Bosnia and Macedonia in the period of March 2006 to March 2018. We apply quantile regression framework to analyse contagion which we base on the occurrence and degree of coexceedances between the reference and analysed market. The results indicate that contagion between stock markets exists, however in different degree for each of the analysed markets. In addition we apply the regression framework specifically for period of financial crisis of 2008 to demonstrate that contagion is stronger during turbulent market periods. JEL Classification G01, G14, G15 Keywords coexceedance, quantile regression, contagion, stock markets Author's e-mail 80605682@fsv.cuni.cz Supervisor's e-mail roman.horvath@fsv.cuni.cz

Dans la thèse on introduit et on étudie une généralisation spatiale sur $\R^d$ du quantile réel usuel sous la forme d'une surface quantile via des formes $\phi$ et d'un point d'observation $O$. Notre point de départ est de simplement admettre la subjectivité due à l'absence de relation d'ordre totale dans $\R^d$ et donc de développer une vision locale et directionnelle des données. Ainsi, les observations seront ordonnées du point de vue d'un observateur se trouvant à un point $O \in \R^d$. Dans le chapitre 2, on introduit la notion du quantile vue d'un observateur $O$ dans la direction $u \in \Sd$ et de niveau $\alpha$ via des des demi-espaces orthogonaux à chaque direction d'observation. Ce choix de classe implique que les résultats de convergence ne dépendent pas du choix de $O$. Sous des hypothèses minimales de régularité, l'ensemble des points quantile vue de $O$ définit une surface fermée. Sous hypothèses minimales, on établit pour les surfaces quantile empiriques associées les théorèmes limites uniformément en le niveau de quantile et la direction d'observation, avec vitesses asymptotiques et bornes d'approximation non-asymptotiques. Principalement la LGNU, la LLI, le TCLU, le principe d'invariance fort uniforme puis enfin l'approximation du type Bahadur-Kiefer uniforme, et avec vitesse d'approximation. Dans le chapitre 3, on étend les résultats du chapitre précédent au cas où les formes $\phi$ sont prises dans une classe plus générale (fonctions, surfaces, projections géodésiques, etc) que des demi-espaces qui correspondent à des projections orthogonales par direction. Dans ce cadre plus général, les résultats dépendent fortement du choix de $O$, et c'est ce qui permet de tirer des interprétations statistiques. Dans le chapitre 4, des conséquences méthodologiques en statistique inférentielle sont tirées. Tout d'abord on introduit une nouvelle notion de champ de profondeurs directionnelles baptisée champ d'altitude. Ensuite, on définit une notion de distance entre lois de probabilité, basée sur la comparaison des deux collections de surfaces quantile du type Gini-Lorrentz. La convergence avec vitesse des mesures empiriques pour cette distance quantile, permet de construire différents tests en contrôlant leurs niveaux et leurs puissances. Enfin, on donne une version des résultats dans le cas où une information auxiliaire est disponible sur une ou plusieurs coordonnées sous la forme de la connaissance exacte de la loi sur une partition finie. / The main issue of the thesis is the development of spatial generalizations on $\R^d$ of the usual real quantile. Facing the usual fact that $\R^d$ is not naturally ordered, our idea is to simply admit subjectivity and thus to define a local viewpoint rather than a global one, anchored at some point of reference $O$ and arbitrary shape $\phi$ with the motivation of crossing information gathered by changing viewpoint $O$, shape $\phi$ and $\alpha$-th order of quantile. In Chapter 2, we study the spatial quantile points seen from an observer $O$ in a direction $u \in \Sd$ of level $\alpha$ through the class of the half-spaces orthogonal to the direction $u$. This choice implies that the convergence theorems do not depend on the choice of $O$. Under minimal regularity assumptions, the set of all quantile points seen from $O$ is a closed surface. Under minimal assumptions, we establish for the associated empirical quantile surfaces the convergence theorems uniformly on the quantile level and the observation direction with the asymptotic speed and non-asymptotic bounds of approximation. Mainly, we establish the ULLN, the ULIL, the UCLT, the uniform strong invariance principle and finally the Bahadur-Kiefer type embedding, with the approximation speed rate. In Chapter 3, all the results of the previous chapter are extended to the case where the shapes $ \phi $ are taken in a class more general (functions, surfaces, geodesic projections, etc) than orthogonal projections (half-spaces). In this general setting, the results depend strongly on the choice of $ O $. It is this dependence which permit to draw statistical interpretations: modes detection, mass localization, etc. In Chapter 4, some methodological consequences in inferential statistics are drawn. First we introduce a new concept of directional depth fields called altitude fields. In a second application is defined a new distances between probability distributions, based on the comparison of two collections of quantile surfaces, which are indexes of the type Gini-Lorrentz. The convergence with speed of the empirical quantile measures for these distances, can build different tests with control of their level and their power. A third use of the quantile surfaces is for the case where $ \alpha = 1/2$. Finally, we give a version of our theorems in the case where auxiliary information is available on one or more coordinates of the random variable. By assuming known the probability of the elements of a finite partition, the asymptotic variance of the limiting process decreases and the simulations with few points clearly shows the reframe of the estimated surfaces to the real ones.