My dissertation consists of three essays in three different areas: international trade; agricultural markets; and nonparametric econometrics. The first and third essays are theoretical papers, while the second essay is empirical. In the first essay, I developed a political economy model of trade agreements where the set of policy instruments are endogenously determined, providing a rationale for countervailing duties (CVDs). Trade-related policy intervention is assumed to be largely shaped in response to rent seeking demand as is often shown empirically. Consequently, the uncertain circumstance during the lifetime of a trade agreement involves both economic and rent seeking conditions. The latter approximates the actual trade policy decisions more closely than the externality hypothesis and thus provides scope for empirical testing. The second essay tests whether normal mixture (NM) generalized autoregressive conditional heteroscedasticity (GARCH) models adequately capture the relevant properties of agricultural commodity prices. Volatility series were constructed for ten agricultural commodity weekly cash prices. NM-GARCH models allow for heterogeneous volatility dynamics among different market regimes. Both in-sample fit and out-of-sample forecasting tests confirm that the two-state NM-GARCH approach performs significantly better than the traditional normal GARCH model. For each commodity, it is found that an expected negative price change corresponds to a higher volatility persistence, while an expected positive price change arises in conjunction with a greater responsiveness of volatility. In the third essay, I propose an estimator for a nonparametric additive unconditional quantile regression model. Unconditional quantile regression is able to assess the possible different impacts of covariates on different unconditional quantiles of a response variable. The proposed estimator does not require d-dimensional nonparametric regression and therefore has no curse of dimensionality. In addition, the estimator has an oracle property in the sense that the asymptotic distribution of each additive component is the same as the case when all other components are known. Both numerical simulations and an empirical application suggest that the new estimator performs much better than alternatives. / the Canadian Agricultural Trade Policy and Competitiveness Research Network, the Structure and Performance of Agriculture and Agri-products Industry Network, and the Institute for the Advanced Study of Food and Agricultural Policy.

Cette thèse de doctorat porte sur la dynamique de l’emploi dans les entreprises et sur la relation entre la dynamique de l’emploi et l’innovation, avec une attention particulière portée sur les entreprises nouvellement créées. Cette thèse conceptualise théoriquement et analyse empiriquement les différents aspects de l’interaction complexe entre le changement technologique et la dynamique de l’emploi, en se concentrant sur les effets hétérogènes des différents types d’innovation sur la croissance de l’emploi. Compte tenu le rôle primordial joué par les nouvelles et jeunes entreprises dans le processus de destruction créatrice et leur apport à la création globale de l’emploi, cette thèse fournit une caractérisation de la contribution nette d’emplois des nouvelles entreprises dans un nombre important de pays, en utilisant des données micro-agrégées issues d’une nouvelle base de données. En outre, elle analyse comment un certain nombre de caractéristiques institutionnelles affectent la création nette d’emplois dans les start-ups, en se concentrant sur les effets hétérogènes des politiques sur les nouvelles entreprises et les entreprises déjà existantes. Cette thèse étudie enfin une caractéristique particulière des lois de distribution des taux de croissance de l’emploi, c’est-à-dire la volatilité de la croissance de l’emploi, que non seulement se révèle être une médiation cruciale des effets des politiques sur la création nette d’emplois, mais a aussi d’importantes implications à la fois micro- et macroéconomiques. / This doctoral thesis focuses on employment dynamics in firms, and on the relationship between employment dynamics and innovation, with a particular focus on the entry process. It conceptualizes theoretically and analyses empirically different aspects of the complex interaction between technical change and employment dynamics, focusing on the heterogeneous effects of different types of innovation on employment growth. In the light of the prominent role of newly-born firms in shaping the creative destruction process and contributing to overall job creation, this thesis provides a characterization of the net job contribution by surviving entrants across a significant number of countries. Using newly collected representative micro-aggregated data, it further analyses whether and how a number of institutional characteristics affect start-ups’ net job creation, focusing on the heterogeneous effects of policies on entrants and incumbents. This thesis finally characterizes a particular feature of the employment growth distributions – employment growth volatility – that not only proves to be crucially mediating the effects of policies on entrants’ net job creation, but also has important micro and macroeconomic implications.

Dans cette thèse, nous étudions des tests du type : (H0) : E [U | X] = 0 p.s. contre (H1) : P {E [U | X] = 0} < 1 où U est le résidu de la modélisation d'une variable Y en fonction de X. Dans ce cadre et pour plusieurs cas particuliers – significativité de variables, régression quantile, données fonctionnelles, modèle single-index –, nous proposons une statistique de test permettant d'obtenir des valeurs critiques issues d'une loi asymptotique pivotale. Dans chaque cas, nous donnons également une méthode de bootstrap appropriée pour les échantillons de petite taille. Nous montrons la consistance envers des alternatives locales – ou à la Pitman – des tests proposés, lorsque ce type d'alternative ne tend pas trop vite vers l'hypothèse nulle. À chaque fois, nous vérifions à partir de simulations sous l'hypothèse nulle et sous une séquence d'hypothèses alternatives que les résultats théoriques sont en accord avec la pratique. / In this thesis, we study test statistics of the form : (H0) : E [U | X] = 0 p.s. contre (H1) : P {E [U | X] = 0} < 1 where U is the residual of some Y modeling with respect to covariates X. In this setup and for several particular cases – significance, quantile regression, functional data, single-index model –, we introduce test statistics that have pivotal asymptotic critical values. For each case, we also give a suitable bootstrap procedure for small samples. We prove the consistency against local – or Pitman – alternatives for the proposed test statistics, when such an alternative does not get close to the null hypothesis too fast. Simulation studies are used to check the effectiveness of the theoretical results in applications.

Thesis(M. Com.(Economics)) -- University of Limpopo, 2019 / This study examined the determinants of learner performance by employing an education production function approach using the descriptive statistics, ordinary least squares (OLS) and quantile regression techniques in 2016. The study utilised the data obtained from SA-SAMS of Bankfontein combined school at Mpumalanga province. In the education production function, learner performance was estimated against variables such as age, gender, days absent and socio-economic status. The results of this study indicated that in the rural combined school, learner performance is strongly influenced by age, absenteeism and socio economic status. For instance, results revealed that absenteeism had a negative effect on learners‟ educational performance. An increase in absenteeism by 1 day led to a reduction in learner‟s examination score by approximately 0.1 percentage points during the chosen period. The “socioeconomic status” variable revealed a statistically significant and negative impact on learners‟ educational performance. The results demonstrate that poverty leads to poor educational performance as measured by examination scores. It is recommended that schools should manage learner diversity (age, gender and socio-economic factors), introduce learner motivation programmes, teacher performance improvement interventions, and improve organisational planning and development, parental involvement among others to retain learners at school. Furthermore, schools should enforce education policies that stipulate entry and exit age at different levels of schooling.

En prévision numérique du temps, les modèles de prévision d'ensemble sont devenus un outil incontournable pour quantifier l'incertitude des prévisions et fournir des prévisions probabilistes. Malheureusement, ces modèles ne sont pas parfaits et une correction simultanée de leur biais et de leur dispersion est nécessaire.Cette thèse présente de nouvelles méthodes de post-traitement statistique des prévisions d'ensemble. Celles-ci ont pour particularité d'être basées sur les forêts aléatoires.Contrairement à la plupart des techniques usuelles, ces méthodes non-paramétriques permettent de prendre en compte la dynamique non-linéaire de l'atmosphère.Elles permettent aussi d'ajouter des covariables (autres variables météorologiques, variables temporelles, géographiques...) facilement et sélectionnent elles-mêmes les prédicteurs les plus utiles dans la régression. De plus, nous ne faisons aucune hypothèse sur la distribution de la variable à traiter. Cette nouvelle approche surpasse les méthodes existantes pour des variables telles que la température et la vitesse du vent.Pour des variables reconnues comme difficiles à calibrer, telles que les précipitations sexti-horaires, des versions hybrides de nos techniques ont été créées. Nous montrons que ces versions hybrides (ainsi que nos versions originales) sont meilleures que les méthodes existantes. Elles amènent notamment une véritable valeur ajoutée pour les pluies extrêmes.La dernière partie de cette thèse concerne l'évaluation des prévisions d'ensemble pour les événements extrêmes. Nous avons montré quelques propriétés concernant le Continuous Ranked Probability Score (CRPS) pour les valeurs extrêmes. Nous avons aussi défini une nouvelle mesure combinant le CRPS et la théorie des valeurs extrêmes, dont nous examinons la cohérence sur une simulation ainsi que dans un cadre opérationnel.Les résultats de ce travail sont destinés à être insérés au sein de la chaîne de prévision et de vérification à Météo-France. / In numerical weather prediction, ensemble forecasts systems have become an essential tool to quantifyforecast uncertainty and to provide probabilistic forecasts. Unfortunately, these models are not perfect and a simultaneouscorrection of their bias and their dispersion is needed.This thesis presents new statistical post-processing methods for ensemble forecasting. These are based onrandom forests algorithms, which are non-parametric.Contrary to state of the art procedures, random forests can take into account non-linear features of atmospheric states. They easily allowthe addition of covariables (such as other weather variables, seasonal or geographic predictors) by a self-selection of the mostuseful predictors for the regression. Moreover, we do not make assumptions on the distribution of the variable of interest. This new approachoutperforms the existing methods for variables such as surface temperature and wind speed.For variables well-known to be tricky to calibrate, such as six-hours accumulated rainfall, hybrid versions of our techniqueshave been created. We show that these versions (and our original methods) are better than existing ones. Especially, they provideadded value for extreme precipitations.The last part of this thesis deals with the verification of ensemble forecasts for extreme events. We have shown several properties ofthe Continuous Ranked Probability Score (CRPS) for extreme values. We have also defined a new index combining the CRPS and the extremevalue theory, whose consistency is investigated on both simulations and real cases.The contributions of this work are intended to be inserted into the forecasting and verification chain at Météo-France.

The aim of this thesis is to conduct a nuanced investigation of connectedness in the global futures market across time and market conditions through a Quantile Vector Autoregression (QVAR) model. Later, a linear regression is utilized to identify determinants of futures market connectedness across market conditions. The sample period consists of daily data from December 2017 to August 2023. Our dataset includes five uncertainties and 19 continuous futures contracts, making it the most comprehensive study of futures market connectedness after the Russian invasion of Ukraine to our knowledge.  The results highlight heterogeneous effects across time and market conditions for all assets, with the futures market connectedness increasing during times of uncertainty. US equity, German Equity, Japanese equity, British equity, gold, silver, USD and EUR are identified as net transmitters of spillovers, whereas the rest of the futures are identified as net receivers. These findings are interesting in the concept of theory as they highlight potential periods of flight-to-safety and safe haven properties for certain futures. When including uncertainties in the QVAR model, financial uncertainty is identified as the only net transmitter, whereas the other uncertainties are net receivers.  Drivers of futures market connectedness depend on market conditions and time, with energy uncertainty being significant for normal markets and the world equity index being significant for bearish markets in both the full sample and a Covid-19 subsample. For the full sample only, financialization is identified as a driver during bullish markets. More variables are significant for the Covid-19 subsample. The commodity index and US dollar index becomes significant in bearish markets and monetary uncertainty in bullish markets.  Our findings are relevant for both investors and policymakers. The results suggest that investors should monitor market conditions when investing in the futures market to suitably optimize, diversify, and hedge their portfolios. For policymakers, monitoring spillover from the futures market is important as it can impact the overall economy by using the industrial sector as a transmission channel. This can aid in early decision-making and minimize the impact of economic downturns. / Das Ziel dieser Arbeit ist, eine nuancierte Untersuchung der Verbundenheit im globalen Terminmarkt über Zeit und Marktbedingungen durch ein Quantile Vector Autoregression (QVAR) Modell durchzuführen. Später benutzen wir eine lineare Regression, um Determinanten der Terminmarktverbundenheit unter verschiedene Marktbedingungen zu identifizieren. Der Zeitraum dieser Untersuchung besteht aus täglichen Daten von Dezember 2017 bis August 2023. Die Daten umfasst fünf Unsicherheitsmaße und 19 kontinuierliche Terminkontrakte, damit ist es nach unserem Wissen die umfassendste Untersuchung über die Verbundenheit des Terminmarkts nach der russischen Invasion die Ukraine.  Die Ergebnisse hervorheben heterogene Effekte über Zeit und Marktbedingungen für alle Variablen, wobei die Verbundenheit des Terminmarkts während unsicherer Perioden verstärkt ist. Der amerikanische Aktienindex, deutsche Aktienindex, japanische Aktienindex, britische Aktienindex, Gold, Silber, US-Dollar und Euro werden als Nettoübermittler von Spillovern identifiziert, während die andere Terminkontrakte als Nettoempfänger identifiziert werden. Die Ergebnisse sind interessant im Kontext der Theorie, da sie sowohl potenzielle Perioden von Flight-to-Safety als auch Safe Haven-Eigenschaften für die Terminkontrakte hinweisen. Bei der Einbeziehung von Unsicherheitsmaßen in das QVAR-Modell wird die finanzielle Unsicherheit als einziger Nettoübermittler identifiziert, während die anderen Unsicherheiten Nettoempfänger sind.  Die Determinanten der Verbundenheit an den Terminmarkt sind von Zeit und Marktbedingungen abhängig, wobei die Energieunsicherheit für normale Marktbedingungen und die Weltaktienindex für bärische Marktbedingungen während sowohl des ganzen Zeitraums als auch des Covid-19 Zeitraums signifikant ist. Finanzialisierung ist nur während des ganzen Zeitraums als Determinant für bullische Marktbedingungen signifikant. Im Covid-19 Zeitraum sind weitere Variablen signifikant. Der Rohstoffindex wird in bärische Marktbedingungen und die US-Dollar-Index wird in bullische Marktbedingungen signifikant.  Die Ergebnisse dieser Untersuchung sind sowohl für Investoren als auch für politische und finanzielle Entscheidungsträger relevant. Die Ergebnisse andeuten, dass Investoren die Marktbedingungen beobachten sollten, wenn sie in den Terminmarkt investieren, um ihre Portfolios zu optimieren, diversifizieren und abzusichern. Für politische und finanzielle Entscheidungsträger ist die Beobachtung von Spillover-Effekten vom Terminmarkt wichtig, da sie auf die Gesamtwirtschaft durch die Industriesektor auswirken können. Darum kann diese kontinuierliche Beobachtung zu früheren makroökonomischen Entscheidungen führen und damit ungünstige wirtschaftliche Auswirkungen minimieren.

Der Risikobegriff bezieht sich auf die Wahrscheinlichkeit eines Schadens aufgrund einer Gefährdungsexposition, in der Finanzwelt meist finanzielle Verluste. Viele Risiken der globalen Finanzwirtschaft sind unbekannt. „Wir wissen es, wenn wir es sehen“, um Potter Stewart (1964) zu paraphrasieren. Der Financial Risk Meter (FRM) soll Aufschluss über die Entstehung systemischer Risiken geben. Durch Verwendung von Quantilregressionstechniken ist der FRM nicht nur ein Maß für finanzielle Risiken. Er bietet durch seine Netzwerktopologie einen tiefen Einblick in die Spill-over-Effekte, die sich als systemische Risikoereignisse manifestieren können. Das FRM-Framework wird in verschiedenen Märkten und Regionen entwickelt. Die FRM-Daten werden für Risiko-Prognose sowie für Portfoliooptimierung genutzt. In Kapitel 1 wird der FRM vorgestellt und auf die Aktienmärkte in den USA und Europa, sowie auch auf die Zinsmärkte und Credit-Default-Swaps angewendet. Der FRM wird dann verwendet, um wirtschaftliche Rezessionen zu prognostizieren. In Kapitel 2 wird der FRM auf den Markt der Kryptowährungen angewendet, um das erste Risikomaß für diese neue Anlageklasse zu generieren. Die errechneten FRM-Daten zu Abhängigkeiten, Spillover-Effekten und Netzwerkaufbau werden dann verwendet, um Tail-Risk-optimierte Portfolios zu erstellen. Der Portfoliooptimierungsansatz wird in Kapitel 3 weitergeführt, in dem der FRM auf die sogenannten Emerging Markets (EM)-Finanzinstitute angewendet wird, mit zwei Zielen. Einerseits gibt der FRM für EM spezifische Spillover-Abhängigkeiten bei Tail-Risk-Ereignissen innerhalb von Sektoren von Finanzinstituten an, zeigt aber auch Abhängigkeiten zwischen den Ländern. Die FRM-Daten werden dann wieder mit Portfoliomanagementansätzen kombiniert. In Kapitel 4 entwickelt den FRM for China ist, eines der ersten systemischen Risikomaße in der Region, zeigt aber auch Methoden zur Erkennung von Spill-Over-Kanälen in Nachbarländer und zwischen Sektoren. / The concept of risk deals with the exposure to danger, in the world of finance the danger of financial losses. In a globalised financial economy, many risks are unknown. "We know it when we see it", to paraphrase Justice Potter Stewart (1964). The Financial Risk Meter (FRM) sheds light on the emergence of systemic risk. Using of quantile regression techniques, it is a meter for financial risk, and its network topology offers insight into the spill-over effects risking systemic risk events. In this thesis, the FRM framework in various markets and regions is developed and the FRM data is used for risk now- and forecasting, and for portfolio optimization approaches. In Chapter 1 the FRM is presented and applied to equity markets in the US and Europe, but also interest rate and credit-default swap markets. The FRM is then used to now-cast and predict economic recessions. In Chapter 2 the FRM is applied to cryptocurrencies, to generate the first risk meter in this nascent asset class. The generated FRM data concerning dependencies, spill-over effects and network set-up are then used to create tail-risk optimised portfolios. In Chapter 3 the FRM is applied to the global market Emerging Market (EM) financial institutions. The FRM for EM gives specific spill-over dependencies in tail-risk events within sectors of financial institutions, but also shows inter-country dependencies between the EM regions. The FRM data is then combined with portfolio management approaches to create tail-risk sensitive portfolios of EM Financial institutions with aim to minimize risk clusters in a portfolio context. In Chapter 4 the Financial Risk Meter for China is developed as the first systemic risk meter in the region, but also derives methods to detect spill-over channels to neighbouring countries within and between financial industry sectors.

Recently, variable selection in high-dimensional data has attracted much research interest. Classical stepwise subset selection methods are widely used in practice, but when the number of predictors is large these methods are difficult to implement. In these cases, modern regularization methods have become a popular choice as they perform variable selection and parameter estimation simultaneously. However, the estimation procedure becomes more difficult and challenging when the data suffer from outliers or when the assumption of normality is violated such as in the case of heavy-tailed errors. In these cases, quantile regression is the most appropriate method to use. In this thesis we combine these two classical approaches together to produce regularized quantile regression methods. Chapter 2 shows a comparative simulation study of regularized and robust regression methods when the response variable is continuous. In chapter 3, we develop a quantile regression model with a group lasso penalty for binary response data when the predictors have a grouped structure and when the data suffer from outliers. In chapter 4, we extend this method to the case of censored response variables. Numerical examples on simulated and real data are used to evaluate the performance of the proposed methods in comparisons with other existing methods.

Measures of central tendency have been widely used for summarising statistical data, with the mean being the most popular summary statistic. However, in reallife applications it is not always the most representative measure of central location, especially when dealing with data which is skewed or contains outliers. Alternative statistics with less bias are the median and the mode. Median and quantile regression has been used in different fields to examine the effect of factors at different points of the distribution. Mode estimation, on the other hand, has found many applications in cases where the analysis focuses on obtaining information about the most typical value or pattern. This thesis demonstrates that mode also plays an important role in the analysis of big data, which is becoming increasingly important in many sectors of the global economy. However, mode regression has not been widely applied, even though there is a clear conceptual benefit, due to the computational and theoretical limitations of the existing estimators. Similarly, despite the popularity of the binary quantile regression model, computational straight forward estimation techniques do not exist. Driven by the demand for simple, well-found and easy to implement inference tools, this thesis develops a series of new regression methods for mode and binary quantile regression. Chapter 2 deals with mode regression methods from the Bayesian perspective and presents one parametric and two non-parametric methods of inference. Chapter 3 demonstrates a mode-based, fast pattern-identification method for big data and proposes the first fully parametric mode regression method, which effectively uncovers the dependency of typical patterns on a number of covariates. The proposed approach is demonstrated through the analysis of a decade-long dataset on the Body Mass Index and associated factors, taken from the Health Survey for England. Finally, Chapter 4 presents an alternative binary quantile regression approach, based on the nonlinear least asymmetric weighted squares, which can be implemented using standard statistical packages and guarantees a unique solution.

The focus of this thesis is on index tracking that aims to replicate the movements of an index of a specific financial market. It is a form of passive portfolio (fund) management that attempts to mirror the performance of a specific index and generate returns that are equal to those of the index, but without purchasing all of the stocks that make up the index. Additionally, we consider the problem of out-performing the index - Enhanced Indexation. It attempts to generate modest excess returns compared to the index. Enhanced indexation is related to index tracking in that it is a relative return strategy. One seeks a portfolio that will achieve more than the return given by the index (excess return). In the first approach, we propose two models for the objective function associated with choice of a tracking portfolio, namely; minimise the maximum absolute difference between the tracking portfolio return and index return and minimise the average of the absolute differences between tracking portfolio return and index return. We illustrate and investigate the performance of our models from two perspectives; namely, under the exclusion and inclusion of fixed and variable costs associated with buying or selling each stock. The second approach studied is that of using Quantile regression for both index tracking and enhanced indexation. We present a mixed-integer linear programming of these problems based on quantile regression. The third approach considered is on quantifying the level of uncertainty associated with the portfolio selected. The quantification of uncertainty is of importance as this provides investors with an indication of the degree of risk that can be expected as a result of holding the selected portfolio over the holding period. Here a bootstrap approach is employed to quantify the uncertainty of the portfolio selected from our quantile regression model.