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Diese Doktorarbeit besteht aus drei Aufsätzen, in welchen die Reaktion von Finanzinstitutionen auf Geldpolitik analysiert wird. In dem ersten Aufsatz finde ich anhand eines Bayesian VAR, dass eine Erhöhung des Leitzinses zu einer zusätzlichen Kreditvergabe in Nichtbanken (NBFI) führt. Banken verleihen wie bereits bekannt weniger. Der Grund für die gegensätzliche Bewegung liegt in der unterschiedliche Art der Finanzierung. Dieser Befund legt nahe, dass die Existenz von NBFI die Volatilität der aggregierten Kreditvergabe zu geldpolitischen Schocks verringern könnte. Zusätzlich bietet die Analyse einen Erklärungsansatz für die Beobachtung, dass sich die Kreditvergabe seit der Finanzkrise stockend entwickelt hat. Im zweiten Aufsatz knüpfe ich an diese empirische Untersuchung an, indem ich ein theoretisches Modell mit unterschiedlichen Arten von Firmenfinanzierung entwerfe. Haushalte müssen sich zwischen festverzinsichlichen und erfolgsbedingten Sparmöglichkeiten entscheiden. Auf Grundlage des Modells von Bernanke, Gertler und Gilchrist (1999) mikrofundiere ich die Entscheidung über Unternehmensgründung in Form von Eigenkapitalinvestitionen. Im dritten Aufsatz entwickele ich ein geschätztes DSGE Modell mit Finanzierungsfriktionen, welches in der Lage ist, die empirischen Ergebnisse zu replizieren. Ich untersuche, wie sich die Regulierung von Schattenbanken auf eine Volkswirtschaft am ZLB auswirkt. Konsumvolatilität wird reduziert, wenn Schattenbankenkredite stattdessen von Banken vergeben werden. Alternativ dazu führt die Behandlung von Schattenbanken wie Investment Fonds dazu, dass eine Volkswirtschaft am ZLB eine mildere Rezession und einen schnelleren Austritt erlebt. Der Grund liegt darin, dass ein Nachfrageschock, der die Volkswirtschaft zum ZLB bringt, eine Reaktion hervorruft, die vergleichbar mit geldpolitischen Schocks ist, da am ZLB keine Möglichkeit der Leitzinsverringerung besteht. / This thesis consists of three essays that analyze the reaction of financial institutions to monetary policy. In the first essay, I use a Bayesian VAR to show that an increase in the monetary policy rate raises credit intermediation by non-bank financial institutions (NBFI). As is well known, credit intermediation by banks is reduced. The movement in opposite directions is explained by the difference in funding. This finding suggests that the existence of NBFI may decrease aggregate volatility following monetary policy shocks. Following this evidence, I construct a theoretical model that includes different types of funding in the second essay. Households face a savings choice between state contingent (equity) and non-state contingent (debt) assets. I use the financial accelerator model of Bernanke, Gertler and Gilchrist (1999) as a basis and microfound the decision by which new net worth in entrepreneurs is created. A Bayesian estimation suggests a change in the survival rate of entrepreneurs, affecting impulse responses. The analysis suggests that models that use the financial accelerator should include endogenous firm entry if variables regarding household portfolios or shocks directly affecting firm net worth are considered. In the third essay, I develop an estimated monetary DSGE model with funding market frictions that is able to replicate the empirical facts. In a counterfactual exercise I study how the regulation of shadow banks affects an economy at the ZLB. Consumption volatility is reduced when shadow bank assets are directly held by commercial banks. Alternatively, regulating shadow banks like investment funds results in a milder recession during, and a quicker escape from, the ZLB. The reason is that a recessionary demand shock that moves the economy to the ZLB has similar effects to a monetary tightening due to the inability to reduce the policy rate below zero.

[pt] Estimamos um modelo DSGE para a economia brasileira abrangendo a transição do regime de bandas cambiais para o regime de metas para a inflação ocorrida em 1999. Utilizamos um modelo novo keynesiano de pequena economia aberta no qual o Banco Central segue uma regra de política monetária estruturalmente distinta em cada regime. Encontramos diferenças significativas na dinâmica macroeconômica e nos mecanismos de transmissão dos choques estruturais, com destaque àqueles relacionados ao setor externo da economia. Realizamos experimentos contrafactuais onde simulamos o impacto de cenários alternativos para a transição de regime na trajetória das variáveis econômicas brasileiras. Entre outros resultados, as simulações sugerem que a manutenção do sistema de bandas cambiais seria insustentável enquanto a antecipação da implementação do regime de metas para a inflação para antes da crise da Rússia de 1998 poderia deixar a economia brasileira em situação mais favorável. Por fim, mostramos que um teste de quebra estrutural aplicado sobre todo o período amostral detecta com precisão a ocorrência da mudança de regime em 1999. / [en] We estimate a DSGE model of the brazilian economy taking into account the transition from the exchange rate band system to inflation targeting occurred in 1999. We use a new Keynesian small open economy model where the Central Bank follows structurally different monetary policy rules in each regime. By comparing the transmission channels of exogenous shocks we find significant differences across the regimes, specially on those shocks related to the foreign sector of the economy.We then perform counterfactual experiments where we simulate the response of key macro variables under alternative scenarios for the regime transition. Among other results our simulations suggest that the continuation of the exchange rate band system could have been unsustainable while anticipating the transition to inflation targeting before the Russian crisis of 1998 could have benefited the economy. Additionally, we show that a structural break test applied to the whole data sample correctly identifies the regime change in 1999.

L'objectif de cette thèse est de proposer une méthodologie statistique permettant de formuler une règle de classement capable de surmonter les difficultés qui se présentent dans le traitement des données lorsque la distribution a priori de la variable réponse est déséquilibrée. Notre proposition est construite autour d'un ensemble particulier de règles d'association appelées "class association rules". Dans le chapitre II, nous avons exposé les bases théoriques qui sous-tendent la méthode. Nous avons utilisé les indicateurs de performance usuels existant dans la littérature pour évaluer un classifieur. A chaque règle "class association rule" est associée un classifieur faible engendré par l'antécédent de la règle que nous appelons profils. L'idée de la méthode est alors de combiner un nombre réduit de classifieurs faibles pour constituer une règle de classement performante. Dans le chapitre III, nous avons développé les différentes étapes de la procédure d'apprentissage statistique lorsque les observations sont indépendantes et identiquement distribuées. On distingue trois grandes étapes: (1) une étape de génération d'un ensemble initial de profils, (2) une étape d'élagage de profils redondants et (3) une étape de sélection d'un ensemble optimal de profils. Pour la première étape, nous avons utilisé l'algorithme "apriori" reconnu comme l'un des algorithmes de base pour l'exploration des règles d'association. Pour la deuxième étape, nous avons proposé un test stochastique. Et pour la dernière étape un test asymptotique est effectué sur le rapport des valeurs prédictives positives des classifieurs lorsque les profils générateurs respectifs sont emboîtés. Il en résulte un ensemble réduit et optimal de profils dont la combinaison produit une règle de classement performante. Dans le chapitre IV, nous avons proposé une extension de la méthode d'apprentissage statistique lorsque les observations ne sont pas identiquement distribuées. Il s'agit précisément d'adapter la procédure de sélection de l'ensemble optimal lorsque les données ne sont pas identiquement distribuées. L'idée générale consiste à faire une estimation bayésienne de toutes les valeurs prédictives positives des classifieurs faibles. Par la suite, à l'aide du facteur de Bayes, on effectue un test d'hypothèse sur le rapport des valeurs prédictives positives lorsque les profils sont emboîtés. Dans le chapitre V, nous avons appliqué la méthodologie mise en place dans les chapitres précédents aux données du projet QUARITE concernant la mortalité maternelle au Sénégal et au Mali. / The aim of this thesis is to design a supervised statistical learning methodology that can overcome the weakness of standard methods when the prior distribution of the response variable is unbalanced. The proposed methodology is built using class association rules. Chapter II deals with theorical basis of statistical learning method by relating various classifiers performance metrics with class association rules. Since the classifier corresponding to a class association rules is a weak classifer, we propose to select a small number of such weak classifiers and to combine them in the aim to build an efficient classifier. In Chapter III, we develop the different steps of the statistical learning method when observations are independent and identically distributed. There are three main steps: In the first step, an initial set of patterns correlated with the target class is generated using "apriori" algorithm. In the second step, we propose a hypothesis test to prune redondant patterns. In the third step, an hypothesis test is performed based on the ratio of the positive predictive values of the classifiers when respective generating patterns are nested. This results in a reduced and optimal set of patterns whose combination provides an efficient classifier. In Chapter IV, we extend the classification method that we proposed in Chapter III in order to handle the case where observations are not identically distributed. The aim being here to adapt the procedure for selecting the optimal set of patterns when data are grouped data. In this setting we compute the estimation of the positive predictive values as the mean of the posterior distribution of the target class probability by using empirical Bayes method. Thereafter, using Bayes factor, a hypothesis test based on the ratio of the positive predictive values is carried out when patterns are nested. Chapter V is devoted to the application of the proposed methodology to process a real world dataset. We studied the QUARITE project dataset on maternal mortality in Senegal and Mali in order to provide a decision making tree that health care professionals can refer to when managing patients delivering in their health facilities.

[pt] Este trabalho lida com o problema de radiolocalização de terminais em um ambiente de comunicações móveis celulares. Desenvolve-se novas alternativas para a estimação da posição, admitindo-se que as medidas de tempo de chegada (ToA) obtidas no enlace-rádio estão corrompidas por ruído aditivo e apresentam erro médio positivo durante períodos aleatórios, devido à ausência de linha de visada (NLOS) entre terminal e estações radio-bases. Em uma das alternativas desenvolve-se um estimador assintoticamente eficiente do erro de NLOS, sob o critério de mínimos quadrados ponderados (WLS). Para esta estimativa, admite- se o conhecimento a priori do espalhamento temporal do canal, e que o perfil de potência do sinal pode ser calculado por uma média temporal de medidas independentes em um receptor RAKE. O esquema de localização apresentado incorpora também um teste de hipóteses desenvolvido sob o critério de Neyman-Pearson, para detectar, a cada instante de tempo, a ocorrência de transições entre os estados LOS/NLOS do canal. Em outra contribuição do trabalho, as coordenadas do terminal são estimadas recursivamente utilizando-se algoritmos bayesianos, com a dimensão do espaço de estados aumentada para incluir o efeito do erro de NLOS sobre as medidas de ToA. Resultados de simulação obtidos sob diferentes cenários comprovam a eficiência dos esquemas de estimação aqui desenvolvidos, quando comparados à única solução de que se tem conhecimento na literatura. Apresenta-se ainda nesta tese uma análise para o problema de ambigüidade em métodos hiperbólicos de localização, cujo objetivo é identificar a região do plano em que este método fornece duas soluções fisicamente admissíveis. A área desta região é comparada com a área total de triangulação. / [en] This work addresses the radiolocation problem of a moving terminal in a cellular mobile communications environment. New alternatives are developed for position estimation, assuming that the Time of Arrival (ToA) measurements obtained from radio link are corrupted by additive noise and have positive mean error during random periods of time due to the non-line of sight (NLOS) propagation condition between the terminal and base stations. In one of the proposals, an asymptotically efficient WLS estimator of the NLOS error is developed under the Weighted Least Squares criterion. It is assumed that the channel temporal scattering model is known and the mean power delay profile can be evaluated by time averaging independent measurements from a RAKE receiver. The location estimation scheme also includes a hypothesis testing based on Neyman- Pearson approach to detect at each instant of time the LOS/NLOS states transitions. In another contribution, the terminal coordinates are recursively estimated using bayesian algorithms, with the state-space dimension augmented to include the NLOS error effect over ToA measurements. Simulation results obtained under different scenarios show the effectiveness of the estimation schemes developed here when compared to the only alternative known from the literature. An analysis concerning the ambiguity problem in hyperbolic location methods is also presented, aiming to determine the regions where this method gives two physically admissible solutions, and compare them to the total trilateration area.

La détection et le pistage de cibles de surface, maritimes ou terrestres, constituent l’un des champs d’application de la surveillance par radar aéroporté. Dans ce contexte spécifique, il s’agit d’estimer les trajectoires d’un ou de plusieurs objets mobiles au cours du temps à partir de mesures radar bruitées. Cependant, plusieurs contraintes s’additionnent au problème d’estimation des trajectoires :1. le nombre d’objets présents dans la région d’intérêt est inconnu et peut évoluer au cours du temps,2. les mesures fournies par le radar ne correspondent pas toutes à des objets mobiles car certaines sont dues à l’environnement ; il s’agit de fausses alarmes,3. une mesure n’est pas toujours disponible pour chaque objet à chaque instant ; il s’agit de non-détections,4. les cibles de surface peuvent être très diverses en termes de capacité de manoeuvre.Pour tenir compte des trois premières exigences, les modèles d’ensembles finis aléatoires peuvent être envisagés pour procéder aux estimations simultanées du nombre d’objets et de leur trajectoire dans un formalisme bayésien. Pour répondre à la quatrième contrainte, une classification des objets à pister peut s’avérer utile. Aussi, dans le cadre de cette thèse, nous nous intéressons à deux traitements adaptatifs qui intègrent ces deux principes.Tout d’abord, nous proposons une approche conjointe de pistage et de classification dédiée au cas d’un objet évoluant en présence de fausses alarmes. Notre contribution réside dans le développement d’un algorithme incorporant un filtre fondé sur un ensemble fini aléatoire de Bernoulli. L’algorithme résultant combine robustesse aux fausses alarmes et capacité à classer l’objet. Cette classification peut être renforcée grâce à l’estimation d’un paramètre discriminant comme la longueur, qui est déduite d’une mesure d’étalement distance.Le second traitement adaptatif présenté dans cette thèse est une technique de pistage de groupes de cibles dont les mouvements sont coordonnés. Chaque groupe est caractérisé par un paramètre commun définissant la coordination des mouvements de ses cibles. Cependant, ces dernières conservent une capacité de manoeuvre propre par rapport à la dynamique de groupe. S’appuyant sur le formalisme des ensembles finis aléatoires, la solution proposée modélise hiérarchiquement la configuration multi-groupes multi-cibles. Au niveau supérieur, la situation globale est représentée par un ensemble fini aléatoire dont les éléments correspondent aux groupes de cibles. Ils sont constitués du paramètredu groupe et d’un ensemble fini aléatoire multi-cibles. Ce dernier contient les vecteurs d’état des cibles du groupe dont le nombre peut évoluer au cours du temps. L’algorithme d’estimation développé est lui-aussi organisé de manière hiérarchique. Un filtre multi-Bernoulli labélisé (LMB) permet d’estimer le nombre de groupes, puis pour chacun d’entre eux, leur probabilité d’existence ainsi que leur paramètre commun. Pour ce faire, le filtre LMB interagit avec un banc de filtres multi-cibles qui opèrent conditionnellement à une hypothèse de groupe. Chaque filtre multi-cibles estime le nombre et les vecteurs d’état des objets du groupe. Cette approche permet de fournir à l’opérationnel des informations sur la situation tactique. / Detecting and tracking maritime or ground targets is one of the application fields for surveillance by airborne radar systems. In this specific context, the goal is to estimate the trajectories of one or more moving objects over time by using noisy radar measurements. However, several constraints have to be considered in addition to the problem of estimating trajectories:1. the number of objects inside the region of interest is unknown and may change over time,2. the measurements provided by the radar can arise from the environment and do not necessarily correspond to a mobile object; the phenomenon is called false detection,3. a measurement is not always available for each object; the phenomenon is called non-detection,4. the maneuverability depends on the surface targets.Concerning the three first points, random finite set models can be considered to simultaneously estimate the number of objects and their trajectories in a Bayesian formalism. To deal with the fourth constraint, a classification of the objects to be tracked can be useful. During this PhD thesis, we developped two adaptive approaches that take into account both principles.First of all, we propose a joint target tracking and classification method dedicated to an object with the presence of false detections. Our contribution is to incorporate a filter based on a Bernoulli random finite set. The resulting algorithm combines robustness to the false detections and the ability to classify the object. This classification can exploit the estimation of a discriminating parameter such as the target length that can be deduced from a target length extent measurement.The second adaptive approach presented in this PhD dissertation aims at tracking target groups whose movements are coordinated. Each group is characterized by a common parameter defining the coordination of the movements of its targets. However, the targets keep their own capabilities of maneuvering relatively to the group dynamics. Based on the random finite sets formalism, the proposed solution represents the multi-target multi-group configuration hierarchically. At the top level, the overall situation is modeled by a random finite set whose elements correspond to the target groups. They consist of the common parameter of the group and a multi-target random finite set. The latter contains the state vectors of the targets of the group whose number may change over time. The estimation algorithm developed is also organized hierarchically. A labeled multi-Bernoulli filter (LMB) makes it possible to estimate the number of groups, and for each of them, to obtain their probability of existence as well as their common parameter. For this purpose, the LMB filter interacts with a bank of multi-target filters working conditionally to a group hypothesis. Each multi-target filter estimates the number and state vectors of the objects in the group. This approach provides operational information on the tactical situation.

This thesis contributes new algorithms and implementations for particle filter-based target tracking. From an algorithmic perspective, modifications that improve a batch-based acoustic direction-of-arrival (DOA), multi-target, particle filter tracker are presented. The main improvements are reduced execution time and increased robustness to target maneuvers. The key feature of the batch-based tracker is an image template-matching approach that handles data association and clutter in measurements. The particle filter tracker is compared to an extended Kalman filter~(EKF) and a Laplacian filter and is shown to perform better for maneuvering targets. Using an approach similar to the acoustic tracker, a radar range-only tracker is also developed. This includes developing the state update and observation models, and proving observability for a batch of range measurements. From an implementation perspective, this thesis provides new low-power and real-time implementations for particle filters. First, to achieve a very low-power implementation, two mixed-mode implementation strategies that use analog and digital components are developed. The mixed-mode implementations use analog, multiple-input translinear element (MITE) networks to realize nonlinear functions. The power dissipated in the mixed-mode implementation of a particle filter-based, bearings-only tracker is compared to a digital implementation that uses the CORDIC algorithm to realize the nonlinear functions. The mixed-mode method that uses predominantly analog components is shown to provide a factor of twenty improvement in power savings compared to a digital implementation. Next, real-time implementation strategies for the batch-based acoustic DOA tracker are developed. The characteristics of the digital implementation of the tracker are quantified using digital signal processor (DSP) and field-programmable gate array (FPGA) implementations. The FPGA implementation uses a soft-core or hard-core processor to implement the Newton search in the particle proposal stage. A MITE implementation of the nonlinear DOA update function in the tracker is also presented.

The New Macroeconometrics may succinctly be described as the application of Bayesian analysis to the class of macroeconomic models called Dynamic Stochastic General Equilibrium (DSGE) models. A prominent local example from this research area is the development and estimation of the RAMSES model, the main macroeconomic model in use at Sveriges Riksbank.   Bayesian estimation of DSGE models is often computationally demanding. In this thesis fast algorithms for Bayesian inference are developed and tested in the context of the state space model framework implied by DSGE models. The algorithms discussed in the thesis deal with evaluation of the DSGE model likelihood function and sampling from the posterior distribution. Block Kalman filter algorithms are suggested for likelihood evaluation in large linearised DSGE models. Parallel particle filter algorithms are presented for likelihood evaluation in nonlinearly approximated DSGE models. Prefetching random walk Metropolis algorithms and adaptive hybrid sampling algorithms are suggested for posterior sampling. The generality of the algorithms, however, suggest that they should be of interest also outside the realm of macroeconometrics.

Esta tesis se ocupa de los problemas de la estimación econométrica con muestras pequeñas, en los contextos del los VARs monetarios y de la investigación empírica del crecimiento. Primero, demuestra cómo mejorar el análisis con VAR estructural en presencia de muestra pequeña. El primer capítulo adapta la especificación con prior intercambiable (exchangeable prior) al contexto del VAR y obtiene nuevos resultados sobre la transmisión monetaria en nuevos miembros de la Unión Europea. El segundo capítulo propone un prior sobre las tasas de crecimiento iniciales de las variables modeladas. Este prior resulta en la corrección del sesgo clásico de la muestra pequeña en series temporales y reconcilia puntos de vista Bayesiano y clásico sobre la estimación de modelos de series temporales. El tercer capítulo estudia el efecto del error de medición de la renta nacional sobre resultados empíricos de crecimiento económico, y demuestra que los procedimientos econométricos robustos a incertidumbre acerca del modelo son muy sensibles al error de medición en los datos. / This thesis deals with the problems of econometric estimation with small samples, in the contexts of monetary VARs and growth empirics. First, it shows how to improve structural VAR analysis on short datasets. The first chapter adapts the exchangeable prior specification to the VAR context, and obtains new findings about monetary transmission in New Member States. The second chapter proposes a prior on initial growth rates of modeled variables, which tackles the Classical small-sample bias in time series, and reconciles Bayesian and Classical points of view on time series estimation. The third chapter studies the effect of measurement error in income data on growth empirics, and shows that econometric procedures which are robust to model uncertainty are very sensitive to measurement error of the plausible size and properties.

My dissertation is a collection of three essays that study various aspects of non-price competition among firms using fully microfounded general equilibrium models. The first two chapters, both coauthored with Benedetto Molinari, introduce advertising expenditures by firms into a dynamic and stochastic general equilibrium model (DSGE), in order to address the question of whether and how aggregate advertising expenditures provide important effects upon the aggregate economy. In particular, the first chapter provides a short-run analysis, by focusing on the implications of aggregate adverting expenditure upon the business cycle. The second chapter, in turn, focuses on long-run effects of advertising, by analyzing the implications upon the steady-state equilibrium of aggregate advertising expenditures by firms. The last chapter, by using a modified version of the canonical New Keynesian model, investigates the effect upon inflation dynamics of non-price competition among firms. / Esta tesis contiene tres ensayos que estudian varios aspectos de la competencia no en precio entre las impresas, utilizando modelos de equilibrio general micro-fundados. En los primeros dos capítulos, ambos coautorados con Benedetto Molinari, se introducen gastos en publicidad de las empresas en un modelo dinámico y estocástico de equilibrio general, a través del cual, se estudian las implicaciones de la publicidad en la economía agregada. El primer capítulo se focaliza en los efectos de corto plazo de la publicidad, analizando las implicaciones con respecto al ciclo económico. El segundo capítulo, estudia los efectos de largo plazo de la publicidad, con el objetivo de analizar las implicaciones sobra el estado estacionario del economía. En el último capítulo se utiliza una versión modificada del modelo Neo-Keynesiano que estudia los efectos de la competencia no en precio en relación la dinámica de la inflación.