Connus sous le nom de Systèmes d’Alerte Avancés, ou Early Warning Systems (EWS), les modèles de prévision des crises financières sont appelés à jouer un rôle déterminant dans l’orientation des politiques économiques tant au niveau microéconomique qu’au niveau macroéconomique et international. Or,dans le sillage de la crise financière mondiale, des questions majeures se posent sur leur réelle capacité prédictive. Deux principales problématiques émergent dans le cadre de cette littérature : comment évaluer les capacités prédictives des EWS et comment les améliorer ?Cette thèse d’économétrie appliquée vise à proposer (i) une méthode d’évaluation systématique des capacités prédictives des EWS et (ii) de nouvelles spécifications d’EWS visant à améliorer leurs performances. Ce travail comporte quatre chapitres. Le premier propose un test original d’évaluation des prévisions par intervalles de confiance fondé sur l’hypothèse de distribution binomiale du processus de violations. Le deuxième chapitre propose une stratégie d’évaluation économétrique des capacités prédictives des EWS. Nous montrons que cette évaluation doit être fondée sur la détermination d’un seuil optimal sur les probabilités prévues d’apparition des crises ainsi que sur la comparaison des modèles.Le troisième chapitre révèle que la dynamique des crises (la persistance) est un élément essentiel de la spécification économétrique des EWS. Les résultats montrent en particulier que les modèles de type logit dynamiques présentent de bien meilleurs capacités prédictives que les modèles statiques et que les modèles de type Markoviens. Enfin, dans le quatrième chapitre nous proposons un modèle original de type probit dynamique multivarié qui permet d’analyser les schémas de causalité intervenant entre différents types crises (bancaires, de change et de dette). L’illustration empirique montre clairement que le passage à une modélisation trivariée améliore sensiblement les prévisions pour les pays qui connaissent les trois types de crises. / Known as Early Warning Systems (EWS), financial crises forecasting models play a key role in definingeconomic policies at microeconomic, macroeconomic and international level. However, in the wake ofthe global financial crisis, numerous questions with respect to their forecasting abilities have been raised,as very few signals were drawn prior to the starting of the turmoil. Two questions arise in this context:how to evaluate EWS forecasting abilities and how to improve them?The broad goal of this applied econometrics dissertation is hence (i) to propose a systematic model-free evaluation methodology for the forecasting abilities of EWS as well as (ii) to introduce new EWSspecifications with improved out-of-sample performance. This work has been concretized in four chapters.The first chapter introduces a new approach to evaluate interval forecasts which relies on the binomialdistributional assumption of the violations series. The second chapter proposes an econometric evaluationmethodology of the forecasting abilities of an EWS. We show that adequate evaluation must take intoaccount the cut-off both in the optimal crisis forecast step and in the model comparison step. The thirdchapter points out that crisis dynamics (persistence) is essential for the econometric specification of anEWS. Indeed, dynamic logit models lead to better out-of-sample forecasting probabilities than those oftheir main competitors (static model and Markov-switching one). Finally, a multivariate dynamic probitEWS is proposed in the fourth chapter to take into account the causality between different types of crises(banking, currency, sovereign debt). The empirical application shows that the trivariate model improvesforecasts for countries that underwent the three types of crises.

La question du développement inclusif dans les pays en développement est au cœur de cette thèse. Cette dernière s'articule autour de quatre chapitres sur les questions de politique fiscale et les questions liées à la croissance inclusive. Le chapitre 1 explore comment la politique fiscale de l’Etat affecte l'inclusivité de la croissance dans les pays en développement. Nous observons que la politique fiscale affecte la croissance inclusive de manière significative si et seulement si les pays ont de fortes qualités institutionnelles. En outre, notre résultat montre qu'il existe un seuil optimal au-delà duquel toute augmentation du taux d'imposition négativement la croissance inclusive. Le chapitre 2 examine les effets des composantes des dépenses publiques sur l'équité et la croissance dans les pays d’Afrique subsaharienne, notamment s'il est possible de concevoir des dépenses publiques en vue de promouvoir une société plus équitable sans sacrifier la croissance économique. Notre étude a permis de montrer que l’investissement en infrastructure a contribué à une croissance plus inclusive en Afrique subsaharienne que d'autres dépenses publiques. Ces résultats suggèrent que des programmes temporaires et bien ciblés devraient être mis en place pour aider ceux qui sont laissés pour compte par le processus de croissance. Le chapitre 3 cherche à savoir si les problèmes d’inégalités de revenus se sont posés ou non dans les périodes d'ajustement budgétaire en Côte d'Ivoire au cours de la période 1980-2014. Nos résultats montrent une amélioration de la performance de croissance après les épisodes de consolidation budgétaire, mais aussi des diminutions de l'écart de revenu dans les périodes suivantes les années d’ajustements budgétaires. Enfin, le chapitre 4 évalue la crédibilité des prévisions budgétaires et leurs effets sur le bien-être social dans les pays de la CEMAC et de l'UEMOA. Nous sommes aboutis aux résultats que l'inefficacité des prévisions budgétaires se produit dans la plupart des cas parce que les erreurs de prévisions sont proportionnelles à la prévision elle-même, mais aussi parce que les erreurs passées sont répétées dans le temps. En outre, une partie des erreurs de prévision des recettes peut s'expliquer par des chocs aléatoires survenus dans l'économie. Par conséquent, ces erreurs dans les prévisions de revenus considérées comme des chocs de politique budgétaire ont un effet négatif sur la croissance inclusive. / The issue of inclusive development in developing countries is at the heart of this thesis. The latter revolves around four chapters on fiscal policy issues and inclusive growth-related matters. Chapter 1 explores how government tax policy affects the inclusiveness of growth in developing countries. Evidence is shown that tax policy affects significantly inclusive growth if and only if the countries have a strong institution quality like low corruption and a good bureaucratic policy. In addition, our result shows that there is an optimal tax beyond which, any increase in the personal income tax rate should have negative impact on inclusive growth. The Chapter 2 examines the effects of government expenditure components on both equity and growth in sub-Saharan countries, especially whether it is possible to design public spending to promote a more equitable society without sacrificing economic growth. We find that investment in infrastructure contributed to more inclusive growth in Sub-sub Saharan African economies than others government spending. These results suggest that temporary and well-targeted programs should be implemented to help those being left out by the growth process. The Chapter 3 investigates whether income inequality matters in the periods of fiscal adjustments in Côte d’Ivoire over the period 1980-2014. The results show an improvement in growth performance after fiscal consolidations episodes, but also income gap decreases in the periods ahead fiscal adjustments. Lastly, Chapter 4 assesses the credibility of fiscal forecasts and their social effects in CEMAC and WAEMU countries. We obtain evidence that the inefficiency of fiscal forecast occurs in most time because the forecast deviation is proportional to the forecast itself, but also because the past errors are repeated in the present. Furthermore, a part of revenue forecast errors can be explained by random shocks to the economy. Therefore, these errors in revenue forecast considered as fiscal policy shocks has a detrimental effect on inclusive growth.

Research of the relationship between human capital and economic growth has been significantly developing since 1960s'. This thesis aims to contribute to the topic of human capital, map its importance in the theoretical and empirical economics and examine the influence on macroeconomic growth. In most of the countries human capital and education are budgeted mainly from the public resources thus is the human capital theory important for the public sector as well. First part of the thesis presents the main growth models and its evolution concerning also the human capital. In the second part there is a summary of the most important empirical literature. In the third part I present and comment the results of the empirical analyses. The benchmark data panel covers 92 countries within years 1960-2005. The method of analyses is panel data regression. The primary finding of the thesis is confirmation of the positive influence that human capital has on the economic growth. Third part also tests the most adequate proxy of the human capital. The quality of the human capital as well as the link between quantity and quality are also reflected in the regression analyses.

New keynesian Phillips curve (NKPC) has become a central model to study the relation between inflation and real economic activity, notably in the framework of optimal monetary policy design. However, some recent evidence suggests that empirical data are usually at odds with the underlying theory. The model due to its inherent structure represents a statistical challenge in its own right. Since Galí and Gertler (1999) published their seminal paper introducing estimation via GMM techniques, they have triggered a heated debate on its empirical relevance. Their approach has been heavily criticised by later authors, mainly on the grounds of questionable behaviour of GMM estimator in the NKPC context and/or its small sample properties. The common criticism includes sensitivity to the choice of instrument set, weak identification and small sample bias. In this thesis I propose a new estimation strategy that provides a remedy to above mentioned shortcomings and allows to obtain reliable estimates. The procedure exploits recent advances in GMM theory as well as in other fields of statistics, in particular in the area of time series factor analysis and bootstrap. The proposed estimation strategy consists of several consecutive steps: first, to reduce a small sample bias resulting from excessive use of instruments I summarize all available information by employing factor analysis and include estimated factors into information set. In the second step I use statistical information criteria to select optimal instruments and eventually I obtain confidence intervals on parameters using bootstrap method. In NKPC context all these methods were used for the first time and can also be used independently. Their combination however provides synergistic effect that helps to improve the properties of estimates and to check the efficiency of given steps. Obtained results suggest that NKPC model can explain Czech inflation dynamics fairly well and provide some support for underlying theory. Among other things the results imply that the policy of disinflation may not be as costly with respect to a loss in aggregate product as earlier versions of Phillips curve would indicate. However, finding a good proxy for real economic activity has proved to be a difficult task. In particular we demonstrated that results are conditional on how the measure is calculated, some measures even showed countercyclical behaviour. This issue -- in the thesis discussed only in passing -- is a subject of future research. In addition to the proposed strategy and provided parameter estimates the thesis brings some partial simulation-based findings. Simulations elaborate on earlier literature on naive bootstrap in GMM context and study performance of bootstrap modifications of unit root and KPSS test.

在總體經濟學中，跨期替代分析方法佔有相當重要的地位。其中跨期替代彈性(the elasticity of intertemporal substitution, EIS)的大小，間接或者直接影響總體經濟中的許多層面，直覺上，例如跨期替代彈性越大，對個人而言，是對當期消費的機會成本提升，使延後消費的意願上升，同時增加個人儲蓄，在正常金融市場情況之下，個人儲蓄金額的增加，將使市場資金的供給量增多，使得企業或個人的投資機會成本降低，經由總體經濟中間接或直接的影響下，則總體經濟成長率應會上升。其中，當消費者效用函數為固定風險趨避係數(constant coefficient of relative risk aversion, CRRA)且具有跨期分割與可加性的特性，加上在傳統經濟學中，假設每個人皆為完全理性的前提下，經由跨期替代分析方法推導後，可以得到相對風險趨避係數(the coefficient of relative risk aversion, RRA)與跨期替代彈性(the elasticity of intertemporal substitution, EIS)恰好是倒數關係。 / 在過去相關研究中，Hansen and Singleton (1983)推估出跨期替代彈性值較大且顯著，但Hall (1988)強調，若考慮資料的時間加總問題(time aggregation problem)， 則前者估計出跨期替代彈性在統計上則不再是顯著；Hall亦於結論提出跨期替代彈性為小於或等於0.1，甚至比0小。在經濟意義上，代表股票市場中投資人的相對風險趨避程度(RRA)極大，直覺上，是不合理的現象，這也是著名的彈性迷思(elasticity puzzle)。於是Epstein and Zin (1991)嘗試建議並修正效用函數為不具時間分割性(non-time separable utility)的效用函數，並得到跨期替代彈性(EIS)與相對風險趨避係數(RRA)互為倒數關係，不復存在的結論。這也說明影響彈性迷思(elasticity puzzle)的原因有許多，其中之一，可能為設定不同形式效用函數所造成。 / 在傳統經濟模型中，假設完全理性的個人決策行為之下，利用跨期替代方法，可以得到跨期替代彈性(EIS)與相對風險趨避程度(RRA)互為倒數關係後，又得到隱含風險趨避程度為無窮大的推估結論。這也是本研究想要來探究的問題，即是彈性迷思(elasticity puzzle)究竟是假設所造成，或者是因為由個體資料加總成總體資料，所產生的謬誤。 / 因此，本研究與其他研究不同之處，在於利用建構時間可分離形式的效用函數(time-separable utility)模型基礎，以遺傳演算(Genetic Algorithms)方法，建構有限理性的人工股票市場進行模擬，其中，模擬方式為設定不同代理人(agent)有不同程度的預測能力，代表其理性程度的差異的表現。 / 本研究發現在有限理性異質性個人的人工股票市場下，相對風險趨避程度係數(RRA)與跨期替代彈性(EIS)不為倒數關係，且設定不同代理人不同的預測能力，亦會影響跨期替代彈性(EIS)的推估數值大小。

The cognitive radio-based wireless networks have been proposed as a promising technology to improve the utilization of the radio spectrum through opportunistic spectrum access. In this context, the cognitive radios opportunistically access the spectrum which is licensed to primary users when the primary user transmission is detected to be absent. For opportunistic spectrum access, the cognitive radios should sense the radio environment and allocate the spectrum and power based on the sensing results. To this end, in this thesis, I develop a novel cooperative spectrum sensing scheme for cognitive radio networks (CRNs) based on machine learning techniques which are used for pattern classification. In this regard, unsupervised and supervised learning-based classification techniques are implemented for cooperative spectrum sensing. Secondly, I propose a novel joint channel and power allocation scheme for downlink transmission in cellular CRNs. I formulate the downlink resource allocation problem as a generalized spectral-footprint minimization problem. The channel assignment problem for secondary users is solved by applying a modified Hungarian algorithm while the power allocation subproblem is solved by using Lagrangian technique. Specifically, I propose a low-complexity modified Hungarian algorithm for subchannel allocation which exploits the local information in the cost matrix. Finally, I propose a novel dynamic common control channel-based medium access control (MAC) protocol for CRNs. Specifically, unlike the traditional dedicated control channel-based MAC protocols, the proposed MAC protocol eliminates the requirement of a dedicated channel for control information exchange. / October 2015

El presente trabajo analiza la influencia de los regímenes cambiarios sobre la dolarización de los pasivos empresariales, focalizándose en la diferencia entre regímenes fijos y flexibles. Para hacerlo se utiliza una muestra de 237 empresas de Argentina, Brasil, Colombia y México para el período 1992-2000, la metodología de estimación GMM-system para modelos de panel dinámicos y dos clasificaciones de regímenes cambiarios. Los resultados sugieren que los regímenes cambiarios fijos, así como su duración y volatilidad, influyen positivamente sobre la proporción de deuda en moneda extranjera mantenida por las firmas. Asimismo, se exploran otros determinantes del grado de dolarización de los pasivos corporativos, introduciéndose variables sugeridas por la literatura pero no analizadas hasta el momento. Se observa que la inestabilidad de la economía afecta las decisiones de cartera de las firmas. A su vez, las expectativas de salvataje por parte del Estado y las regulaciones generan problemas de información asimétrica, incentivando a las firmas a tomar un mayor riesgo cambiario. Por último se explora la relación entre la dolarización de los pasivos corporativos y el original sin interno, concluyendo que la reducción de éste último es, en parte, alcanzada vía dolarización de la deuda de largo plazo.

In this paper, we propose two types of sieve bootstrap, univariate and multivariate approach, for the generalized method of moments estimators of time series data. Compared with the nonparametric block bootstrap, the sieve bootstrap is in essence parametric, which helps fitting data better when researchers have prior information about the time series properties of the variables of interested. Our Monte Carlo experiments show that the performances of these two types of sieve bootstrap are comparable to the performance of the block bootstrap. Furthermore, unlike the block bootstrap, which is sensitive to the choice of block length, these two types of sieve bootstrap are less sensitive to the choice of lag length.

This thesis if focused on the application of methods of robust econometrics to real economic data. We focuse on the issuies of international trade in Czech Republic and the problem of employment and growth of small businesses in Europe. We also focues on estimation of panel data by classical approaches (least squares, fixed effects, GMM) and bzy robust techniques. The first part of dissertation focuses on analyzing determinants of FDI in Czech manufacturing industry. The aim is to estimate a model where the stock of FDI is expressed as a function of several economic factors (K/L, profit per worker, R&D, Balassa index and others). We estimate these models by OLS, fixed effects and GMM. With regard to ambiguous results we used least trimmed squares as a diagnostic tool for detection of outliers. Elimination of two polluting industries out of the data set brings certain improvement in significance of some factors. The second part of dissertation we focus on an estimation of models of employment and net production in 28 European countries for small businesses as a function of economic and institutional variables by special technique of estimation. We describe robust version of within group fixed effects estimation. The aim of paper is to estimate a set of models and to test the properties of estimator. With...

Conselho Nacional de Pesquisa e Desenvolvimento Científico e Tecnológico - CNPq / This work presents a study of the spectral dynamics characteristics of audio signals. More specifically, we aim at detecting regularities that can be modeled in typical domestic sounds, in order to classify them. Our starting point is the work of Sehili et al. [2], in which a household sounds classification system based on GMM is proposed. The Sehili system is reproduced in this work as a baseline system. Following the same protocol of experiments, a 73 % recognition rate is achieved. Afterwards, three sets of experiments are performed, arranged so that each new approach incorporates a new technique to highlight a different aspect of the spectral dynamics. The first technique is the insertion of the discrete gradient information of feature vectors, a strategy aimed at a local spectral dynamic analysis, and resultes in a perceptible increase in recognition rate. The next experiment is conducted with a HMM based classifier, in which the spectral dynamic should be encoded in state transition probability matrices. The tests with the HMM do not result in improved recognition rates. The last experiment is based on a features extraction method, proposed by the author, called Patterns of Energy Envelope per Band (PEEB). The PEEB is an extractor that highlight the signal spectral dynamics inside narrow bands. In domestic sounds recognition tests, the classification system based on a combination of PEEB, MFCC and GMM strategies resulted in a significant improvement over all other systems tested. We conclude, based on our results, that the spectral dynamics of the studied dataset plays an important role in the classification task. However, the approaches for spectral dynamic information extraction, studied in this work, are not definitive, for it is clear that they can be further developed. For example, in the case of PEEB, the recognition rate is strongly dependent on the sound class, suggesting more elaborate forms of fusion of PEEB and MFCC features for each class. / Este trabalho é um estudo da característica da dinâmica espectral em sinais sonoros, com vistas a encontrar as regularidades que podem ser modeladas em sons tipicamente domésticos, com o objetivo de classificá-los. O ponto de partida é o trabalho de Sehili et al. [1], no qual é proposto um sistema de classificação de sons domésticos baseado em GMM. O sistema de Sehili é reproduzido neste trabalho como marco zero na análise da dinâmica espectral, seguindo o mesmo roteiro dos experimentos. A partir daí, três conjuntos de experimentos são realizados, organizados de forma que, a cada novo experimento, uma técnica – que destaca um aspecto diferente da dinâmica espectral – seja incorporada. A primeira técnica analisada é a inserção da informação de gradiente discreto dos vetores de características, estratégia que representa uma análise de dinâmica espectral local e que resulta num aumento perceptível na taxa de classificação. O próximo experimento é realizado com um classificador baseado em HMM, no qual a informação de dinâmica espectral deve ser codificada na matriz de probabilidades de transição de estados do modelo. Os testes com o HMM não resultam em melhora na taxa de reconhecimento das classes de sons. O último experimento é baseado num extrator de características proposto pelo autor, chamado de Padrões de Envelopes de Energia por Banda (PEEB). O PEEB é um extrator que destaca os padrões de evolução espectro-temporais do sinais. Nos testes de reconhecimento de sons domésticos, o sistema de classificação baseado numa combinação das estratégias PEEB, MFCC e GMM resultam numa melhora significativa em relação a todos os outros sistemas testados. Conclui-se, com base nos resultados, que a dinâmica espectral dos sinais da base estudada é relevante à tarefa de classificação. No entanto, as maneiras de extração da informação de dinâmica espectral estudadas neste trabalho não são definitivas, pois ainda há muito espaço para desenvolvê-las. Por exemplo, no caso do PEEB, nota-se que a taxa de classificação fortemente é dependente da classe sonora, sugerindo formas mais elaboradas de fusão das características PEEB e MFCC para cada classe.