Дисертації з теми "Weak instrument"

This dissertation is intended to provide a statistical foundation for the IV models and shed lights on a number of issues related to the IV method. The first chapter shows that the theoretical Instrumental Variable model can be derived by reparameterization of a well-specified statistical model defined on the joint distribution of the involved random variables as the actual (local) data generation process. This reveals the covariance structure of the error terms of the usual theory-driven instrumental variable model. The revealed covariance structure of the IV model have important implications, particularly, for designing simulation studies. Monte Carlo simulations are used to reexamine the Nelson and Startz (1990a) findings regarding the performance of IV estimators when the instruments are weak. The results from the simulation exercises indicate that the sampling distribution of ^Î _IV is concentrated around ^Î _OLS. The second chapter considers the underlying joint distribution function of the instrumental variable (IV) model and presents an alternative definition for the exogenous and relevant instruments. The paper extracts a system of independent and orthogonal equations that covers up a non-orthogonal structural model and argues that the estimated IV regression is well-specified if the underlying system of equations is well-specified. It proposes a new instrument relevancy measure that does not suffer from the first-stage R² deficiencies. Third chapter argues the application of the IV method in estimation of models with omitted variable. The paper considers the implicit parametrization of statistical models and presents five conditions for an appropriate instruments. Two of them are empirically measurable and can be tested. This improves the literature by adding one more objective criterion for the selection of instruments. This chapter applies the IV method to estimate the rate of return to education in Iran. It argues that the education of two cohorts of Iranians was delayed or cut short by the Cultural Revolution. Therefore, the Cultural Revolution, as an exogenous shock to the supply of education, establishes the year of birth as the exogenous and relevant instrument for education. Using the standard Mincerian earnings function with control for experience, ethnicity, location of residence and sector of employment, the instrumental variable estimate of the return to schooling is equal to 5.6%. The estimation results indicate that the Iranian labor market values degrees more than years of schooling.
Ph. D.

Instrumental Variable (IV) methods are widely used in the analysis of economic data when the explanatory variable of interest is endogenous and so OLS estimation of the model is biased. However, if the instruments used do not strongly predict the endogenous variable being instrumented then the IV estimator will also give biased results. Weak Instrument Asymptotic theory can be used to model the strength of the instruments in Two-Stage Least Squares (2SLS) IV models and critical values have been developed to test for Weak Instruments in models with one time period. In the first part of this thesis I extend Weak Instrument Asymptotics to a model with multiple endogenous variables where the instruments available can strongly predict each of the endogenous variables separately but correlation between the endogenous variables means that they cannot be jointly predicted and so the overall strength of the instruments in the model is weak. I develop a partial F-statistic to test for 'Weak Instruments of this form and show that this test has the correct size using currently existing critical values for testing for Weak Instruments. I then extend the Weak Instrument Asymptotics to Panel Data models with multiple time periods, and one endogenous variable. I show that it is no longer possible to use the F -statistic to test for Weak Instruments but it is possible to use the Effective F-statistic developed by Olea and Pflueger (2013) with appropriately clustered standard errors to test for 'Weak Instruments in Pallel Data models. Finally, I extend this analysis to look a AR(l) panel data models and show that it is possible to control the strcngth of the instruments asymptotically by changing the persistence of the autoregressive process. I also show the Effective F-statistic has the correct size in these AR(l) models.

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This dissertation deals with the problem of making inference when there is weak identification in models of instrumental variables regression. More specifically we are interested in one-sided hypothesis testing for the coefficient of the endogenous variable when the instruments are weak. The focus is on the conditional tests based on likelihood ratio, score and Wald statistics. Theoretical and numerical work shows that the conditional t-test based on the two-stage least square (2SLS) estimator performs well even when instruments are weakly correlated with the endogenous variable. The conditional approach correct uniformly its size and when the population F-statistic is as small as two, its power is near the power envelopes for similar and non-similar tests. This finding is surprising considering the bad performance of the two-sided conditional t-tests found in Andrews, Moreira and Stock (2007). Given this counter intuitive result, we propose novel two-sided t-tests which are approximately unbiased and can perform as well as the conditional likelihood ratio (CLR) test of Moreira (2003).
Esta dissertação trata do problema de inferência na presença de identificação fraca em modelos de regresso com variáveis instrumentais. Mais especificamente em testes de hipóteses com relação ao parâmetro da variável endógena quando os instrumentos são fracos. O principal foco é nos testes condicionais unilaterais baseados nas estatísticas de razão de máxima verossimilhança, score e Wald. Resultados teóricos e numéricos mostram que o teste t condicional unilateral baseado no estimador de mínimos quadrados em dois estágios tem uma boa performance mesmo na presença de instrumentos fracamente correlacionados com a variável endógena. A abordagem condicional corrige uniformemente o tamanho do teste t e quando a estatística F populacional é tão pequena quanto dois, o poder do teste é próximo ao power envelope tanto de testes similares quanto de não similares. Tal resultado é surpreendente visto a má performance dos testes t’s condicionais bilaterais relatada em (6, Andrews, Moreira and Stock (2007)). Dado esse resultado aparentemente contra intuitivo, apresentamos novos testes t’s condicionals bilaterais que são aproximadamente não viesados e performam, em alguns casos, tão bem quanto o teste condicional baseado na estatística de razão de verossimilhança de ( 19 , Moreira (2003)).

(I A Few-Cluster-Robust Test for Weak Instruments) - ABSTRACT - In the usual IV regression models, the quality of estimation and inference dra- matically depends on the relevance of the set of instruments. To test such condition, most empiricists rely on the first-stage F-statistic as suggested by Stock and Yogo (2005a). However, this method is not valid for clustered data, especially when there are few clusters. Recently, weak instrument-robust methods for inference have been proposed, but none of them deals with the latter case. In this paper, we propose a simple method to perform inference which is robust both to the presence of weak instruments and few clusters in the case of a single endogenous regressor. (II Sharing or gambling over losses?) - ABSTRACT - This paper investigates experimentally whether individuals prefer to share an exogenous loss in a deterministic way or to gamble over it. In particular, in some scenarios subjects face an equal allocation of the loss, in others a social lottery with an equal chance to suffer it entirely. The loss is implemented after the endowment is earned. We find that the loss domain does not affect subjects' behavior directly, but it decreases their probability to play in a competitive way.

Two new specification tests for overidentifying restrictions proposed by Hahn and Hausman (2002:b) are here tested and compared to the classical Sargan test. Power properties are found to be very similar in overall performance, while Sargan generally has better size than the new tests. Also, size is distorted for one of the new tests, thus a tendency to reject prevails. In addition, sometimes severe bias is found which affects the tests’ performances, something that differs from earlier studies.

Les amas de galaxies sont des outils cosmologiques et astrophysiques essentiels, car ce sont les objets les plus grands et les plus massifs gravitationnellement liées dans l'Univers. L'étude de leur fonction de masse, de leur fonction de corrélation et des relations d'échelle entre leur masse et différentes observables nous permettent de tester les prévisions des modèles cosmologique et les scenarii de formation des structures. Ils sont aussi d'intéressants laboratoires pour l'étude de la formation et de l'évolution des galaxies, et de leur interactions avec le milieu qui les entourent, dans d’environnements denses. Pour y parvenir, estimer précisément leur masse revêt une importance fondamentale. J’ai étudié la précision de la richesse optique calculée par l’algorithme de détection d’amas RedGOLD (Licitra et al. 2016) en tant que mass proxy, en utilisant des mesures de lentilles gravitationnelles (weak lensing) et des observations en rayon X. J’ai mesuré les masses cumulées d’un échantillon de 1323 amas de galaxies dans le CFHTLS et NGVS à 0.2
Galaxy clusters are essential cosmological and astrophysical tools, since they represent the largest and most massive gravitationally bound structures in the Universe. Through the study of their mass function, of their correlation function, and of the scaling relations between their mass and different observables, we can probe the predictions of cosmological models and structure formation scenarios. They are also interesting laboratories that allow us to study galaxy formation and evolution, and their interactions with the intra-cluster medium, in dense environments. For all of these goals, an accurate estimate of cluster masses is of fundamental importance. I studied the accuracy of the optical richness obtained by the RedGOLD cluster detection algorithm (Licitra et al. 2016) as a mass proxy, using weak lensing and X-ray mass measurements. I measured stacked weak lensing cluster masses for a sample of 1323 galaxy clusters in the CFHTLS W1 and in the NGVS at 0.2

In recent years, increasing attention has turned toward the effect of health information or health knowledge on nutrient intake. In determining the effect of health information on nutrient demand, researchers face the estimation problem of dealing with the endogeneity of health information knowledge. The standard approach for dealing with this problem is an instrumental variables (IV) procedure. Unfortunately, recent research has demonstrated that the IV procedure may not be reliable in the types of data sets that contain health information and nutrient intakes because the instruments are not sufficiently correlated with the endogenous variables (i.e., instruments are weak). This thesis compares the reliability of the IV procedure (and the Hausman test) with a relatively new procedure, directed graphs, given weak instruments. The goal is to determine if the method of directed graphs performs better in identifying an endogenous variable and also relevant instruments. The performance of the Hausman test and directed graphs are first assessed through conducting a Monte-Carlo sampling experiment containing weak instruments. Because the structure of the model is known in the Monte-Carlo experiment, these results are used as a guideline to determine which procedure would be more reliable in a real world setting. The procedures are then applied to a real-world cross-sectional dataset on nutrient intake. This thesis provides empirical evidence that neither the IV estimator (and Hausman test) or the directed graphs are reliable when instruments are weak, as in a cross-sectional dataset.

Thesis (M.S.)--West Virginia University, 2010.
Title from document title page. Document formatted into pages; contains vi, 41 p. : ill. (some col.). Includes abstract. Includes bibliographical references (p. 37-41).

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This work studies the hypothesis testing based on generalized method of moments (GMM) estimation given by instruments condition. The importance for the development of Economics lies on the fact that when identi cation is weak, the standard test can be misleading. Therefore, it is made a review of proposed tests to overcome this problem and also present two useful frameworks of study; from Moreira (2002), Moreira and Moreira (2013) and Kleibergen (2005). So, this work conciliate the previous frameworks a way to write the score proposed initially in Kleibergen (2005) using Moreira and Moreira (2013) statistics and presents the optimal score test based on asymptotic theory from Newey and McFadden (1984). Moreover, the study shows the equivalence between the GMM and maximum likelihood estimation to deal with the weak instruments problem.
Esta dissertação se propõe ao estudo de inferência usando estimação por método generalizado dos momentos (GMM) baseado no uso de instrumentos. A motivação para o estudo está no fato de que sob identificação fraca dos parâmetros, a inferência tradicional pode levar a resultados enganosos. Dessa forma, é feita uma revisão dos mais usuais testes para superar tal problema e uma apresentação dos arcabouços propostos por Moreira (2002) e Moreira & Moreira (2013), e Kleibergen (2005). Com isso, o trabalho concilia as estatísticas utilizadas por eles para realizar inferência e reescreve o teste score proposto em Kleibergen (2005) utilizando as estatísticas de Moreira & Moreira (2013), e é obtido usando a teoria assintótica em Newey & McFadden (1984) a estatística do teste score ótimo. Além disso, mostra-se a equivalência entre a abordagem por GMM e a que usa sistema de equações e verossimilhança para abordar o problema de identificação fraca.

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In this work we focus on tests for the parameter of an endogenous variable in a weakly identi ed instrumental variable regressionmodel. We propose a new unbiasedness restriction for weighted average power (WAP) tests introduced by Moreira and Moreira (2013). This new boundary condition is motivated by the score e ciency under strong identi cation. It allows reducing computational costs of WAP tests by replacing the strongly unbiased condition. This latter restriction imposes, under the null hypothesis, the test to be uncorrelated to a given statistic with dimension given by the number of instruments. The new proposed boundary condition only imposes the test to be uncorrelated to a linear combination of the statistic. WAP tests under both restrictions to perform similarly numerically. We apply the di erent tests discussed to an empirical example. Using data from Yogo (2004), we assess the e ect of weak instruments on the estimation of the elasticity of inter-temporal substitution of a CCAPM model.
Este trabalho trata de testes para o parâmetro de uma variável endógena em modelos de regressão com variáveis instrumentais fracas. Propomos uma nova restrição para o viés dos testes weighted average power (WAP), desenvolvidos em Moreira e Moreira (16, 2013). A motivação para essa nova restrição se baseia na eficiência do teste score sob a hipótese de identificação forte. Essa hipótese permite reduzir o custo computacional dos testes WAP, substituindo a restrição de strongly unbiased. Esta ultima demanda que, sob a hipótese nula, o teste seja ortogonal a uma dada estatística com sua dimensão dada pelo número de instrumentos. A restrição aqui proposta exige somente que o teste seja não correlacionado com uma combinação linear dessa estatística. Nas simulações, ambos os testes apresentam um desempenho numericamente similar. Aplicamos ainda os testes discutidos neste trabalho na estimação da elasticidade de substituição intertemporal de um modelo CCAPM.

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This thesis contains three chapters. The first chapter considers tests of the parameter of an endogenous variable in an instrumental variables regression model. The focus is on one-sided conditional t-tests. Theoretical and numerical work shows that the conditional 2SLS and Fuller t-tests perform well even when instruments are weakly correlated with the endogenous variable. When the population F-statistic is as small as two, the power is reasonably close to the power envelopes for similar and non-similar tests which are invariant to rotation transformations of the instruments. This finding is surprising considering the poor performance of two-sided conditional t-tests found in Andrews, Moreira, and Stock (2007). These tests have bad power because the conditional null distributions of t-statistics are asymmetric when instruments are weak. Taking this asymmetry into account, we propose two-sided tests based on t-statistics. These novel tests are approximately unbiased and can perform as well as the conditional likelihood ratio (CLR) test. The second and third chapters are interested in maxmin and minimax regret tests for broader hypothesis testing problems. In the second chapter, we present maxmin and minimax regret tests satisfying more general restrictions than the alpha-level and the power control over all alternative hypothesis constraints. More general restrictions enable us to eliminate trivial known tests and obtain tests with desirable properties, such as unbiasedness, local unbiasedness and similarity. In sequence, we prove that both tests always exist and under suficient assumptions, they are Bayes tests with priors that are solutions of an optimization problem, the dual problem. In the last part of the second chapter, we consider testing problems that are invariant to some group of transformations. Under the invariance of the hypothesis testing, the Hunt-Stein Theorem proves that the search for maxmin and minimax regret tests can be restricted to invariant tests. We prove that the Hunt-Stein Theorem still holds under the general constraints proposed. In the last chapter we develop a numerical method to implement maxmin and minimax regret tests proposed in the second chapter. The parametric space is discretized in order to obtain testing problems with a finite number of restrictions. We prove that, as the discretization turns finer, the maxmin and the minimax regret tests satisfying the finite number of restrictions have the same alternative power of the maxmin and minimax regret tests satisfying the general constraints. Hence, we can numerically implement tests for a finite number of restrictions as an approximation for the tests satisfying the general constraints. The results in the second and third chapters extend and complement the maxmin and minimax regret literature interested in characterizing and implementing both tests.
Esta tese contém três capítulos. O primeiro capítulo considera testes de hipóteses para o coeficiente de regressão da variável endógena em um modelo de variáveis instrumentais. O foco é em testes-t condicionais para hipóteses unilaterais. Trabalhos teóricos e numéricos mostram que os testes-t condicionais centrados nos estimadores de 2SLS e Fuller performam bem mesmo quando os instrumentos são fracamente correlacionados com a variável endógena. Quando a estatística F populacional é menor que dois, o poder é razoavelmente próximo do poder envoltório para testes que são invariantes a transformações que rotacionam os instrumentos (similares ou não similares). Este resultado é surpreendente considerando a baixa performance dos testes-t condicionais para hipóteses bilaterais apresentado em Andrews, Moreira, and Stock (2007). Estes testes possuem baixo poder porque as distribuições das estatísticas-t na hipótese nula são assimétricas quando os instrumentos são fracos. Explorando tal assimetria, nós propomos testes para hipóteses bilaterais baseados em estatísticas-t. Estes testes são aproximadamente não viesados e podem performar tão bem quanto o teste de razão de máxima verossimilhança condicional. No segundo e no terceiro capítulos, nosso interesse é em testes do tipo maxmin e minimax regret para testes de hipóteses mais gerais. No segundo capítulo, nós apresentamos testes maxmin e minimax regret que satisfazem restrições mais gerais que as restrições de tamanho e de controle sobre todo o poder na hipótese alternativa. Restrições mais gerais nos possibilitam eliminar testes triviais e obter testes com propriedades desejáveis, como por exemplo não viés, não viés local e similaridade. Na sequência, nós provamos que ambos os testes existem e, sob condições suficientes, eles são testes Bayesianos com priors que são solução de um problema de otimização, o problema dual. Na última parte do segundo capítulo, nós consideramos testes de hipóteses que são invariantes à algum grupo de transformações. Sob invariância, o Teorema de Hunt-Stein implica que a busca por testes maxmin e minimax regret pode ser restrita a testes invariantes. Nós provamos que o Teorema de Hunt-Stein continua válido sob as restrições gerais propostas. No último capítulo, nós desenvolvemos um procedimento numérico para implementar os testes maxmin e minimax regret propostos no segundo capítulo. O espaço paramétrico é discretizado com o objetivo de obter testes de hipóteses com um número finito de pontos. Nós provamos que, ao considerarmos partições mais finas, os testes maxmin e minimax regret que satisfazem um número finito de pontos possuem o mesmo poder na hipótese alternativa que os testes maxmin e minimax regret que satisfazem as restrições gerais. Portanto, nós podemos implementar numericamente os testes que satisfazem um número finito de pontos como aproximação aos testes que satisfazem as restrições gerais.

Today, the rapid development of the industry uses new materials for cutting tools. Besides the production of new types of solid high-speed steel and alloys, focuses on strengthening and surface protection products different surfaces. One of the main trends in this area is to develop wear-resistant coatings and their application to cutting tools [1], watches with PVD-coated, glasses with gold frames and others. This is made possible by the application as coating materials based on Ti and Zr, Mo and Cr on the use of technology in active gas mixture of nitrogen N [2].

This thesis studies inference to the complier treatment effect denoted LATE. The standard approach is to base the inference on the two-stage least squares (2SLS) estimator and asymptotic Neyman inference, i.e., the t-test. The paper suggests a Fisher Randomization Test based on the t-test statistic as an alternative to the Neyman inference. Based on the setup with a randomized experiment with noncompliance, for which one can identify the LATE, I compare the two approaches in a Monte Carlo (MC) simulations. The results from the MC simulation is that the Fisher randomization test is not a valid alternative to the Neyman’s test as it has too low power.

Thesis (M.S.)--West Virginia University, 2009.
Title from document title page. Document formatted into pages; contains viii, 143 p. : ill. (some col.). Includes abstract. Includes bibliographical references.

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.

Every year millions of children worldwide are forced into the labour pool. More than half of these children are involved in some of the worst forms of child labour, which include slavery, work under hazardous conditions and illegal activities such as drug trafficking and prostitution. Children involved in child labour are usually deprived of access to basics such as proper nutrition, adequate shelter, education, basic hygiene and healthcare, and recreation. Due to the increase in poverty across the world, the vulnerability of children in child labour, and their need for income to survive in a poverty-stricken environment, these children simply cannot escape from the exploitative practices and worst forms of labour. Despite the dangers associated with child labour, not all forms of work done by children are hazardous to them. The income earned by children in this way contributes substantially to their own survival and that of their families. Work can also help children acquire certain skills, which can contribute to their development. This dissertation investigates, in the light of the current socio-economic circumstances of South Africa, the possibility to allow children to work for their survival and simultaneously to protect them through regulatory measures from exploitative labour practices and the worst forms of child labour. The dissertation starts with a general overview of child labour, including the historical development of the regulation of child labour, problems encountered in defining child labour, advantages and disadvantages of child labour as well as the various current perspectives and approaches to child labour. Special attention is given to the importance of education and the current problems experienced in the education system (which needs to be adjusted for child labourers) as well as the situation regarding girls in the labour market. An exploration of the international legal framework applicable to child labour is coupled with a description of the current international conventions as well as international initiatives, which mainly seek to eliminate child labour. Emphasis is placed on the most important conventions that have a direct impact on child labour: The United Nations Convention on the Rights of the Child, the International Labour Organisation's conventions and the African Charter on the Rights and Welfare of the Child. Subsequently, the dissertation canvasses the approach to child labour in South Africa with an overview of the nature and extent of child labour in South Africa and an exploration of the nexus between international law and South African national law. The author expounds in detail on the current South African national legal framework applicable to children and child labour, as well as the variety of South-African programmes that seek to effectively regulate child labour. By way of comparison, the author investigates the approach to child labour in India, exploring the socio-economic and political circumstances as well as the extent of child labour in India, as also the federal law and child labour programmes that regulate child labour in India. India’s approach to child labour is compared with that of South Africa to gauge how the two differ and what South-Africa can adopt from India. The dissertation critically evaluates the South African approach to child labour and offers proposals to address the challenges in limiting, if not eradicating, the worst forms of child labour and its exploitative practices in South Africa. The author reviews and evaluates the measures and programmes implemented in India, with a view to improve the position of children involved in child labour in South Africa. The author closes by critically analysing the hypothesis posed at the outset of this study: that a judicious acceptance of child labour (excluding its worst forms), when considered in the context of its proper and effective regulation through legislation and enforcement, is the best approach to combat the perils associated with child labour in South Africa.
Dissertation (LLM)--University of Pretoria, 2012.
Private Law
unrestricted

Quantifier et mesurer le risque dans un environnement partiellement ou totalement incertain est probablement l'un des enjeux majeurs de la recherche appliquée en mathématiques financières. Cela concerne l'économie, la finance, mais d'autres domaines comme la santé via les assurances par exemple. L'une des difficultés fondamentales de ce processus de gestion des risques est de modéliser les actifs sous-jacents, puis d'approcher le risque à partir des observations ou des simulations. Comme dans ce domaine, l'aléa ou l'incertitude joue un rôle fondamental dans l'évolution des actifs, le recours aux processus stochastiques et aux méthodes statistiques devient crucial. Dans la pratique l'approche paramétrique est largement utilisée. Elle consiste à choisir le modèle dans une famille paramétrique, de quantifier le risque en fonction des paramètres, et d'estimer le risque en remplaçant les paramètres par leurs estimations. Cette approche présente un risque majeur, celui de mal spécifier le modèle, et donc de sous-estimer ou sur-estimer le risque. Partant de ce constat et dans une perspective de minimiser le risque de modèle, nous avons choisi d'aborder la question de la quantification du risque avec une approche non-paramétrique qui s'applique à des modèles aussi généraux que possible. Nous nous sommes concentrés sur deux mesures de risque largement utilisées dans la pratique et qui sont parfois imposées par les réglementations nationales ou internationales. Il s'agit de la Value at Risk (VaR) qui quantifie le niveau de perte maximum avec un niveau de confiance élevé (95% ou 99%). La seconde mesure est l'Expected Shortfall (ES) qui nous renseigne sur la perte moyenne au delà de la VaR.

La dernière décennie a connu un intérêt croissant pour les problèmes posés par les variables instrumentales faibles dans la littérature économétrique, c’est-à-dire les situations où les variables instrumentales sont faiblement corrélées avec la variable à instrumenter. En effet, il est bien connu que lorsque les instruments sont faibles, les distributions des statistiques de Student, de Wald, du ratio de vraisemblance et du multiplicateur de Lagrange ne sont plus standard et dépendent souvent de paramètres de nuisance. Plusieurs études empiriques portant notamment sur les modèles de rendements à l’éducation [Angrist et Krueger (1991, 1995), Angrist et al. (1999), Bound et al. (1995), Dufour et Taamouti (2007)] et d’évaluation des actifs financiers (C-CAPM) [Hansen et Singleton (1982,1983), Stock et Wright (2000)], où les variables instrumentales sont faiblement corrélées avec la variable à instrumenter, ont montré que l’utilisation de ces statistiques conduit souvent à des résultats peu fiables. Un remède à ce problème est l’utilisation de tests robustes à l’identification [Anderson et Rubin (1949), Moreira (2002), Kleibergen (2003), Dufour et Taamouti (2007)]. Cependant, il n’existe aucune littérature économétrique sur la qualité des procédures robustes à l’identification lorsque les instruments disponibles sont endogènes ou à la fois endogènes et faibles. Cela soulève la question de savoir ce qui arrive aux procédures d’inférence robustes à l’identification lorsque certaines variables instrumentales supposées exogènes ne le sont pas effectivement. Plus précisément, qu’arrive-t-il si une variable instrumentale invalide est ajoutée à un ensemble d’instruments valides? Ces procédures se comportent-elles différemment? Et si l’endogénéité des variables instrumentales pose des difficultés majeures à l’inférence statistique, peut-on proposer des procédures de tests qui sélectionnent les instruments lorsqu’ils sont à la fois forts et valides? Est-il possible de proposer les proédures de sélection d’instruments qui demeurent valides même en présence d’identification faible? Cette thèse se focalise sur les modèles structurels (modèles à équations simultanées) et apporte des réponses à ces questions à travers quatre essais. Le premier essai est publié dans Journal of Statistical Planning and Inference 138 (2008) 2649 – 2661. Dans cet essai, nous analysons les effets de l’endogénéité des instruments sur deux statistiques de test robustes à l’identification: la statistique d’Anderson et Rubin (AR, 1949) et la statistique de Kleibergen (K, 2003), avec ou sans instruments faibles. D’abord, lorsque le paramètre qui contrôle l’endogénéité des instruments est fixe (ne dépend pas de la taille de l’échantillon), nous montrons que toutes ces procédures sont en général convergentes contre la présence d’instruments invalides (c’est-à-dire détectent la présence d’instruments invalides) indépendamment de leur qualité (forts ou faibles). Nous décrivons aussi des cas où cette convergence peut ne pas tenir, mais la distribution asymptotique est modifiée d’une manière qui pourrait conduire à des distorsions de niveau même pour de grands échantillons. Ceci inclut, en particulier, les cas où l’estimateur des double moindres carrés demeure convergent, mais les tests sont asymptotiquement invalides. Ensuite, lorsque les instruments sont localement exogènes (c’est-à-dire le paramètre d’endogénéité converge vers zéro lorsque la taille de l’échantillon augmente), nous montrons que ces tests convergent vers des distributions chi-carré non centrées, que les instruments soient forts ou faibles. Nous caractérisons aussi les situations où le paramètre de non centralité est nul et la distribution asymptotique des statistiques demeure la même que dans le cas des instruments valides (malgré la présence des instruments invalides). Le deuxième essai étudie l’impact des instruments faibles sur les tests de spécification du type Durbin-Wu-Hausman (DWH) ainsi que le test de Revankar et Hartley (1973). Nous proposons une analyse en petit et grand échantillon de la distribution de ces tests sous l’hypothèse nulle (niveau) et l’alternative (puissance), incluant les cas où l’identification est déficiente ou faible (instruments faibles). Notre analyse en petit échantillon founit plusieurs perspectives ainsi que des extensions des précédentes procédures. En effet, la caractérisation de la distribution de ces statistiques en petit échantillon permet la construction des tests de Monte Carlo exacts pour l’exogénéité même avec les erreurs non Gaussiens. Nous montrons que ces tests sont typiquement robustes aux intruments faibles (le niveau est contrôlé). De plus, nous fournissons une caractérisation de la puissance des tests, qui exhibe clairement les facteurs qui déterminent la puissance. Nous montrons que les tests n’ont pas de puissance lorsque tous les instruments sont faibles [similaire à Guggenberger(2008)]. Cependant, la puissance existe tant qu’au moins un seul instruments est fort. La conclusion de Guggenberger (2008) concerne le cas où tous les instruments sont faibles (un cas d’intérêt mineur en pratique). Notre théorie asymptotique sous les hypothèses affaiblies confirme la théorie en échantillon fini. Par ailleurs, nous présentons une analyse de Monte Carlo indiquant que: (1) l’estimateur des moindres carrés ordinaires est plus efficace que celui des doubles moindres carrés lorsque les instruments sont faibles et l’endogenéité modérée [conclusion similaire à celle de Kiviet and Niemczyk (2007)]; (2) les estimateurs pré-test basés sur les tests d’exogenété ont une excellente performance par rapport aux doubles moindres carrés. Ceci suggère que la méthode des variables instrumentales ne devrait être appliquée que si l’on a la certitude d’avoir des instruments forts. Donc, les conclusions de Guggenberger (2008) sont mitigées et pourraient être trompeuses. Nous illustrons nos résultats théoriques à travers des expériences de simulation et deux applications empiriques: la relation entre le taux d’ouverture et la croissance économique et le problème bien connu du rendement à l’éducation. Le troisième essai étend le test d’exogénéité du type Wald proposé par Dufour (1987) aux cas où les erreurs de la régression ont une distribution non-normale. Nous proposons une nouvelle version du précédent test qui est valide même en présence d’erreurs non-Gaussiens. Contrairement aux procédures de test d’exogénéité usuelles (tests de Durbin-Wu-Hausman et de Rvankar- Hartley), le test de Wald permet de résoudre un problème courant dans les travaux empiriques qui consiste à tester l’exogénéité partielle d’un sous ensemble de variables. Nous proposons deux nouveaux estimateurs pré-test basés sur le test de Wald qui performent mieux (en terme d’erreur quadratique moyenne) que l’estimateur IV usuel lorsque les variables instrumentales sont faibles et l’endogénéité modérée. Nous montrons également que ce test peut servir de procédure de sélection de variables instrumentales. Nous illustrons les résultats théoriques par deux applications empiriques: le modèle bien connu d’équation du salaire [Angist et Krueger (1991, 1999)] et les rendements d’échelle [Nerlove (1963)]. Nos résultats suggèrent que l’éducation de la mère expliquerait le décrochage de son fils, que l’output est une variable endogène dans l’estimation du coût de la firme et que le prix du fuel en est un instrument valide pour l’output. Le quatrième essai résout deux problèmes très importants dans la littérature économétrique. D’abord, bien que le test de Wald initial ou étendu permette de construire les régions de confiance et de tester les restrictions linéaires sur les covariances, il suppose que les paramètres du modèle sont identifiés. Lorsque l’identification est faible (instruments faiblement corrélés avec la variable à instrumenter), ce test n’est en général plus valide. Cet essai développe une procédure d’inférence robuste à l’identification (instruments faibles) qui permet de construire des régions de confiance pour la matrices de covariances entre les erreurs de la régression et les variables explicatives (possiblement endogènes). Nous fournissons les expressions analytiques des régions de confiance et caractérisons les conditions nécessaires et suffisantes sous lesquelles ils sont bornés. La procédure proposée demeure valide même pour de petits échantillons et elle est aussi asymptotiquement robuste à l’hétéroscédasticité et l’autocorrélation des erreurs. Ensuite, les résultats sont utilisés pour développer les tests d’exogénéité partielle robustes à l’identification. Les simulations Monte Carlo indiquent que ces tests contrôlent le niveau et ont de la puissance même si les instruments sont faibles. Ceci nous permet de proposer une procédure valide de sélection de variables instrumentales même s’il y a un problème d’identification. La procédure de sélection des instruments est basée sur deux nouveaux estimateurs pré-test qui combinent l’estimateur IV usuel et les estimateurs IV partiels. Nos simulations montrent que: (1) tout comme l’estimateur des moindres carrés ordinaires, les estimateurs IV partiels sont plus efficaces que l’estimateur IV usuel lorsque les instruments sont faibles et l’endogénéité modérée; (2) les estimateurs pré-test ont globalement une excellente performance comparés à l’estimateur IV usuel. Nous illustrons nos résultats théoriques par deux applications empiriques: la relation entre le taux d’ouverture et la croissance économique et le modèle de rendements à l’éducation. Dans la première application, les études antérieures ont conclu que les instruments n’étaient pas trop faibles [Dufour et Taamouti (2007)] alors qu’ils le sont fortement dans la seconde [Bound (1995), Doko et Dufour (2009)]. Conformément à nos résultats théoriques, nous trouvons les régions de confiance non bornées pour la covariance dans le cas où les instruments sont assez faibles.
The last decade shows growing interest for the so-called weak instruments problems in the econometric literature, i.e. situations where instruments are poorly correlated with endogenous explanatory variables. More generally, these can be viewed as situations where model parameters are not identified or nearly so (see Dufour and Hsiao, 2008). It is well known that when instruments are weak, the limiting distributions of standard test statistics - like Student, Wald, likelihood ratio and Lagrange multiplier criteria in structural models - have non-standard distributions and often depend heavily on nuisance parameters. Several empirical studies including the estimation of returns to education [Angrist and Krueger (1991, 1995), Angrist et al. (1999), Bound et al. (1995), Dufour and Taamouti (2007)] and asset pricing model (C-CAPM) [Hansen and Singleton (1982, 1983), Stock and Wright (2000)], have showed that the above procedures are unreliable in presence of weak identification. As a result, identification-robust tests [Anderson and Rubin (1949), Moreira (2003), Kleibergen (2002), Dufour and Taamouti (2007)] are often used to make reliable inference. However, little is known about the quality of these procedures when the instruments are invalid or both weak and invalid. This raises the following question: what happens to inference procedures when some instruments are endogenous or both weak and endogenous? In particular, what happens if an invalid instrument is added to a set of valid instruments? How robust are these inference procedures to instrument endogeneity? Do alternative inference procedures behave differently? If instrument endogeneity makes statistical inference unreliable, can we propose the procedures for selecting "good instruments" (i.e. strong and valid instruments)? Can we propose instrument selection procedure which will be valid even in presence of weak identification? This thesis focuses on structural models and answers these questions through four chapiters. The first chapter is published in Journal of Statistical Planning and Inference 138 (2008) 2649 – 2661. In this chapter, we analyze the effects of instrument endogeneity on two identificationrobust procedures: Anderson and Rubin (1949, AR) and Kleibergen (2002, K) test statistics, with or without weak instruments. First, when the level of instrument endogeneity is fixed (does not depend on the sample size), we show that all these procedures are in general consistent against the presence of invalid instruments (hence asymptotically invalid for the hypothesis of interest), whether the instruments are "strong" or "weak". We also describe situations where this consistency may not hold, but the asymptotic distribution is modified in a way that would lead to size distortions in large samples. These include, in particular, cases where 2SLS estimator remains consistent, but the tests are asymptotically invalid. Second, when the instruments are locally exogenous (the level of instrument endogeneity approaches zero as the sample size increases), we find asymptotic noncentral chi-square distributions with or without weak instruments, and describe situations where the non-centrality parameter is zero and the asymptotic distribution remains the same as in the case of valid instruments (despite the presence of invalid instruments). The second chapter analyzes the effects of weak identification on Durbin-Wu-Hausman (DWH) specification tests an Revankar-Harttley exogeneity test. We propose a finite-and large-sample analysis of the distribution of DWH tests under the null hypothesis (level) and the alternative hypothesis (power), including when identification is deficient or weak (weak instruments). Our finite-sample analysis provides several new insights and extensions of earlier procedures. The characterization of the finite-sample distribution of the test-statistics allows the construction of exact identificationrobust exogeneity tests even with non-Gaussian errors (Monte Carlos tests) and shows that such tests are typically robust to weak instruments (level is controlled). Furthermore, we provide a characterization of the power of the tests, which clearly exhibits factors which determine power. We show that DWH-tests have no power when all instruments are weak [similar to Guggenberger(2008)]. However, power does exist as soon as we have one strong instruments. The conclusions of Guggenberger (2008) focus on the case where all instruments are weak (a case of little practical interest). Our asymptotic distributional theory under weaker assumptions confirms the finite-sample theory. Moreover, we present simulation evidence indicating: (1) over a wide range cases, including weak IV and moderate endogeneity, OLS performs better than 2SLS [finding similar to Kiviet and Niemczyk (2007)]; (2) pretest-estimators based on exogeneity tests have an excellent overall performance compared with usual IV estimator. We illustrate our theoretical results through simulation experiment and two empirical applications: the relation between trade and economic growth and the widely studied problem of returns to education. In the third chapter, we extend the generalized Wald partial exogeneity test [Dufour (1987)] to non-gaussian errors. Testing whether a subset of explanatory variables is exogenous is an important challenge in econometrics. This problem occurs in many applied works. For example, in the well know wage model, one should like to assess if mother’s education is exogenous without imposing additional assumptions on ability and schooling. In the growth model, the exogeneity of the constructed instrument on the basis of geographical characteristics for the trade share is often questioned and needs to be tested without constraining trade share and the other variables. Standard exogeneity tests of the type proposed by Durbin-Wu-Hausman and Revankar-Hartley cannot solve such problems. A potential cure for dealing with partial exogeneity is the use of the generalized linear Wald (GW) method (Dufour, 1987). The GW-procedure however assumes the normality of model errors and it is not clear how robust is this test to non-gaussian errors. We develop in this chapter, a modified version of earlier procedure which is valid even when model errors are not normally distributed. We present simulation evidence indicating that when identification is strong, the standard GW-test is size distorted in presence of non-gaussian errors. Furthermore, our analysis of the performance of different pretest-estimators based on GW-tests allow us to propose two new pretest-estimators of the structural parameter. The Monte Carlo simulations indicate that these pretest-estimators have a better performance over a wide range cases compared with 2SLS. Therefore, this can be viewed as a procedure for selecting variable where a GW-test is used in the first stage to decide which variables should be instruments and which ones are valid instruments. We illustrate our theoretical results through two empirical applications: the well known wage equation and the returns to scale in electricity supply. The results show that the GW-tests cannot reject the exogeneity of mother’s education, i.e. mother’s education may constitute a valid IV for schooling. However, the output in cost equation is endogenous and the price of fuel is a valid IV for estimating the returns to scale. The fourth chapter develops identification-robust inference for the covariances between errors and regressors of an IV regression. The results are then applied to develop partial exogeneity tests and partial IV pretest-estimators which are more efficient than usual IV estimator. When more than one stochastic explanatory variables are involved in the model, it is often necessary to determine which ones are independent of the disturbances. This problem arises in many empirical applications. For example, in the New Keynesian Phillips Curve, one should like to assess whether the interest rate is exogenous without imposing additional assumptions on inflation rate and the other variables. Standard Wu-Durbin-Hausman (DWH) tests which are commonly used in applied work are inappropriate to deal with such a problem. The generalized Wald (GW) procedure (Dufour, 1987) which typically allows the construction of confidence sets as well as testing linear restrictions on covariances assumes that the available instruments are strong. When the instruments are weak, the GW-test is in general size distorted. As a result, its application in models where instruments are possibly weak–returns to education, trade and economic growth, life cycle labor supply, New Keynesian Phillips Curve, pregnancy and the demand for cigarettes–may be misleading. To answer this problem, we develop a finite-and large-sample valid procedure for building confidence sets for covariances allowing for the presence of weak instruments. We provide analytic forms of the confidence sets and characterize necessary and sufficient conditions under which they are bounded. Moreover, we propose two new pretest-estimators of structural parameters based on our above procedure. Both estimators combine 2SLS and partial IV-estimators. The Monte Carlo experiment shows that: (1) partial IV-estimators outperform 2SLS when the instruments are weak; (2) pretestestimators have an excellent overall performance–bias and MSE– compared with 2SLS. Therefore, this can be viewed as a variable selection method where the projection-based techniques is used to decide which variables should be instrumented and which ones are valid instruments. We illustrate our results through two empirical applications: the relation between trade and economic growth and the widely studied problem of returns to education. The results show unbounded confidence sets, suggesting that the IV are relatively poor in these models, as questioned in the literature [Bound (1995)].

Tese no âmbito do doutoramento em Relações Internacionais, Política Internacional e Resolução de conflitos, apresentada à Faculdade de Economia da Universidade de Coimbra
Extensive literature produced on the history of the Israeli state has reproduced a dichotomy between the Palestinian citizens of the State and a homogeneous Jewish majority. This dichotomy is itself nourished by International Relations’ academic neglect to study nation- and state-building processes, which inevitably mask processes of social fragmentation and polarization between ethnic, cultural, and religious groups. In this thesis we argue that nation-building in Israel has been undermined by ethnic and religious tensions that date back to the establishment of the State. The exclusion of several Jewish groups from the centers of power leads to the reinforcement of their group identities, and even to their political mobilization along ethnic and religious lines. Confronted with this situation, Israeli leaderships choose to construct the Palestinian minority as a threat, and Israel national identity becomes negatively dependent on the presence of a distinct outgroup. By applying Barry Buzan’s concept of “weak states” to the Israeli case study, we aim to understand how cumulative securitization processes, which transform minorities into security threats, can contribute to a positive (albeit unstable) identification with the state. This approach to securitization as a political theory of security, allows us to understand how collective identities are forged and reinforced through securitization; and to focus on the marginalization and exclusion of other groups, often hidden behind a security discourse. Through an in-depth hystorical analysis of state- and nation-building efforts in Israel, and resorting to an analysis of the Israeli leaderships discourse in the post-second Intifada period (2000-2018), we will highlight the main loci of struggle among Israeli Jewish ethnic and religious groups, and point out how the presence of a seemingly threatening and hostile Palestinian minority fosters collective solidarity among the Israeli Jewish majority, while simultaneoulsy hinders the process of nation-building.
A extensa literatura produzida sobre a história do Estado de Israel tende a opor os cidadãos Palestinianos de Israel a uma maioria judaica homogénea. Esta dicotomia é alimentada pela falta de atenção oferecida pela área das Relações Internationais ao estudo dos processos de construção nacional e estatal, o que inevitavelmente esconde processos de fragmentação e polarização social entre grupos étnicos, culturais e religiosos. Nesta tese argumentamos que a construção nacional em Israel tem sido minada pelas tensões étnicas e religiosas que remontam à criação do Estado. A exclusão de vários grupos judaicos dos centros de poder conduz à intensificação das suas identidades de grupo, e até à sua mobilização política em função de critérios étnicos e religiosos. Confrontadas com esta situação, as lideranças Israelitas escolhem identificar a minoria Palestiniana como uma ameaça, criando uma dependência negativa da identidade nacional israelita em relação à presença de um grupo distinto. Através da aplicação do conceito de “estados fracos” de Barry Buzan ao estudo de caso Israelita, pretendemos compreender de que forma processos cumulativos de securitização, que transformam minorias em ameaças à segurança, podem contribuir para uma identificação positiva (ainda que instável) com o Estado. Este enfoque na securitização enquanto teoria política de segurança permite compreender de que forma as identidades coletivas são construídas e reproduzidas através da securitização, assim como identificar a marginalização e exclusão de outros grupos, frequentemente mascaradas pelo discurso securitátio. Através de uma análise histórica aprofundada dos processos de construção nacional e estatal em Israel, e procedendo à análise do discurso das lideranças israelitas no período pós-segunda Intifada (2000-2018), destacaremos os principais focos de disputa entre os diferentes grupos étnicos e religiosos israelitas, e indicaremos como a presença de uma minoria palestiniana, vista como hostil e ameaçadora, encoraja sentimentos de solidariedade coletiva entre a maioria judaica, ao mesmo tempo que coloca entraves ao processo de construção nacional.

Σε ένα γραμμικό υπόδειγμα βοηθητικών μεταβλητών η ασθενής συσχέτιση των βοηθητικών μεταβλητών με τις ενδογενείς ερμηνευτικές μεταβλητές είναι γνωστή στη βιβλιογραφία ως το ζήτημα των ασθενών βοηθητικών μεταβλητών. Στην παρούσα εργασία διερευνώνται διάφορες πτυχές του εν λόγω ζητήματος και επισημαίνονται πιθανές μέθοδοι για την αντιμετώπισή του. Επίσης, μελετάται η απόδοση των εκτιμητών OLS, TSLS, BTSLS, LIML και Fuller-k κάτω από την υπόθεση των ασθενών βοηθητικών μεταβλητών, μέσω ενός πειράματος Monte Carlo, με τα αποτελέσματα να τεκμηριώνουν τη δυσκολία λήψης αξιόπιστων σημειακών εκτιμήσεων.
Weak instruments arise when the instruments in linear instrumental variables (IV) regression are weakly correlated with the included endogenous variables. We review most of the recent studies on weak instruments and point to several methods that have been proposed to deal with such instruments. Using a Monte Carlo experiment we study the performance of OLS, TSLS, BTSLS, LIML and Fuller-k estimators under weak instruments. Our results indicate the difficulty of obtaining reliable point estimates.

Over the past few decades it has become evident that the work/family interface is a much broader concept that does not only stress the negative side of the relationship, but also include a positive side. This refers to the process by which participation in one role (e.g. work role) is made better or easier by virtue of participation in the other role (e.g. family role). South Africa is a multicultural society, which consists of four groups (i.e. Black, White, Coloured and Indian), speaking eleven official languages. All of these groups are faced with unique and different circumstances. Apart from cultural, ethnic and linguistic differences, other divergent elements may exist (i.e. values and norms). Therefore South African employees may experience the positive side of the work/family interface differently from employees within other countries. To add to the problem, it is not clear how South African employees‟ experiences of enrichment between work and family domains compare to the experiences of employees in other countries. Furthermore, to date no measuring instrument to assess the enrichment between work and family domains in both directions (work-to-family and family-to-work) exists, that is unique to the South African context. This could pose potential problems for organisations and for future studies on the positive side of work/family in South Africa. The objectives of this research were 1) to determine how the positive side of the work/family interface, particularly work-family enrichment, is conceptualised according to the literature; 2) to develop a new work-family enrichment instrument that is suitable for the South African context and that addresses conceptual and measurement issues relating to previous positive measurements of the work/family interface; 3) to investigate the psychometric properties of the newly developed work-family enrichment instrument; and 4) to assess antecedents and outcomes of work-family enrichment among employees within the South African context. The study consisted of four phases. During the first phase, following an extensive review of literature covering the positive side of the work/family interface, a theoretical framework was proposed for the study. Thereafter, a new instrument that measures work-family enrichment was developed based on the proposed theoretical framework. The instrument was tested via Rasch modelling with a pre-limenary study (N = 527), in order to overcome some of the measurement limitations from the previous positive work-family instruments. This test was followed by investigating the psychometric properties (i.e. construct validity, discriminant validity, convergent validity and external validity; N = 627) of the newly developed MACE Work-Family Enrichment Instrument. During the final phase, antecedents, work-family enrichment and outcomes were assessed in the South African context. In both phases 3 and 4, the following instruments (accompanied by the new instrument) were utilised, namely the Work Resources Scale, Home Resources Scale, Utrecht Work Engagement Scale, Family Engagement Scale, Job Satisfaction Scale, Career Satisfaction Scale, Life Satisfaction Scale, Family Satisfaction Scale and the Work-family Enrichment Scale. During the first phase, the literature revealed that the positive side of the work-family interface is presented by various concepts (i.e. work-family enhancement, work-family facilitation, work-family positive spillover and work-family enrichment). The review also revealed that, to date, the work-family enrichment concept has been the only concept in literature on the positive work/family interface that is grounded in a properly developed conceptualised theoretical model. The fundamental thinking behind the work-family enrichment model is that work and family each provides individuals with resources (i.e. skills and perspectives, psychological and physical, social-capital, flexibility, material) in the one domain, that may help the individual improve the quality of his/her performance in the other domain. These resources thus enable improved performance in the other role either directly (i.e. instrumental path) or indirectly (i.e. affective path). During the second phase a new work-family enrichment instrument was developed, namely the MACE Work-Family Enrichment Instrument. This instrument was based on the proposed work-family enrichment theoretical model for both directions (i.e. work-to-family and family-to-work). Initially 133 items were developed that the researcher obtained from the existing literature, and 161 items were self-developed. During the evaluation study, various problematic items were eliminated by using the Rasch measurement model. The third phase included the validation study in which the psychometric properties of the new MACE instrument was investigated. The results provided evidence for construct validity, discriminant validity and convergent validity, and showed significant relations with external variables. Adequate internal consistency was also found for the proposed scales. The final number of items retained after this phase in the development and pilot study of the MACE Work-Family Enrichment Instrument were 34. During the final phase, various relationships were pointed out between antecedents (i.e. various work resources and home resources), work-family enrichment dimensions, as well as dimensions and outcomes of this type of enrichment. These included work-engagement dimensions, family engagement dimensions, as well as satisfaction-dimensions for work, career, life and the family environment. The results of these relationships were found to be in accordance with other literature on the positive side of the work/family interface. The present study provided evidence for the psychometric properties of the new MACE instrument, which researchers and managers can use to investigate the specific enrichment between work and family domains of employees in a South African context. The results give researchers and managers insight into the specific antecedents (e.g. work resources) and outcomes (e.g. job satisfaction) that play a role in work-family enrichment. This insight can be used as basis on which interventions can be developed to deal with these issues currently. Recommendations were also made for future research.
PhD (Industrial Psychology), North-West University, Potchefstroom Campus, 2014

Ma thèse est composée de trois essais sur l'inférence par le bootstrap à la fois dans les modèles de données de panel et les modèles à grands nombres de variables instrumentales #VI# dont un grand nombre peut être faible. La théorie asymptotique n'étant pas toujours une bonne approximation de la distribution d'échantillonnage des estimateurs et statistiques de tests, je considère le bootstrap comme une alternative. Ces essais tentent d'étudier la validité asymptotique des procédures bootstrap existantes et quand invalides, proposent de nouvelles méthodes bootstrap valides. Le premier chapitre #co-écrit avec Sílvia Gonçalves# étudie la validité du bootstrap pour l'inférence dans un modèle de panel de données linéaire, dynamique et stationnaire à effets fixes. Nous considérons trois méthodes bootstrap: le recursive-design bootstrap, le fixed-design bootstrap et le pairs bootstrap. Ces méthodes sont des généralisations naturelles au contexte des panels des méthodes bootstrap considérées par Gonçalves et Kilian #2004# dans les modèles autorégressifs en séries temporelles. Nous montrons que l'estimateur MCO obtenu par le recursive-design bootstrap contient un terme intégré qui imite le biais de l'estimateur original. Ceci est en contraste avec le fixed-design bootstrap et le pairs bootstrap dont les distributions sont incorrectement centrées à zéro. Cependant, le recursive-design bootstrap et le pairs bootstrap sont asymptotiquement valides quand ils sont appliqués à l'estimateur corrigé du biais, contrairement au fixed-design bootstrap. Dans les simulations, le recursive-design bootstrap est la méthode qui produit les meilleurs résultats. Le deuxième chapitre étend les résultats du pairs bootstrap aux modèles de panel non linéaires dynamiques avec des effets fixes. Ces modèles sont souvent estimés par l'estimateur du maximum de vraisemblance #EMV# qui souffre également d'un biais. Récemment, Dhaene et Johmans #2014# ont proposé la méthode d'estimation split-jackknife. Bien que ces estimateurs ont des approximations asymptotiques normales centrées sur le vrai paramètre, de sérieuses distorsions demeurent à échantillons finis. Dhaene et Johmans #2014# ont proposé le pairs bootstrap comme alternative dans ce contexte sans aucune justification théorique. Pour combler cette lacune, je montre que cette méthode est asymptotiquement valide lorsqu'elle est utilisée pour estimer la distribution de l'estimateur split-jackknife bien qu'incapable d'estimer la distribution de l'EMV. Des simulations Monte Carlo montrent que les intervalles de confiance bootstrap basés sur l'estimateur split-jackknife aident grandement à réduire les distorsions liées à l'approximation normale en échantillons finis. En outre, j'applique cette méthode bootstrap à un modèle de participation des femmes au marché du travail pour construire des intervalles de confiance valides. Dans le dernier chapitre #co-écrit avec Wenjie Wang#, nous étudions la validité asymptotique des procédures bootstrap pour les modèles à grands nombres de variables instrumentales #VI# dont un grand nombre peu être faible. Nous montrons analytiquement qu'un bootstrap standard basé sur les résidus et le bootstrap restreint et efficace #RE# de Davidson et MacKinnon #2008, 2010, 2014# ne peuvent pas estimer la distribution limite de l'estimateur du maximum de vraisemblance à information limitée #EMVIL#. La raison principale est qu'ils ne parviennent pas à bien imiter le paramètre qui caractérise l'intensité de l'identification dans l'échantillon. Par conséquent, nous proposons une méthode bootstrap modifiée qui estime de facon convergente cette distribution limite. Nos simulations montrent que la méthode bootstrap modifiée réduit considérablement les distorsions des tests asymptotiques de type Wald #$t$# dans les échantillons finis, en particulier lorsque le degré d'endogénéité est élevé.
My dissertation consists of three essays on bootstrap inference in both large panel data models and instrumental variable (IV) models with many instruments and possibly, many weak instruments. Since the asymptotic theory is often not a good approximation to the sampling distribution of test statistics and estimators, I consider the bootstrap as an alternative. These essays try to study the asymptotic validity of existing bootstrap procedures and when they are invalid, to propose new valid bootstrap methods. The first chapter (co-authored with Sílvia Gonçalves) studies the validity of the bootstrap for inference on a stationary linear dynamic panel data model with individual fixed effects. We consider three bootstrap methods: the recursive-design wild bootstrap, the fixed-design wild bootstrap and the pairs bootstrap. These methods are natural generalizations to the panel context of the bootstrap methods considered by \citeasnoun{GK} in pure time series autoregressive models. We show that the recursive-design wild bootstrap fixed effects OLS estimator contains a built-in bias correction term that mimics the incidental parameter bias. This is in contrast with the fixed-design wild bootstrap and the pairs bootstrap whose distributions are incorrectly centered at zero. As it turns out, both the recursive-design and the pairs bootstrap are asymptotically valid when applied to the bias-corrected estimator, but the fixed-design bootstrap is not. In the simulations, the recursive-design bootstrap is the method that does best overall. The second chapter extends our pairwise bootstrap results to dynamic nonlinear panel data models with fixed effects. These models are often estimated with the Maximum Likelihood Estimator (MLE) which also suffers from an incidental parameter bias. Recently, \citeasnoun{DhaeneJochmans} have proposed the split-jackknife estimation method. Although these estimators have asymptotic normal approximations that are centered at the true parameter, important size distortions remain in finite samples. \citeasnoun{DhaeneJochmans} have proposed the pairs bootstrap as an alternative in this context without a theoretical justification. To fill this gap, I show that this method is asymptotically valid when used to estimate the distribution of the half-panel jackknife estimator although it does not consistently estimate the distribution of the MLE. A Monte Carlo experiment shows that bootstrap-based confidence intervals that rely on the half-panel jackknife estimator greatly help to reduce the distortions associated to the normal approximation in finite samples. In addition, I apply this bootstrap method to a canonical model of female-labor participation to construct valid confidence intervals. In the last chapter (co-authored with Wenjie Wang), we study the asymptotic validity of bootstrap procedures for instrumental variable (IV) models with many weak instruments. We show analytically that a standard residual-based bootstrap and the restricted efficient (RE) bootstrap of Davidson and MacKinnon (2008, 2010, 2014) cannot consistently estimate the limiting distribution of the LIML estimator. The foremost reason is that they fail to adequately mimic the identification strength in the sample. Therefore, we propose a modified bootstrap procedure which consistently estimates this limiting distribution. Our simulations show that the modified bootstrap procedure greatly reduces the distortions associated to asymptotic Wald ($t$) tests in finite samples, especially when the degree of endogeneity is high.

Tese de Doutoramento Engenharia Têxtil
O microambiente do vestuário é o espaço entre o vestuário e a pele, estando relacionado com o calor e com o contato do vestuário com a pele. Muitos investigadores pesquisam a relação entre pele e vestuário com vista ao conforto total desse microambiente. O conforto total do vestuário é classificado em 4 grupos básicos, nomeadamente conforto termo-fisiolíogico, conforto sensorial, conforto psicológico e conforto ergonómico. Desse modo, o objetivo dessa tese é avaliar o conforto sensorial e termo-fisiolíogico de amostras têxteis e de bonés desportivos por meio da Análise Descritiva Quantitativa e de ensaios ao uso, estabelecendo uma compreensão sobre as suas características de conforto. Os procedimentos metodológicos foram fundamentados em dois métodos principais: a metodologia objetiva, adaptada de normas de análise sensorial usadas pelas indústrias alimentícias e de cosméticos, para avaliação do conforto sensorial tátil de amostras têxteis e de bonés desportivos, realizada no Brasil; e o método subjetivo, onde o conforto do boné desportivo é avaliado por dois grupos de 10 utentes por meio de ensaios ao uso, um em Portugal, em ambiente de clima controlado, e outro no Brasil, em ambiente externo. Com o método objetivo foi possível: desenvolver o léxico têxtil Brasileiro e compará-lo com os léxicos Francês e Português; selecionar e treinar um painel sensorial tátil têxtil Brasileiro; e fazer a avaliação sensorial tátil em amostras têxteis e de bonés desportivos por meio da Análise Descritiva Quantitativa. Com o método subjetivo, foram avaliadas as sensações do conforto geral, temperatura, humidade e a pressão que as amostras de bonés desportivos exercem na cabeça do utente através de ensaios ao uso. Como resultado da avaliação objetiva, foi possível perceber que o painel classificou de modo semelhante as amostras têxteis e as amostras de bonés. No que respeita à avaliação subjetiva, apesar de não apresentar diferenças significativas entre as médias estimadas e os dados obtidos em ambiente de clima controlado foi possível agrupar os bonés por tipo de modelo e, em alguns atributos, as amostras confecionadas com tecidos 100% algodão apresentaram melhor performance.
The microenvironment of clothing is the space between clothing and skin, being related to heat and the contact of clothing with the skin. Many researchers study the relationship between skin and clothing for the total comfort of this microenvironment. The total comfort of clothing is classified into four basic groups, namely thermophysiological comfort, sensory comfort, psychological comfort and ergonomic comfort. Thus, the objective of this thesis is to evaluate the sensory and thermophysiological comfort of textile samples and sports caps through Quantitative Descriptive Analysis and wear trial, establishing an understanding of their comfort characteristics. The methodological procedures were based on two main methods: the objective methodology, adapted from sensory analysis standards used by the food and cosmetics industries, to evaluate the tactile sensory comfort of textile samples and sports caps, performed in Brazil; and the subjective method, where the comfort of the sports cap is evaluated by two groups of ten wearers through wear trial, one in Portugal, in a climate controlled environment, and another in Brazil, in an outdoor environment. With the objective method it was possible to: develop the Brazilian textile lexicon and compare it with the French and Portuguese lexicons; select and train a Brazilian textile tactile sensory panel; and to make the tactile sensorial evaluation in textile samples and sports caps through the Quantitative Descriptive Analysis. With the subjective method, the sensations of general comfort, temperature, humidity and the pressure that the samples of sports caps exert on the wearer's head through wear tests were evaluated. As a result of the objective assessment, it was possible to notice that the panel similarly classified textile samples and cap samples. Regarding the subjective evaluation, despite not presenting significant differences between the estimated averages and the data obtained in controlled climate environment, it was possible to group the caps by model type and, in some attributes, the samples made with 100% cotton fabrics presented best performance.