Do men and women receive different pay-offs, in terms of wage, from cognitive skills in the Swedish labor market? To answer this, the classical Mincer equation is expanded with a variable for cognitive skills (literacy and numeracy) and an interaction term between being a male and cognitive skills to be able to distinguish the actual difference in pay-off. I use data from OECD’s PIAAC survey of adult skills, which provides a unique opportunity to examine gender pay-off differences concerning cognitive skills. The results show that men have a larger pay-off than women once occupation is sufficiently controlled for

abstract: Gerrymandering is a central problem for many representative democracies. Formally, gerrymandering is the manipulation of spatial boundaries to provide political advantage to a particular group (Warf, 2006). The term often refers to political district design, where the boundaries of political districts are “unnaturally” manipulated by redistricting officials to generate durable advantages for one group or party. Since free and fair elections are possibly the critical part of representative democracy, it is important for this cresting tide to have scientifically validated tools. This dissertation supports a current wave of reform by developing a general inferential technique to “localize” inferential bias measures, generating a new type of district-level score. The new method relies on the statistical intuition behind jackknife methods to construct relative local indicators. I find that existing statewide indicators of partisan bias can be localized using this technique, providing an estimate of how strongly a district impacts statewide partisan bias over an entire decade. When compared to measures of shape compactness (a common gerrymandering detection statistic), I find that weirdly-shaped districts have no consistent relationship with impact in many states during the 2000 and 2010 redistricting plan. To ensure that this work is valid, I examine existing seats-votes modeling strategies and develop a novel method for constructing seats-votes curves. I find that, while the empirical structure of electoral swing shows significant spatial dependence (even in the face of spatial heterogeneity), existing seats-votes specifications are more robust than anticipated to spatial dependence. Centrally, this dissertation contributes to the much larger social aim to resist electoral manipulation: that individuals & organizations suffer no undue burden on political access from partisan gerrymandering. / Dissertation/Thesis / Doctoral Dissertation Geography 2017

Frequentist model averaging has started to grow in popularity, and it is considered a good alternative to model selection. It has recently been applied favourably to gen- eralized linear models, where it has mainly been purposed to aid the prediction of probabilities. The performance of averaging estimators has largely been compared to that of models selected using AIC or BIC, without much discussion of model screening. In this paper, we study the performance of model averaging in classification problems, and evaluate performances with reference to a single prediction model tuned using cross-validation. We discuss the concept of model screening and suggest two methods of constructing a candidate model set; averaging over the models that make up the LASSO regularization path, and the so called LASSO-GLM hybrid. By means of a Monte Carlo simulation study, we conclude that model averaging does not necessarily offer any improvement in classification rates. In terms of risk, however, we see that both methods of model screening are efficient, and their errors are more stable than those achieved by the cross-validated model of comparison.

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No description available.

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.

L’imputation simple est très souvent utilisée dans les enquêtes pour compenser pour la non-réponse partielle. Dans certaines situations, la variable nécessitant l’imputation prend des valeurs nulles un très grand nombre de fois. Ceci est très fréquent dans les enquêtes entreprises qui collectent les variables économiques. Dans ce mémoire, nous étudions les propriétés de deux méthodes d’imputation souvent utilisées en pratique et nous montrons qu’elles produisent des estimateurs imputés biaisés en général. Motivé par un modèle de mélange, nous proposons trois méthodes d’imputation et étudions leurs propriétés en termes de biais. Pour ces méthodes d’imputation, nous considérons un estimateur jackknife de la variance convergent vers la vraie variance, sous l’hypothèse que la fraction de sondage est négligeable. Finalement, nous effectuons une étude par simulation pour étudier la performance des estimateurs ponctuels et de variance en termes de biais et d’erreur quadratique moyenne. / Single imputation is often used in surveys to compensate for item nonresponse. In some cases, the variable requiring imputation contains a large amount of zeroes. This is especially frequent in business surveys that collect economic variables. In this thesis, we study the properties of two imputation procedures frequently used in practice and show that they lead to biased estimators, in general. Motivated by a mixture regression model, we then propose three imputation procedures and study their properties in terms of bias. For the proposed imputation procedures, we consider a jackknife variance estimator that is consistent for the true variance, provided the overall sampling fraction is negligible. Finally, we perform a simulation study to evaluate the performance of point and variance estimators in terms of relative bias and mean square error.

L’imputation simple est très souvent utilisée dans les enquêtes pour compenser pour la non-réponse partielle. Dans certaines situations, la variable nécessitant l’imputation prend des valeurs nulles un très grand nombre de fois. Ceci est très fréquent dans les enquêtes entreprises qui collectent les variables économiques. Dans ce mémoire, nous étudions les propriétés de deux méthodes d’imputation souvent utilisées en pratique et nous montrons qu’elles produisent des estimateurs imputés biaisés en général. Motivé par un modèle de mélange, nous proposons trois méthodes d’imputation et étudions leurs propriétés en termes de biais. Pour ces méthodes d’imputation, nous considérons un estimateur jackknife de la variance convergent vers la vraie variance, sous l’hypothèse que la fraction de sondage est négligeable. Finalement, nous effectuons une étude par simulation pour étudier la performance des estimateurs ponctuels et de variance en termes de biais et d’erreur quadratique moyenne. / Single imputation is often used in surveys to compensate for item nonresponse. In some cases, the variable requiring imputation contains a large amount of zeroes. This is especially frequent in business surveys that collect economic variables. In this thesis, we study the properties of two imputation procedures frequently used in practice and show that they lead to biased estimators, in general. Motivated by a mixture regression model, we then propose three imputation procedures and study their properties in terms of bias. For the proposed imputation procedures, we consider a jackknife variance estimator that is consistent for the true variance, provided the overall sampling fraction is negligible. Finally, we perform a simulation study to evaluate the performance of point and variance estimators in terms of relative bias and mean square error.

在生態學、生物學、和比較文學的研究中，物種個數通常是評估生物多樣性的重要指標，單一群落物種數的估計已有非常豐富的相關研究。較為知名者包括Good (1953)提出未出現物種的機率，作為估計物種數的參考，往後Good的想法被大量延伸，推演出不少新的估計方法，像是Burnham and Overton (1978)的Jackknife估計法，Chao and Lee (1992)利用涵蓋機率的估計。相對而言，兩群落共有物種數的研究較少，現有研究中較為知名的有Chao et al. (2000)的估計式。 本研究延伸Good想法，探討Jackknife估計法在兩群落的應用，以出現一次的共有物種（一階Jackknife估計），推估未出現共有物種機率，並且仿造Burnham and Overton的想法，建立共有物種數的估計值及變異數。本文除了以電腦模擬，也使用實例（包括：金庸武俠小說、台灣野生水鳥、巴拿馬螃蟹和巴洛科羅拉多森林）檢驗本文的Jackknife估計法，利用涵蓋機率角度發現抽出某特定比例樣本時，估計值涵蓋母體共有物種數之機率值達到九成以上，且也與Chao提出的估計值比較。 / The number of species is frequently used to measure the biodiversity of a population in ecology, biology, and comparative literature. There are quite a lot of studies related to estimating the number of species. Among these studies, Good (1953) proposed a famous estimate (Turing’s estimate) for the probability of unseen species. Subsequently, many methods have been proposed for estimating the number of species based on Good’s idea. For example, the Jackknife estimator by Burnham and Overton (1978) and sample coverage probability by Chao and Lee (1992) are two famous estimates for the number of species. In contrast, there are not many studies for the number of shared species in two communities, and Chao et al. (2000) is probably the only one. This article extends Good’s idea and the Jackknife method to estimate the number of shared species in two communities. Similar to Burnham and Overton, we establish the estimate and its estimated variance, based on the number of species appearing exactly once. We also use computer simulation and real data sets (Jin-Yong martial arts novels, Taiwan wild birds, Panama crustacean, and Barro Colorado Island forest) to evaluate the proposed method. We found that the coverage probability for confidence interval covering the true number of shared species is more than 90%. In addition, we compare the proposed method with Chao’s method.

<p>The intention is to draw more specific connections between certain deconvolution methods and also to demonstrate the application of the statistical theory of estimation in the presence of measurement error. A parametric methodology for deconvolution when the underlying distribution is of the Pareto form is developed. Maximum likelihood estimation (MLE) of the parameters of the convolved distributions is considered. Standard errors of the estimated parameters are calculated from the inverse Fisher&rsquo / s information matrix and a jackknife method. Probability-probability (P-P) plots and Kolmogorov-Smirnov (K-S) goodnessof- fit tests are used to evaluate the fit of the posited distribution. A bootstrapping method is used to calculate the critical values of the K-S test statistic, which are not available.</p>