We develop a simple, non-parametric approach for estimating the entire distribution of skill. Our approach avoids the challenge of correctly specifying the distribution, and allows for a joint analysis of multiple measures–a key requirement for examining skill. Our results show that more than 85% of the funds are skilled at detecting profitable trades, but unskilled at overriding capacity constraints. Aggregating both skill dimensions using the value added, we find that around 70% of the funds are able to generate profits. The average value added after funds have reached their long-term size equals 7.1 mio. per year, which represents two thirds of the optimal value predicted by neoclassical theory. For all skill measures, the distribution is highly non-normal and reveals a strong heterogeneity across funds.
Dynamic stochastic general equilibrium models with ex-post heterogeneity due to idiosyncratic risk pose numerous challenges stemming from the cross-sectional distribution of endogenous variables which changes stochastically over time due to aggregate risk. In this thesis, I tackle various open questions. My first contribution is of a theoretical nature as I establish existence and uniqueness of the Aiyagari-Bewley growth model. The second challenge I address has a more practical concern. I propose a new numerical method to compute solutions to heterogeneous agent models. With the derived approximation error bounds, I ensure convergence to the rational expectations equilibrium. Equipped with this novel theoretically founded method, I show that even two standard economic models like the Aiyagari-Bewley growth model and the Huggett economy yield intriguing results. When agents progressively share idiosyncratic risk, heterogeneity increasingly amplifies aggregate risk. Furthermore, the volatility of the expected stationary cross-sectional distribution and of the stationary price distribution rises.
The comprehensive and systematic study of collective action organizations (AOs) requires a new methodological approach that takes into account the rise of online sources as well as the new ways in which people interact and participate in politics. This article aims to present and situate in the related literature such an approach, which was recently created and applied in two European Commission funded research projects, LIVEWHAT and TransSOL, across nine and eight countries respectively. Moving beyond recent studies using online sources, our research used a hubs website based approach to study alternative action organizations (AAOs) in the LIVEWHAT project and transnational solidarity AOs in the TransSOL project. The hubs and subhubs websites that aggregate data on AOs in multiple regions were scraped to identify national samples that offer advanced coverage of the repertoire of AO activities, as defined by the teams. These nodal websites were used as sources, similar to the way in which newspapers are treated in protest event analysis. The article situates and compares the new action organization analysis approach against its foundational protest event, protest case, and political claims analyses, as well as other approaches offering data on online activism. It outlines its main features and the related data construction process, while showcasing its application in the two European Commission cross-national projects. Finally, its merits and limitations are discussed, including a reference to how it can be used as a foundation for a mixed methods approach.
This article investigates the involvement of alternative action organizations in three forms of political advocacy in an attempt to gauge their degree of politicization. These forms can be understood as representing three different ways of making political claims: by raising public awareness with respect to a given cause or issue, by trying to influence the policy maker through “insider” lobbying activities, and by protesting in the streets as “outsiders.” Our findings show strong cross-national variations in all three forms of political activities, although not always following a consistent pattern. They also suggest that there is a relationship between the severity of the economic crisis and the form of advocacy. Most important, our analysis suggests that the politicization of alternative action organizations depends both on certain internal characteristics such as their degree of formalization and professionalization, as well as their thematic focus, and the scope of their activities, and on the broader context in terms of economic crisis, austerity policies, and political opportunities. As regards the latter, we find an impact especially on lobbying and protesting.
In this Master Thesis, we have analytically derived and numerically implemented three estimators of the Prediction Divergence Criterion (Avella-Medina et al., working paper) for Model Selection within the logistic regression framework. After the validation of these estimators by means of simulations, we have performed Model Selection both when the order of the variables was known in advance and when the order was correct but decided by an already existing algorithm, namely the binary lasso (Friedman et al., 2010). Finally we have produced evidences of the good performance of two of these estimators, one derived from the L2 norm error measure and the other from the binomial deviance, respectively in highly and moderately correlated settings. They have been proven better, to the extension of the simulation study, than the defaults methods, based on 10-fold Cross Validation, currently available in the glmnet(Friedman et al., 2017) R package.
The thesis examines political discourse quality online and proposes a methodology for analyzing online conversations in an automated way. The study builds on Habermas' work by examining the quality of the public sphere in a digital age. Primarily, it examines the portion of the public sphere which deals with political discussions on online platforms. The proposed technique, DelibAnalysis, is a combination of random forests classification and k-means clustering using term-frequency inverse-document-frequency. The DelibAnalysis methodology is applied to a diverse dataset of online conversations between citizens and elected representatives in Canada, the United States and the United Kingdom using Facebook and blog platforms. This analysis is used to derive insights about the state of the online public sphere and the differences between platforms and discussion frameworks. The objective of this research is to provide a systematic framework for the semi-automated discourse quality analysis of large datasets, and in applying this framework, to yield insight into the structure and features of political discussions online.
In this thesis I investigate the validity and the accuracy properties of the posterior quantiles in Bayesian statistics when replacing the parametric likelihood with the Cressie-Read empirical likelihoods based on a set of unbiased M-estimating equations. At first order I study the validity of the empirical posterior distribution derived from the pseudo-likelihood constructed with profiled weights and estimated at a minimum distance from the empirical distribution in the Cressie-Read family of divergences, indexed by γ. The bias in coverage of the resulting empirical posterior quantile is inversely proportional to the asymptotic efficiency of the estimator corresponding to the set of M-estimating functions. By comparing different members of the Cressie-Read family of empirical likelihoods for models in the exponential family, I establish a hierarchy in the accuracy of the quantile function of the empirical posterior distribution depending on the index parameter γ.
