This paper focuses on the estimation of the concentration curve of a finite population, when data are collected according to a complex sampling design with different inclusion probabilities.A (design-based) Hájek type estimator for the Lorenz curve is proposed, and its asymptotic properties are studied. Then, a resampling scheme able to approximate the asymptotic law of the Lorenz curve estimator is constructed. Applications are given to the construction of (i) a confidence band for the Lorenz curve, (ii) confidence intervals for the Gini concentration ratio, and (iii) a test for Lorenz dominance. The merits of the proposed resampling procedure are evaluated through a simulation study.

No abstract

We test the usefulness of machine learning (ML) for the valuation and pricing of sovereign risk in the Euro area along two important dimensions: i) its predictive accuracy compared with traditional econometric methods, and ii) its assessment of the main economic factors underlying market perceptions of sovereign risk.We find that ML techniques can capture the dynamics inherent in the market valuation of country risk far more efficiently than traditional econometric models, both in the cross-section and in the time series. Moreover, we show that public sentiment about financial news, redenomination fears and the degree of hawkishness/dovishness expressed in the ECB president's speeches are major contributors to sovereign bond spreads. We also confirm that macroeconomic and global financial factors affect sovereign risk assessment and the corresponding formation of sovereign spreads.

No abstract

No abstract