How Similar Are Cohabitation and Marriage? Legal Approaches to Cohabitation across Western Europe

Although cohabitation and childbearing within cohabitation have increased in Europe over recent decades, the variation across Europe remains remarkably wide. Most studies on union formation have not explicitly addressed the role of state policies in the development of cohabitation or discussed how countries have responded to changes in union formation by passing legislation. Here we discuss historical and theoretical issues relevant to the relationship between state policies and union formation and describe policies relating to cohabitation and marriage in nine Western European countries. Drawing on secondary sources and legal documents, we examine the quantity of regulations that mention cohabitation and the approach to cohabitation in 19 policy dimensions. We then place the countries along a continuum, from those that have equalized cohabitation and marriage to those that only regulate marriage. As a whole, this overview raises questions about the changing institution of marriage, as well as the increasing institutionalization of cohabitation.     

Personality and Subjective Well-Being: What Hides Behind Global Analyses?

The relation between personality and subjective well-being (SWB) remains involved in a considerable ambiguity and the numerous studies conducted have neglected an approach at a more detailed level of analysis. This study explores the idea that neuroticism, extraversion and conscientiousness facets predict differentially each SWB component. A battery of self-report questionnaires was used to assess personality and SWB in 398 teachers of primary and high schools. Findings of a cross-sectional study showed that neuroticism, extraversion and conscientiousness facets contributed to significantly explain the variance in positive affect, negative affect and life satisfaction. Moreover, these facets predicted differentially each of the three SWB components. At same time, this study corroborates two important premises: the specificity of facets as discrete traits and the independence of the three SWB components.     

Is Over-Education a Problem in Spain? Empirical Evidence Based on the EU-SILC

Spain is suffering an important economic slowdown which affects significantly to unemployment rises. For this reason, many individuals are considering to increase their educational level. However, this decision can lead to get worse labor market indicators. In this paper, we analyze the problem of over-education in Spain and its effect on earnings. Using the last four waves (2009–2012) of the European Union Statistics on Income and Living Conditions (EU-SILC, 2009–2012) this study analyses over-education situation of employed people and compares over-education rates and earnings of these individuals. Cross-sectional analysis (focused on a recent period of time) is performed. We apply different matching techniques based on propensity score methods to evaluate the impact of over-education on personal earnings. The results obtained in this paper add more empirical evidence supporting the effect of education on earnings together with occupational placement because over-education could be a great problem for many countries. In Spain, there exists a problem of over-education which varies among occupations. Overeducated workers are most likely to be found among “Service workers and shop and market sales workers” and “Elementary occupations”. Also, the majority of individuals working as “Professionals” are adequately educated. Besides, over-education has a significant negative impact on earnings. Although the results depend on the way we define over-education (interval or modal method), those individuals over-educated receive less earnings than they correspond to. In fact, the decrease can reach about 5000 euro per year.     

Choice of the ridge factor from the correlation matrix determinant

Ridge regression is the alternative method to ordinary least squares, which is mostly applied when a multiple linear regression model presents a worrying degree of collinearity. A relevant topic in ridge regression is the selection of the ridge parameter, and different proposals have been presented in the scientific literature. Since the ridge estimator is biased, its estimation is normally based on the calculation of the mean square error (MSE) without considering (to the best of our knowledge) whether the proposed value for the ridge parameter really mitigates the collinearity. With this goal and different simulations, this paper proposes to estimate the ridge parameter from the determinant of the matrix of correlation of the data, which verifies that the variance inflation factor (VIF) is lower than the traditionally established threshold. The possible relation between the VIF and the determinant of the matrix of correlation is also analysed. Finally, the contribution is illustrated with three real examples.     

Multilevel maximum likelihood estimation with application to covariance matrices

The asymptotic variance of the maximum likelihood estimate is proved to decrease when the maximization is restricted to a subspace that contains the true parameter value. Maximum likelihood estimation allows a systematic fitting of covariance models to the sample, which is important in data assimilation. The hierarchical maximum likelihood approach is applied to the spectral diagonal covariance model with different parameterizations of eigenvalue decay, and to the sparse inverse covariance model with specified parameter values on different sets of nonzero entries. It is shown computationally that using smaller sets of parameters can decrease the sampling noise in high dimension substantially.     

Portmanteau tests for linearity of stationary time series

This article considers the problem of testing for linearity of stationary time series. Portmanteau tests are discussed which are based on generalized correlations of residuals from a linear model (that is, autocorrelations and cross-correlations of different powers of the residuals). The finite-sample properties of the tests are assessed by means of Monte Carlo experiments. The tests are applied to 100 time series of stock returns.     

Tests for time series of counts based on the probability-generating function

We propose testing procedures for the hypothesis that a given set of discrete observations may be formulated as a particular time series of counts with a specific conditional law. The new test statistics incorporate the empirical probability-generating function computed from the observations. Special emphasis is given to the popular models of integer autoregression and Poisson autoregression. The asymptotic properties of the proposed test statistics are studied under the null hypothesis as well as under alternatives. A Monte Carlo power study on bootstrap versions of the new methods is included as well as real-data examples.     

Uniform Convergence of Compactly Supported Wavelet Expansions of Gaussian Random Processes

New results on uniform convergence in probability for expansions of Gaussian random processes using compactly supported wavelets are given. The main result is valid for general classes of non stationary processes. An application of the obtained results to stationary processes is also presented. It is shown that the convergence rate of the expansions is exponential.