Modified first-difference estimator in a panel data model with unobservable factors both in the errors and the regressors when the time dimension is small

Panel data models with factor structures in both the errors and the regressors have received considerable attention recently. In these models, the errors and the regressors are correlated and the standard estimators are inconsistent. This paper shows that, for such models, a modified first-difference estimator (in which the time and the cross-sectional dimensions are interchanged) is consistent as the cross-sectional dimension grows but the time dimension is small. Although the estimator has a non standard asymptotic distribution, t and F tests have standard asymptotic distribution under the null hypothesis.     

Valuing and Integrating Informal Care as a Core Component of Long-Term Care for Older People: A Comparison of Recent Developments in Italy and Spain

The international long-term care (LTC) debate has recently been focusing on how to strengthen home care provision. In this regard, a major role has been played by informal care and how to best integrate it in a holistic care approach. Italy and Spain, usually labeled as “familialist” or “family-based” care models, have been promoting national reforms or actions to support the integration of “informal” actors into the overall LTC system. Through a comparative review of recent trends observed in the two care regimes, this article aims at contributing to improve our cross-national understanding of how LTC is changing across Europe, identifying the basic approaches adopted in Italy and Spain and highlighting both their strengths and drawbacks.     

What Colleges Are Doing About Student Binge Drinking A Survey of College Administrators

Abstract

In 1999, the Harvard School of Public Health College Alcohol Study surveyed 734 US college administrators to learn what colleges were doing to prevent binge drinking. Respondents rated the severity of student alcohol-abuse problems and described prevention efforts and institutional investments in prevention infrastructure. Prevention practices were widespread in the areas of general education about alcohol, use of policy controls to limit access to alcohol, restricting advertising at home-game sporting events, and allocation of living space to alcohol-free dormitories. Programming was less prevalent for more targeted alcohol education, outreach, and restrictions on alcohol advertising in campus media. Nationally, most of the surveyed colleges reported having a campus alcohol specialist, many had task forces, and about half were performing in-house data collection. Less common were program evaluations, community agreements, or neighborhood exchanges. Prevention practices varied with institutional characteristics and the surveyed administrators' perceptions of the severity of alcohol problems.     

A note about calibrated prediction regions and distributions

In this work we address the problem of the construction of prediction regions and distribution functions, with particular regard to the multidimensional setting. Firstly, we define a simple procedure for calculating the predictive distribution function which gives improved prediction limits. Secondly, with a multivariate generalization of a result presented in Ueki and Fueda (2007), we propose a method for correcting estimative prediction regions, to reduce their coverage error to the third-order accuracy. The improved prediction regions and the associated distribution functions are easy to calculate using a suitable bootstrap procedure. Examples of application are included, showing the good performance of the proposed method, even if we consider an approximated model for prediction purposes.     

Gender wage differentials in Italy: a structural estimation approach

This paper studies gender wage differentials by providing a maximum likelihood structural estimation of the frictional parameters of an equilibrium search model with on-the-job search and firm heterogeneity. In a second step, I also consider the role of discrimination. Results indicate higher level of search frictions for women; this result is confirmed by various robustness checks and by different specification and estimation strategies. I also find that the resulting mapping from productivity to wages for men is highly non-linear, while for women it is almost linear. Search, productivity and discrimination play different roles in shaping the gender differential depending on the specification and estimation of the model.     

Pension insurance and the participation of private enterprise

One of the principal developments of our time in the field of social security policy has been the growing importance of the desire of individuals for greater economic security in their old age. This desire has in its turn given rise to an increasing tendency to seek benefits complementing those offered or promised by public social insurance schemes. In Europe and North America private enterprise has for many years played an active role in the field of complementary provident arrangements; but in the countries of Latin America interest in schemes of this kind has only begun to develop in recent years. A study of the present situation shows a need to establish concepts permitting an objective approach to the problem of whether — and ultimately, how — a modern State can succeed in striking a satisfactory balance between public intervention and private enterprise in the field of provision against deferred risks or, if it cannot do so, what the prospects are for the securing of a better future by present-day society and how that goal could be achieved. A reform of provident systems in Latin America is inevitable. The difficulty lies in determining how to reorganize public systems so as to ensure their continuance and to entrust them with the principal role in ensuring social solidarity while at the same time opening up opportunities for free private complementary insurance.     

The Finite Sample Performance of Estimators for Mediation Analysis Under Sequential Conditional Independence

Using a comprehensive simulation study based on empirical data, this article investigates the finite sample properties of different classes of parametric and semiparametric estimators of (natural) direct and indirect causal effects used in mediation analysis under sequential conditional independence assumptions. The estimators are based on regression, inverse probability weighting, and combinations thereof. Our simulation design uses a large population of Swiss jobseekers and considers variations of several features of the data-generating process (DGP) and the implementation of the estimators that are of practical relevance. We find that no estimator performs uniformly best (in terms of root mean squared error) in all simulations. Overall, so-called “g-computation” dominates. However, differences between estimators are often (but not always) minor in the various setups and the relative performance of the methods often (but not always) varies with the features of the DGP.     

Flexible clustering via hidden hierarchical Dirichlet priors

The Bayesian approach to inference stands out for naturally allowing borrowing information across heterogeneous populations, with different samples possibly sharing the same distribution. A popular Bayesian nonparametric model for clustering probability distributions is the nested Dirichlet process, which however has the drawback of grouping distributions in a single cluster when ties are observed across samples. With the goal of achieving a flexible and effective clustering method for both samples and observations, we investigate a nonparametric prior that arises as the composition of two different discrete random structures and derive a closed-form expression for the induced distribution of the random partition, the fundamental tool regulating the clustering behavior of the model. On the one hand, this allows to gain a deeper insight into the theoretical properties of the model and, on the other hand, it yields an MCMC algorithm for evaluating Bayesian inferences of interest. Moreover, we single out limitations of this algorithm when working with more than two populations and, consequently, devise an alternative more efficient sampling scheme, which as a by-product, allows testing homogeneity between different populations. Finally, we perform a comparison with the nested Dirichlet process and provide illustrative examples of both synthetic and real data.     

Conjugate analysis of multivariate normal data with incomplete observations

The authors discuss prior distributions that are conjugate to the multivariate normal likelihood when some of the observations are incomplete. They present a general class of priors for incorporating information about unidentified parameters in the covariance matrix. They analyze the special case of monotone patterns of missing data, providing an explicit recursive form for the posterior distribution resulting from a conjugate prior distribution. They develop an importance sampling and a Gibbs sampling approach to sample from a general posterior distribution and compare the two methods.