Using repeated-prevalence data in multi-state modeling of renal replacement therapy

Multi-state models help predict future numbers of patients requiring specific treatments but these models require exhaustive incidence data. Deriving reliable predictions from repeated-prevalence data would be helpful. A new method to model the number of patients that switch between therapeutic modalities using repeated-prevalence data is presented and illustrated. The parameters and goodness of fit obtained with the new method and repeated-prevalence data were compared to those obtained with the classical method and incidence data. The multi-state model parameters’ confidence intervals obtained with annually collected repeated-prevalence data were wider than those obtained with incidence data and six out of nine pairs of confidence intervals did not overlap. However, most parameters were of the same order of magnitude and the predicted patient distributions among various renal replacement therapies were similar regardless of the type of data used. In the absence of incidence data, a multi-state model can still be successfully built with annually collected repeated-prevalence data to predict the numbers of patients requiring specific treatments. This modeling technique can be extended to other chronic diseases.     

More Efficient Tests Robust to Heteroskedasticity of Unknown Form

In the presence of heteroskedasticity of unknown form, the Ordinary Least Squares parameter estimator becomes inefficient, and its covariance matrix estimator inconsistent. Eicker (1963) and White (1980) were the first to propose a robust consistent covariance matrix estimator, that permits asymptotically correct inference. This estimator is widely used in practice. Cragg (1983) proposed a more efficient estimator, but concluded that tests basd on it are unreliable. Thus, this last estimator has not been used in practice. This article is concerned with finite sample properties of tests robust to heteroskedasticity of unknown form. Our results suggest that reliable and more efficient tests can be obtained with the Cragg estimators in small samples.     

Financing retirement pensions: Pay-as-you-go and funded systems in the European Union

The techniques used to finance retirement pensions in the European Union are extremely varied, as are schemes'institutional frameworks. The standard distinction between pay-as-you-go and funded systems obscures the fact that there are four main types of schemes, each with very distinct methods of regulation and conditions of long-term viability. An analysis of the current procedures reveals the extent to which the schemes'ways of operating differ, and points to considerable variations in the way the social actors participate in the process of regulation. The adaptation of pension systems to new realities is dependent on factors which go far beyond a simple choice between two basic methods of financing.     

A Bayesian approach to statistical inference in stochastic DEA

Stochastic DEA can deal effectively with noise in the non-parametric measurement of efficiency but unfortunately formal statistical inference on efficiency measures in not possible. In this paper, we provide a Bayesian approach to the problem organized around simulation techniques that allow for finite-sample inferences on efficiency scores. The new methods are applied to efficiency analysis of the Greek banking system for the period 1993–1999. The results show that the majority of the Greek banks operate close to best market practices.     

Pensions in the European Union: Adapting to Economic and Social Changes

Throughout the European Union, pension schemes are undergoing a process of adaptation to a completely different context. Some of the main stumbling blocks to this process are the great number of issues involved and the diversity of questions that these raise: demographic changes, the labour market, economic growth, social justice, the ways of regulating the established provisions and decision-making in their regard. These dimensions do not offer ready-made answers, from the standpoint of pension financing. Apart from the complexity of the situation, however, a fundamental component of European societies is definitely at stake here: the ability to guarantee a decent level of security to all citizens when they reach retirement age.     

A Theory of Macroprudential Policies in the Presence of Nominal Rigidities

We propose a theory of monetary policy and macroprudential interventions in financial markets. We focus on economies with nominal rigidities in goods and labor markets and subject to constraints on monetary policy, such as the zero lower bound or fixed exchange rates. We identify an aggregate demand externality that can be corrected by macroprudential interventions in financial markets. Ex post, the distribution of wealth across agents affects aggregate demand and output. Ex ante, however, these effects are not internalized in private financial decisions. We provide a simple formula for the required financial interventions that depends on a small number of measurable sufficient statistics. We also characterize optimal monetary policy. We extend our framework to incorporate pecuniary externalities, providing a unified approach to both externalities. Finally, we provide a number of applications which illustrate the relevance of our theory.     

Nonparametric Identification of Risk Aversion in First‐Price Auctions Under Exclusion Restrictions

This paper studies the nonparametric identification of the first‐price auction model with risk averse bidders within the private value paradigm. First, we show that the benchmark model is nonindentified from observed bids. We also derive the restrictions imposed by the model on observables and show that these restrictions are weak. Second, we establish the nonparametric identification of the bidders' utility function under exclusion restrictions. Our primary exclusion restriction takes the form of an exogenous bidders' participation, leading to a latent distribution of private values that is independent of the number of bidders. The key idea is to exploit the property that the bid distribution varies with the number of bidders while the private value distribution does not. We then extend these results to endogenous bidders' participation when the exclusion restriction takes the form of instruments that do not affect the bidders' private value distribution. Though derived for a benchmark model, our results extend to more general cases such as a binding reserve price, affiliated private values, and asymmetric bidders. Last, possible estimation methods are proposed.     

Care-giving Coping Strategies Among Mothers with Chronically Ill Children

ABSTRACT

The purpose of the study is to identify the types of stresses experienced and the coping strategies used by mothers who are responsible for providing care to their chronically ill and disabled children. A qualitative approach was selected to systematically document the experiences and highlight individual coping strategies. Purposive sampling was used in this qualitative pilot study, fifteen (15) mothers who were known to the researcher as well as two schools that specifically cater for children with disabilities were selected to participate in the study. Thirty questionnaires were handed out of which fifteen were returned. Data was collected using the coping Health Inventory for Parents (CHIP) to measure the extent to which parents perceived a number of coping strategies to be helpful in coping with the chronic illness of their child (ren). The scale comprised 45 items with 3 subscales. The results highlighted the types of stresses that were experienced by the mothers and the coping strategies practiced by these mothers when caring for their disabled children. Mothers who care for chronically ill children are faced with daily stress that relates to care as well as their personal life.     

Analysis of adaptive directional stratification for the controlled estimation of rare event probabilities

In the context of structural reliability, a small probability to be assessed, a high computational time model and a relatively large input dimension are typical constraints which brought together lead to an interesting challenge. Indeed, in this framework many existing stochastic methods fail in estimating the failure probability with robustness.