Testing Covariance Stationarity

In this paper, we show that the widely used stationarity tests such as the Kwiatkowski Phillips, Schmidt, and Shin (KPSS) test have power close to size in the presence of time-varying unconditional variance. We propose a new test as a complement of the existing tests. Monte Carlo experiments show that the proposed test possesses the following characteristics: (i) in the presence of unit root or a structural change in the mean, the proposed test is as powerful as the KPSS and other tests; (ii) in the presence of a changing variance, the traditional tests perform badly whereas the proposed test has high power comparing to the existing tests; (iii) the proposed test has the same size as traditional stationarity tests under the null hypothesis of stationarity. An application to daily observations of return on U.S. Dollar/Euro exchange rate reveals the existence of instability in the unconditional variance when the entire sample is considered, but stability is found in subsamples.     

The impact of AIDS on income and human capital

This paper studies the impact of HIV/AIDS on per capita income and education. It explores two channels on how HIV/AIDS affects income that have not been sufficiently stressed by previous literature: the reduction of the incentives to stay in school due to shorter expected longevity and the reduction in productivity of experienced workers. In the model, individuals live for three periods, may get infected in the second period, and with some probability die of AIDS before reaching the third period of their lives. Parents care for the welfare of the future generations so that they will maximize lifetime utility of their dynasty. The simulations predict that the most affected countries in Sub-Saharan Africa in the future will be, on average, 30% poorer than they would be without AIDS. Schooling will decline in some cases by 40%. These figures are dramatically reduced with widespread medical treatment, as it increases the survival probability and productivity of infected individuals.     

Multi‐pillared social insurance systems: The post‐reform picture in Chile,Uruguay and Brazil

This article focuses on an analysis of social insurance models and reforms in Chile, Uruguay and Brazil. Noting that these three countries are following different reform trajectories, the article explores trends in the restructuring of each of these insurance systems across the course of successive reforms. In the systems, different trends are supporting a closer link between contributions and benefits, according growing importance to private individual accounts and favouring the expansion of the role played by social assistance. These trends all suggest a move towards various forms of multi‐pillared social insurance, but with uncertain results in terms of redistribution and the dynamics of the fundamental objectives of social insurance.     

Optimal Alarm Systems for Count Processes

In many phenomena described by stochastic processes, the implementation of an alarm system becomes fundamental to predict the occurrence of future events. In this work we develop an alarm system to predict whether a count process will upcross a certain level and give an alarm whenever the upcrossing level is predicted. We consider count models with parameters being functions of covariates of interest and varying on time. This article presents classical and Bayesian methodology for producing optimal alarm systems. Both methodologies are illustrated and their performance compared through a simulation study. The work finishes with an empirical application to a set of data concerning the number of sunspot on the surface of the sun.     

Cointegration: Bayesian Significance Test

To estimate causal relationships, time series econometricians must be aware of spurious correlation, a problem first mentioned by Yule (1926 Yule , G. U. ( 1926 ). Why we do sometimes get nonsense-correlations between time-series? A study in sampling and the nature of time-series (with discussion) . J. Roy. Statist. Soc. 89 : 1 – 64 .[Crossref] , [Google Scholar]). To deal with this problem, one can work either with differenced series or multivariate models: VAR (VEC or VECM) models. These models usually include at least one cointegration relation. Although the Bayesian literature on VAR/VEC is quite advanced, Bauwens et al. (1999 Bauwens , L. , Lubrano , M. , Richard , J.-F. ( 1999 ). Bayesian Inference in Dynamic Econometric Models . Oxford : Oxford University Press . [Google Scholar]) highlighted that “the topic of selecting the cointegrating rank has not yet given very useful and convincing results”.

The present article applies the Full Bayesian Significance Test (FBST), especially designed to deal with sharp hypotheses, to cointegration rank selection tests in VECM time series models. It shows the FBST implementation using both simulated and available (in the literature) data sets. As illustration, standard non informative priors are used.     

Asymptotic Location of Largest Values of a Stationary Random Field

The study of the relationship between extreme values of dependent random fields and their locations has important practical applications, for instance, when dealing with censored data.

In this article we study the asymptotic behavior of the joint locations of the largest order statistics generated by a stationary random field with extremal index as well as the joint limiting distribution of the location of a high level exceedance nearest of the origin and the location of the maximum.