Spatially correlated Poisson sampling

A new method for sampling from a finite population that is spread in one, two or more dimensions is presented. Weights are used to create strong negative correlations between the inclusion indicators of nearby units. The method can be used to produce unequal probability samples that are well spread over the population in every dimension, without any spatial stratification. Since the method is very general there are numerous possible applications, especially in sampling of natural resources where spatially balanced sampling has proven to be efficient. Two examples show that the method gives better estimates than other commonly used designs.     

Medical care and regional mortality differences within the countries of the European community

This paper addresses the question whether regional mortality differences within developed countries reflect differences in characteristics of medical care service. It adds two new elements to previous studies on this subject: it concentrates on selected ‘avoidable’ causes of death, and it makes parallel analyses for ten EC countries. The results show that levels of medical care supply contribute little to the explanation of regional differences in mortality from the selected conditions. It is concluded that if regional mortality differences are related to health care, factors other than the level of supply are probably involved.     

Wild bootstrap seasonal unit root tests for time series with periodic nonstationary volatility

We investigate the behavior of the well-known Hylleberg, Engle, Granger and Yoo (HEGY) regression-based seasonal unit root tests in cases where the driving shocks can display periodic nonstationary volatility and conditional heteroskedasticity. Our set up allows for periodic heteroskedasticity, nonstationary volatility and (seasonal) generalized autoregressive-conditional heteroskedasticity as special cases. We show that the limiting null distributions of the HEGY tests depend, in general, on nuisance parameters which derive from the underlying volatility process. Monte Carlo simulations show that the standard HEGY tests can be substantially oversized in the presence of such effects. As a consequence, we propose wild bootstrap implementations of the HEGY tests. Two possible wild bootstrap resampling schemes are discussed, both of which are shown to deliver asymptotically pivotal inference under our general conditions on the shocks. Simulation evidence is presented which suggests that our proposed bootstrap tests perform well in practice, largely correcting the size problems seen with the standard HEGY tests even under extreme patterns of heteroskedasticity, yet not losing finite sample relative to the standard HEGY tests.     

UNINSURED COUNTERCYCLICAL RISK: AN AGGREGATION RESULT AND APPLICATION TO OPTIMAL MONETARY POLICY

We consider an incomplete markets economy with capital accumulation and endogenous labor supply. Individuals face countercyclical idiosyncratic labor and asset risk. We derive conditions under which the aggregate allocations and price system can be found by solving a representative agent problem. This result is applied to analyze the properties of an optimal monetary policy in a New Keynesian economy with uninsured countercyclical individual risk. The optimal monetary policy that emerges from our incomplete markets economy is the same as the optimal monetary policy in a representative agent model with preference shocks. When price rigidity is the only friction the optimal monetary policy calls for stabilizing the inflation rate at zero.     

Asymptotic properties of the GMLE in the case 1 interval-censorship model with discrete inspection times

We consider the case 1 interval censorship model in which the survival time has an arbitrary distribution function F₀ and the inspection time has a discrete distribution function G. In such a model one is only able to observe the inspection time and whether the value of the survival time lies before or after the inspection time. We prove the strong consistency of the generalized maximum-likelihood estimate (GMLE) of the distribution function F₀ at the support points of G and its asymptotic normality and efficiency at what we call regular points. We also present a consistent estimate of the asymptotic variance at these points. The first result implies uniform strong consistency on [0, ∞) if F₀ is continuous and the support of G is dense in [0, ∞). For arbitrary F₀ and G, Peto (1973) and Tumbull (1976) conjectured that the convergence for the GMLE is at the usual parametric rate n½ Our asymptotic normality result supports their conjecture under our assumptions. But their conjecture was disproved by Groeneboom and Wellner (1992), who obtained the nonparametric rate ni under smoothness assumptions on the F₀ and G.     

Practice and Outcomes of Multidisciplinary Research for Environmental Sustainability

Since about 1990, when sustainability became a key concept for a wide range of scientific disciplines, the need for multidisciplinary collaboration has increased. We present five illustrative cases from the long-standing environmental research work at the University of Groningen. The projects described are about hazardous materials risk, odor annoyance, energy scenario evaluation, climate decision analysis, and household consumption, respectively. The various case discussions emphasize experiences in research conceptualization, project design and execution, main findings, policy advice and surplus value, and difficulties met. Conclusions and recommendations are presented about the practice of multidisciplinary research. Finally, some challenges for research and development about environmental sustainability are discussed.