Impulse Balance and Multiple‐Period Feedback in the Newsvendor Game

Human subjects in the newsvendor game place suboptimal orders: orders are typically between the expected profit‐maximizing quantity and mean demand (“pull‐to‐center bias”). In previous work, we have shown that impulse balance equilibrium (IBE), which is based on a simple ex post rationality principle along with an equilibrium condition, can predict ordering decisions in the laboratory. In this study, we extend IBE to standing orders and multiple‐period feedback and show that it predicts—in line with previous findings—that constraining newsvendors to make a standing order for a sequence of periods moves the average of submitted orders toward the optimum.     

Benchmarks derived from quantitative endpoints - an approach accounting for litter effects

The use of quantitative variables for risk assessment suffers from the lack of a clear-cut definition of risk. The proposals of Chen and Gaylor (1992) and of Kodell and West (1993) showed a way out of this dilemma. Additional risk is defined as the increase in probability of being in an abnormal state for an exposed individual. In this paper we show how this approach can be generalized to situations where an additional source of variability, often called litter effect, is present. This occurs often in studies on teratogenicity. The coverage of confidence bounds on the additional risk is shown to be sufficient using a small simulation study.     

Risks and opportunities in eastern Europe

Reinhard Hünerberg believes there are exciting and profitable business ventures possible for foreigners in Eastern Europe, and he traces the reasons for these. But factors remain in the situation in this region which counsel strict caution on the part of the foreign investor.     

Exponential GARCH Modeling With Realized Measures of Volatility

We introduce the realized exponential GARCH model that can use multiple realized volatility measures for the modeling of a return series. The model specifies the dynamic properties of both returns and realized measures, and is characterized by a flexible modeling of the dependence between returns and volatility. We apply the model to 27 stocks and an exchange traded fund that tracks the S&P 500 index and find specifications with multiple realized measures that dominate those that rely on a single realized measure. The empirical analysis suggests some convenient simplifications and highlights the advantages of the new specification.     

On Statistical Models for d-Dimensional Stable Processes, and Some Generalizations

In statistical models where jumps of a d -dimensional stable process ( S _t ) _{t ≥0} are observed in windows with certain asymptotic properties, and where parameters appearing in the Levy measure of S are to be estimated, we have asymptotically efficient estimators. If Poisson random measure μ on (0, ∞) × ( R ^d {0}) with intensity dt Λ( dx ) replaces the jump measure of S , where Λ is a ε-finite measure on R ^d {0} admitting tail parameters in a suitable sense, we specify a notion of neighbourhood which allows to treat efficiency in statistical experiments of the second type by switching to accompanying sequences of the stable process type considered first.     

Reduced-rank regression: A useful determinant identity

We formulate an identity for the determinant of a product involving rectangular matrices. The identity can be used to derive the maximum likelihood estimator in reduced-rank regressions with Gaussian innovations. Furthermore, the identity sheds light on the structure of the estimation problem that arises when the reduced-rank parameters are subject to additional constraints.     

Samples of iid Lognormals: Approximations for Characteristics of Minima

We concentrate on characteristics of minima X_N from samples of iid lognormals X_1i ～ 2pLND. We demonstrate that the distribution of X_N for 2 ≤ N ≤ 1,000 may be fitted more accurately by 2pLND than by the limiting Gumbel distribution. An extended power model is established to represent the quotients C_N/C₁, where C_N is the mean, standard deviation, or p-quantile of X_N and C₁ is the corresponding characteristic of X_1i. Our empirical comparisons show that this model provides not only more accurate estimates than alternating approximations but it is also much simpler than its competitors.     

Spatial model fitting for large datasets with applications to climate and microarray problems

Many problems in the environmental and biological sciences involve the analysis of large quantities of data. Further, the data in these problems are often subject to various types of structure and, in particular, spatial dependence. Traditional model fitting often fails due to the size of the datasets since it is difficult to not only specify but also to compute with the full covariance matrix describing the spatial dependence. We propose a very general type of mixed model that has a random spatial component. Recognizing that spatial covariance matrices often exhibit a large number of zero or near-zero entries, covariance tapering is used to force near-zero entries to zero. Then, taking advantage of the sparse nature of such tapered covariance matrices, backfitting is used to estimate the fixed and random model parameters. The novelty of the paper is the combination of the two techniques, tapering and backfitting, to model and analyze spatial datasets several orders of magnitude larger than those datasets typically analyzed with conventional approaches. Results will be demonstrated with two datasets. The first consists of regional climate model output that is based on an experiment with two regional and two driver models arranged in a two-by-two layout. The second is microarray data used to build a profile of differentially expressed genes relating to cerebral vascular malformations, an important cause of hemorrhagic stroke and seizures.