Monetary aggregation and the neutrality of money

This article tests the long-run neutrality of money proposition using quarterly U.S. data over the period from 1960:1 to 1996:2 and the methodology suggested by King and Watson (1997), paying particular attention to the integration and cointegration properties of the variables. Comparisons are made among simple sum, Divisia, and currency equivalent (CE) monetary aggregates using the Anderson et al. (1997a, 1997b) series of Divisia and CE monetary aggregates.     

Focused information criterion and model averaging based on weighted composite quantile regression

We study the focused information criterion and frequentist model averaging and their application to post‐model‐selection inference for weighted composite quantile regression (WCQR) in the context of the additive partial linear models. With the non‐parametric functions approximated by polynomial splines, we show that, under certain conditions, the asymptotic distribution of the frequentist model averaging WCQR‐estimator of a focused parameter is a non‐linear mixture of normal distributions. This asymptotic distribution is used to construct confidence intervals that achieve the nominal coverage probability. With properly chosen weights, the focused information criterion based WCQR estimators are not only robust to outliers and non‐normal residuals but also can achieve efficiency close to the maximum likelihood estimator, without assuming the true error distribution. Simulation studies and a real data analysis are used to illustrate the effectiveness of the proposed procedure.     

A Staged Service Innovation Model*

Drawing from the new product development (NPD) literature, service quality literature (SERVQUAL), and empirically grounded research with 53 service innovation decision makers, we develop a staged service innovation model (SIM) for decision makers. We tested the model using empirical data from 329 firms across five industries. The empirical results show that integrating prelaunch service quality training into new service development process leads to successful service innovation. The model developed in this article can be used as a decision support tool and diagnostic model for assessing service innovation ideas, evaluating performance of ongoing service innovations, allocating resources, and improving success rate of service innovations. Decision makers can use the measures developed in this study as a checklist to identify their strengths in delivering service quality to their own customers as well as areas of improvement. This article extends service innovation research by combining NPD and service quality development into a single study and opens the door to further work that could help improve the success rate of service innovations. The model can serve as a base model for future research extensions in service innovation research. A major takeaway for the academic reader is that the SIM demonstrates the value of using the SERVQUAL literature to understand how best to provide excellent quality that results in more fully satisfied customers and, ultimately, improved service performance.     

Collusion via Resale

The English auction is susceptible to tacit collusion when post‐auction interbidder resale is allowed. We show this by constructing equilibria where, with positive probability, one bidder wins the auction without any competition and divides the spoils by optimally reselling the good to the other bidders. These equilibria interim Pareto‐dominate (among bidders) the standard value‐bidding equilibrium without requiring the bidders to make any commitment on bidding behavior or postbidding spoil division.     

Sample size calculation for an agreement study

It is often necessary to compare two measurement methods in medicine and other experimental sciences. This problem covers a broad range of data. Many authors have explored ways of assessing the agreement of two sets of measurements. However, there has been relatively little attention to the problem of determining sample size for designing an agreement study. In this paper, a method using the interval approach for concordance is proposed to calculate sample size in conducting an agreement study. The philosophy behind this is that the concordance is satisfied when no more than the pre‐specified k discordances are found for a reasonable large sample size n since it is much easier to define a discordance pair. The goal here is to find such a reasonable large sample size n. The sample size calculation is based on two rates: the discordance rate and tolerance probability, which in turn can be used to quantify an agreement study. The proposed approach is demonstrated through a real data set. Copyright © 2009 John Wiley & Sons, Ltd.     

A Statistical Approach to Thermal Management of Data Centers Under Steady State and System Perturbations

Temperature control for a large data center is both important and expensive. On the one hand, many of the components produce a great deal of heat, and on the other hand, many of the components require temperatures below a fairly low threshold for reliable operation. A statistical framework is proposed within which the behavior of a large cooling system can be modeled and forecast under both steady state and perturbations. This framework is based upon an extension of multivariate Gaussian autoregressive hidden Markov models (HMMs). The estimated parameters of the fitted model provide useful summaries of the overall behavior of and relationships within the cooling system. Predictions under system perturbations are useful for assessing potential changes and improvements to be made to the system. Many data centers have far more cooling capacity than necessary under sensible circumstances, thus resulting in energy inefficiencies. Using this model, predictions for system behavior after a particular component of the cooling system is shut down or reduced in cooling power can be generated. Steady-state predictions are also useful for facility monitors. System traces outside control boundaries flag a change in behavior to examine. The proposed model is fit to data from a group of air conditioners within an enterprise data center from the IT industry. The fitted model is examined, and a particular unit is found to be underutilized. Predictions generated for the system under the removal of that unit appear very reasonable. Steady-state system behavior also is predicted well.     

Construction of the nearest neighbor embracing graph of a point set

This paper gives optimal algorithms for the construction of the Nearest Neighbor Embracing Graph (NNE-graph) of a given point set V of size n in the k-dimensional space (k-D) for k = 2,3. The NNE-graph provides another way of connecting points in a communication network, which has lower expected degree at each point and shorter total length of connections with respect to those using Delaunay triangulation. In fact, the NNE-graph can also be used as a tool to test whether a point set is randomly generated or has some particular properties. We show that in 2-D the NNE-graph can be constructed in optimal time in the worst case. We also present an time algorithm, where d is the -th largest degree in the utput NNE-graph. The algorithm is optimal when . The algorithm is also sensitive to the structure of the NNE-graph, for instance when , the number of edges in NNE-graph is bounded by for any value g with . We finally propose an time algorithm for the problem in 3-D, where d and are the -th largest vertex degree and the largest vertex degree in the NNE-graph, respectively. The algorithm is optimal when the largest vertex degree of the NNE-graph is .