Inter-market competition for exchange traded funds

We examine how the different mix of informed and liquidity trading in the market for ETFs affects the nature of inter-market competition. We find that both the characteristics of the securities and the structures of the competing markets jointly determine the nature of inter-market competition. Given the superior execution quality on the ECNs and the low adverse selection costs in the ETF market, anonymous market such as the ECNs, attract both liquidity and informed traders. We also find that markets compete in a subset of ETFs. In addition, we find that quotebased competition is prevalent in the market for ETFs.     

On minimum distance estimators for unimodal densities

It is shown that minimum distance estimators for families of unimodal densities are always consistent; the rate of convergence is indicated. An algorithm is proposed for computing the minimum distance estimator for the family of all unimodal densities. References are given to the maximum likelihood method and the kernel method.     

Preventing HIV in injection drug users: exploring the tradeoffs between interventions

Injection drug users (IDUs) transmit the human immunodeficiency virus (HIV) via both needle sharing and sex. Available interventions for this population have varying costs and effectiveness and focus on different risk behaviors. In this analysis, we look at two interventions. One is inexpensive, broad-based and provides modest risk reductions (street outreach (SO)); the other is narrowly focused, expensive and relatively effective (methadone maintenance). This analysis explores the effects of population risk behavior, intervention effectiveness, intervention costs, and decision constraints when allocating funds between these two interventions to maximize effectiveness. We develop a model of the spread of HIV, dividing IDUs into susceptibles (uninfected) and infectives, and separately portraying sex and injection risk. We simulate the epidemic in New York City for time periods from the mid-1980s to the early 1990s, and incorporate the behavioral effects of two interventions performed singly or in combination to find the allocation that maximizes the number of infections averted in the IDUs and their noninjecting sex partners, assuming interventions have increasing marginal costs. We find that the optimal allocation nearly always involves spending the maximum allowable amount on SO. This result is largely insensitive to variations in risk parameters, intervention efficacy, or cost. The model's structure, however, makes clear that many factors contribute to this insensitivity, namely the scope of the interventions, the dual drug/sex nature of HIV risk in the population, the asymmetry of sexual risk for men and women, and the potential benefits to nonIDUs.     

The Impact of Transaction Costs and the Expected Length of Stay on Homeownership

Economic theory suggests that an increase in the expected length of stay in a dwelling increases the probability of a household choosing to own a house rather than rent. This hypothesis is derived from recognition that there are substantial transaction costs associated with homeownership and increased expected time in the home reduces the annualized transaction cost. Using a military data set, we confirm that expected length of stay in a dwelling and the transaction costs of selling are very important to the ownership decision. Our best estimate of the transaction costs of selling a home are the sum of 3% of house value and 4% of household earnings.     

Forecasting economic time series using flexible versus fixed specification and linear versus nonlinear econometric models

Nine macroeconomic variables are forecast in a real-time scenario using a variety of flexible specification, fixed specification, linear, and nonlinear econometric models. All models are allowed to evolve through time, and our analysis focuses on model selection and performance. In the context of real-time forecasts, flexible specification models (including linear autoregressive models with exogenous variables and nonlinear artificial neural networks) appear to offer a useful and viable alternative to less flexible fixed specification linear models for a subset of the economic variables which we examine, particularly at forecast horizons greater than 1-step ahead. We speculate that one reason for this result is that the economy is evolving (rather slowly) over time. This feature cannot easily be captured by fixed specification linear models, however, and manifests itself in the form of evolving coefficient estimates. We also provide additional evidence supporting the claim that models which ‘win’ based on one model selection criterion (say a squared error measure) do not necessarily win when an alternative selection criterion is used (say a confusion rate measure), thus highlighting the importance of the particular cost function which is used by forecasters and ‘end-users’ to evaluate their models. A wide variety of different model selection criteria and statistical tests are used to illustrate our findings.     

A strategic human resource perspective of firm competitive behavior

Embedded within firms are unique stores of intangible human assets that likely influence the way firms compete. We argue that the human and social capital of a firm, particularly at the upper echelon and board of director (BOD) levels, contribute to the firm's awareness of the competitive environment and its motivation and ability to undertake numerous, complex, and forceful competitive actions. We also suggest that the firm's executive compensation systems moderate the effects of these intangible human assets on firm competitive behavior. By examining how human capital, intra-firm social capital, and executive compensation influence firm competitive behavior, we advance a strategic HRM perspective of firm competitive behavior and outline several implications for future research.     

Are generalized call-spreads efficient?

In order to explain coexistence of a deductible for low values of the loss and an upper limit for high values of the loss in insurance contracts, we consider the exchange of risk between two rank dependent expected utility maximizers. It is shown that if the insurer (insured) takes more into account the lowest outcomes – hence maximal losses – than the insured (insurer), then the optimal contract has an upper limit (includes a deductible for high values of the loss). If furthermore, the insured (insurer) neglects the highest outcomes while the insurer (insured) does not, the optimal contract includes a deductible (full insurance) for low values of the loss.     

A comparison of parametric and non-parametric efficiency measures: The case of Norwegian ferries

The trunk road system in Norway has to be supplemented by a number of ferries due to the long coastline with numerous islands and fjords. Most of the ferries are run by private companies, but at a loss. The deficit are covered by the Ministry of Transport. The subsidies have risen rapidly in the last years and have focussed attention on whether the ferries are really run as efficiently as possible. To change the incentives to economize, a lump-sum payment is considered. To implement such a system, an initial assessment of reasonable input requirements is needed. The aim of this article is to provide such a yardstick by establishing a best practice frontier. Both a non-parametric and a parametric approach to a deterministic frontier are tried and differences of results discussed. Peculiarities due to choice of methods are revealed. The efficiency distributions are quite similar for the two methods except for scale efficiency, where the parametric method indicates substantial unrealized scale economies, while the non-parametric approach shows the largest and some small ferries to be scale efficient. The results indicate a substantial rationalization potential of about 30 percent in total.I am indebted to three referees for forcing me to improve significantly the quality of the study. Any remaining shortcomings are, of course, my responsibility.     

Are robust estimation methods useful in the structural errors-in-variables model?

Summary In this paper it is investigated whether robust estimation procedures for the parameters of a regression model are also applicable when the observations are generated by the errors-in-variables model. Specifically, attention is paid to bounded-influence estimators, i.e. estimators that are constructed in such a way that the influence of a single observation on the outcome of the estimator is bounded. Both the classical errors-in-variables model and models with contaminated observational errors are considered.The authors are indebted to a referee for his valuable comments on an earlier version of this paper.