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To address the safety concerns generated by truck crashes occurred in big cities, this paper analyzes the zip code tabulation area (ZCTA)-based truck crash frequency across four temporal intervals – morning (6:00–10:00), mid-day (10:00–15:00), afternoon (15:00–19:00), and night (19:00–6:00) in New York City in 2010. A multivariate conditional autoregressive count model is used to recognize both spatial and temporal dependences. The results prove the presence of spatial and temporal dependencies for truck crashes that occurred in neighboring areas. Built environment attributes such as various types of business establishment density and traffic volume for different types of vehicles, which are important factors to consider for crashes occurred in an urban setting, are also examined in the study.

The purpose of this paper is to study likelihood ratio order for mixture and its components via their Glaser’s functions for weighted distributions. So, some theoretical examples using exponential family and their mixtures are presented.

First, Glaser’s functions of mixture and its components for weighted distributions in different scenarios are computed. Then by them the likelihood ratio order is investigated between mixture and its components.

The authors find conditions for weight functions under which the mixture random variable is between of its components in likelihood ratio order.

Results are obtained for weight function in general. It is well known that the some special weights are order statistics, up and down records, hazard rate, reversed hazard rate, moment generating function, etc. So, the results are valid for all of them.

In this paper we look at patents as alternative to trade secrets. We disentangle the disclosure motive for patent protection from the traditional reward motive by adjusting the level of patent protection so as to make the innovator just indifferent between patenting and keeping the innovation secret. Thus, we keep the reward (expected profits) to the innovator fixed and focus on ex post efficiency. When duplication is not feasible and secrecy only entails the risk of public disclosure (a leakage), patents and secrets are perfect substitutes. Yet, a distinctive features of trade secret protection is that it allows for independent creation. The duplicative efforts to reproduce a concealed innovation make patents and secrets imperfect substitutes. If such duplicative efforts are actually exerted under secrecy, patents provide the pre-specified incentive to innovate at least social cost. If, however, the threat of duplication induces the innovator to preemptively license her trade secret, and such licensing agreements allow the innovator to appropriate all the saved duplication costs, then secrets can reward innovative activity more efficiently than patents. Thus, the issue of whether patents are socially preferable to secrets boils down to an assessment of the prevalence and the efficiency of trade secret licensing. The available empirical evidence suggests that licensing of trade secret information is limited and so hints at the superiority of patents.

This paper aims to illustrate how a Bayesian approach to yield fitting can be implemented in a non-parametric framework with automatic smoothing inferred from the data. It also briefly illustrates the advantages of such an approach using real data.

The paper uses an infinite dimensional (functional space) approach to inverse problems. Numerical computations are carried out using a Markov Chain Monte-Carlo algorithm with several tweaks to ensure good performance. The model explicitly uses bid-ask spreads to allow for observation errors and provides automatic smoothing based on them.

A non-parametric framework allows to capture complex shapes of zero-coupon yield curves typical for emerging markets. Bayesian approach allows to assess the precision of estimates, which is crucial for some applications. Examples of estimation results are reported for three different bond markets: liquid (German), medium liquidity (Chinese) and illiquid (Russian).

The result shows that infinite-dimensional Bayesian approach to term structure estimation is feasible. Market practitioners could use this approach to gain more insight into interest rates term structure. For example, they could now be able to complement their non-parametric term structure estimates with Bayesian confidence intervals, which would allow them to assess statistical significance of their results.

The model does not require parameter tuning during estimation. It has its own parameters, but they are to be selected during model setup.

The purpose of this paper is to address the problem of scheduling under uncertainty in construction projects. The existing methods for determining a project schedule are based on assumption of complete knowledge of project parameters; but in reality there is uncertainty in construction projects, deriving from a multitude of context‐dependent sources and often provided as outcome of a risk analysis process. Thus, classical deterministic analysis might provide a schedule which is not sufficiently protected against possible disruptions.

A quantitative methodology is developed for planning construction projects under uncertainty aimed at determining a reliable resource feasible project schedule by taking into account the available probabilistic information to produce solutions that are less sensitive to perturbations that occur on line. The methodology relies on a computer‐supported system that allows to identify, analyze and quantify the schedule reliability and the impact of possible disruptions on the duration of the project.

It is found that the proposed methodology can exploit more information about the uncertain parameters than the commonly‐used deterministic method, and it provides an improved understanding of the schedule reliability in presence of uncertainty. The schedule generated with a classical deterministic method sets a completely unrealistic planned project delivery date of about 1,250 days, with a probability around 50 per cent to be exceeded. This behavior can be very unsatisfactory for construction projects for which high penalties are usually associated to heavy due date violations.

This paper presents an approach for robust scheduling of construction project problem under uncertainty. We provide a tool able to support managers in developing a workable and realistic project schedule to be used as a guideline for project control and monitoring.

Economic conditions such as convexity, homogeneity, homotheticity, and monotonicity are all important assumptions or consequences of assumptions of economic functionals to be estimated. Recent research has seen a renewed interest in imposing constraints in nonparametric regression. We survey the available methods in the literature, discuss the challenges that present themselves when empirically implementing these methods, and extend an existing method to handle general nonlinear constraints. A heuristic discussion on the empirical implementation for methods that use sequential quadratic programming is provided for the reader, and simulated and empirical evidence on the distinction between constrained and unconstrained nonparametric regression surfaces is covered.

A large literature studies why firms self-regulate and “signal green.” However, it has ignored that regulators have enforcement discretion, and may act strategically. We fill this gap. We build a game theoretic model of whether a firm should signal its type through substantial self-regulation. We find self-regulation is a double-edged sword: it can potentially preempt legislation, but it can also lead regulators to demand higher levels of compliance from greener firms if preemption fails. We show how self-regulatory decisions depend upon industry characteristics and political responsiveness to corporate environmental leadership. We have made a number of simplifying assumptions. We assume activist groups cannot challenge regulatory flexibility in court, and that regulatory penalties are fixed and are not collected by the regulator. Firms with low compliance costs confront a tradeoff regarding self-regulation. They can blend in with the rest of the industry, and take few self-regulatory steps. This reduces the risk of regulation somewhat, and preserves their ability to obtain regulatory flexibility should regulation be imposed. Alternatively, they can step up with substantial self-regulation. This better mitigates the risk of regulation, but at the risk of signaling low costs and becoming a target for stringent enforcement should regulation pass. Recent work has found negative market reactions to corporate claims of voluntary emissions reductions, despite the conventional wisdom that it “pays to be green.” We offer a new explanation to scholars and managers: regulatory discretion may undermine the ability of industry self-regulation to profitably preempt mandatory regulatory requirements.

This study considers a contracting problem between a fairness concerned entrepreneur (EN) and a fair-neutral venture capitalist (VC) to explore the effects of asymmetry, agency conflicts and fairness concerns.

The authors construct the model by assuming the EN's risk aversion degree is private information, which is more realistic but ignored in most studies. Under the principal–agent framework, the authors solve the VC's optimal contracting models by identifying the ranges of feasible solution, where the optimal solutions of these models are explicit and nicely reconcile the “private equity” puzzle. Moreover, validity of the optimal solutions is verified by numerical simulations.

In accordance with empirical evidence, information asymmetry lowers the optimal equity share that the VC provides to EN but raises EN's profit due to lower effort disutility and information rent. Moreover, the authors find that the fairness concerns is beneficial for the EN, where it not only increases the EN's optimal equity share, but also enhances the certainty equivalence of the EN's utility regarding its profit. Relative to the benchmark model where the EN's risk aversion degree is common knowledge, the EN's efforts recommended by the optimal contract is less sensitive to the EN's fairness concerns degree when the EN does not actually announce its risk aversion degree.

First, the authors incorporate asymmetry to study a two-period contracting problem and explore how it affects the equity shares allocated to the contractual parties. Second, the authors incorporate fairness concerns and analyze its effect regarding the decision-makings and profits.

This paper aims to consider the effects of a merger on technology adoption and welfare in the presence of passive cross ownership. Merger increases investments in process technology and may increase welfare. The results are important for antitrust policies and suggest that the antitrust authorities may not need to be too concerned about mergers in industries with cross ownership.

Game-theoretic analysis.

Merger increases investments in process technology and may increase welfare.

To the best of the author’s knowledge, this study is original.

We explore the driving forces behind the channel choices of the manufacturer and the platform by considering asymmetric selling cost and demand information.

This paper develops game-theoretical models to study different channel strategies for an E-commerce supply chain, in which a manufacturer distributes products through a platform that may operate in either the marketplace channel or the reseller channel.

Three primary models are built and analyzed. The comparison results show that the platform would share demand information in the reseller channel only if the service cost performance is relatively high. Besides, with an increasing selling cost, the equilibrium channel might shift from the marketplace to the reseller. With increasing information accuracy, the manufacturer tends to select the marketplace channel, while the platform tends to select the reseller channel if the service cost performance is low and tends to select the marketplace channel otherwise.

All these results have been numerically verified in the experiments. At last, we also resort to numerical study and find that as the service cost performance increases, the equilibrium channel may shift from the reseller channel to the marketplace channel. These results provide managerial guidance to online platforms and manufacturers regarding strategic decisions on channel management.

Although prior research has paid extensive attention to the driving forces behind the online channel choice between marketplace and reseller, there is at present few study considering the case where a manufacturer selling through an online platform faces a demand information disadvantage in the reseller channel and sales inefficiency in the marketplace channel. To fill this research gap, our work illustrates the interaction between demand information asymmetry and selling cost asymmetry to identify the equilibrium channel strategy and provides useful managerial guidelines for both online platforms and manufacturers.