A Hotelling–Downs model of electoral competition with the option to quit

We study a variant of the multi-candidate Hotelling–Downs model that recognizes that politicians, even after declaring candidacy, have the option of withdrawing from the electoral contest before the election date and saving the cost of continuing campaign. We find that this natural variant significantly alters equilibrium predictions. We give conditions for the existence of an equilibrium for an arbitrary finite number of candidates and an arbitrary distribution of single-peaked preferences of voters. We also provide a partial characterization of the equilibrium outcomes that addresses whether policy convergence can be a feature of equilibrium outcomes when more than two candidates enter the electoral contest.     

Some order relations between selection and inclusion probabilities for PPSWOR sampling scheme

In this paper we study some order relations between the selection and the inclusion probabilities for PPSWOR Sampling Scheme. We also establish some interesting bounds on the inclusion probabilities in terms of the selection probabilities.     

Coalition-proof Bertrand equilibria

Summary. This paper examines the coalition-proof Nash equilibria of a Bertrand model of price competition where firms supply all demand. When firms are asymmetric we prove existence and provide a sufficient condition for uniqueness. For symmetric firms, we show that an equilibrium is necessarily unique. We also examine whether this unique equilibrium outcome is implementable through a sequential move game where the firms take turns at announcing prices. Finally we examine the limiting property of such equilibria as the number of firms go to infinity.Received: 20 March 2002, Revised: 5 August 2003JEL Classification Numbers: D43, D41, L13.Correspondence to: Kunal SenguptaWe are deeply indebted to an anonymous referee for very helpful and incisive comments that led to substantial improvements in the paper. We also gratefully acknowledge the hospitality of the Department of Finance, Hong Kong University of Science and Technology where much of the work on this paper was carried out.     

The experience trap

When companies put seasoned managers in charge of important projects, they don't expect missed deadlines, budget overruns, and rampant defects. However, that's what researchers found when they tested hundreds of experienced project managers with computer games that simulated software development projects. The study, conducted by two professors from Insead and one from Naval Postgraduate School, strongly suggests that veterans in complex environments suffer a breakdown in the learning process. The research reveals three reasons for the breakdowns: Time lags between causes and effects make it difficult to see how they're connected; fallible estimates color the chain of decisions that determine a project's outcome; and a bias toward the initial goals prevents managers from setting revised, more appropriate, targets when project circumstances change. Sticking to an initial low budget goal after a project grew in scope, for instance, led subjects to ignore quality assurance, which led to soaring defect rates--and costs. Companies can take practical steps to fix the learning cycle. They can provide feedback that shows the relationships between important variables in the environment. Such feedback might reveal, say, the 20-day ramp-up that a new quality assurance team needs before becoming fully effective. Tools that apply formal models to calculate such things as the effect of turnover on team productivity also help. Setting goals for behavior, instead of targets for performance, is critical as well. Finally, firms can create project "flight simulators" that mimic actual learning environments but don't let complexity overwhelm trainees. Managers can continue learning only if they get decision support tailored to the challenges they face. Firms would do well to focus more on training people higher up in the organization and stop leaving them to fend for themselves.     

Sequential estimation of a linear function of normal means under asymmetric loss function

The problem of estimating a linear function of k normal means with unknown variances is considered under an asymmetric loss function such that the associated risk is bounded from above by a known quantity. In the absence of a fixed sample size rule, sequential stopping rules satisfying a general set of assumptions are considered. Two estimators are proposed and second-order asymptotic expansions of their risk functions are derived. It is shown that the usual estimator, namely the linear function of the sample means, is asymptotically inadmissible, being dominated by a shrinkage-type estimator. An example illustrates the use of different multistage sampling schemes and provides asymptotic expansions of the risk functions. Received: August 1999     

THE OPTIMAL CAPACITY EXPANSION POLICY FOR A MULTI-PRODUCT FIRM UNDER A DYNAMIC FRAMEWORK *

An optimal capacity policy model for a multi-product firm is constructed in a dynamic programming framework, which assumes the following: all current inputs have a perfectly competitive market, all capital resources are limitational, though capable of being utilized in more than one production line and there is a marketing cost function of a specified type for the outputs sold.     

Interlinkages between external debt financing,credit cycles and output fluctuations in emerging market economies

Review of World Economics - This paper examines the role of external debt financing (EDF) in shaping the credit cycles and the joint implications of EDF flows and credit growth for the output...