Perfect Bayesian implementation when the planner is a player

The paper contributes to the perfect Bayesian implementation problem when the planner selects an outcome after agents send a signal profile. In our problem, the planner always selects the outcomes that maximize her expected utility, given her posterior belief about the state. The paper explicitly models the problem and shows a full characterization of SCF set that can be perfect Bayesian implemented in FGP equilibrium.     

The <Emphasis Type="Italic">n</Emphasis>-person Kalai-Smorodinsky bargaining solution under pre-donations

This study examines the behavior of simple n-person bargaining problems under pre-donations where the Kalai-Smorodinsky (KS) solution is operant. Pre- donations are a unilateral commitment to transfer a portion of one’s utility to someone else, and are used to distort the bargaining set and thereby influence the bargaining solution. In equilibrium, these pre-donations are Pareto-improving over the undistorted solution; moreover, when the agents’ preferences are sufficiently distinct, the equilibrium solution coincides with the concessionary division rule.     

Bayes风险值

风险值(VaR)是最近几年发展起来的一种市场风险度量尺度,是目前国际上最常用的金融市场风险度量基准。传统的VaR估计方法基本上都依赖于历史数据,并且只能在市场处于正常变动时衡量所面临的风险。由于金融市场中影响资产收益状况的因素时刻都在变化之中,资产收益分布中的参数也是不断变化的,因此用Bayes方法预测未来收益状况的VaR更合适。本文给出了BVaR和BCVaR的概念,讨论了BVa／R的性质与特点,并对市场因子服从正态分布时组合BVaR以及非线性组合BVaR的计算进行了研究。     

Bayesian incentive compatible beliefs

The problem of incentives for correct revelation is studied as a game with incomplete information where players have individual beliefs concerning other's types. General conditions on the beliefs are given which are shown to be sufficient for the existence of a Pareto-efficient mechanism for which truth-telling is a Bayesian equilibrium.     

Constitutional implementation

We consider the problem of implementing a social choice correspondence H in Nash equilibrium when the constitution of the society is given by an effectivity function E. It is assumed that the effectivity function of , is a sub-correspondence of E. We found necessary and efficient conditions for a game form to implement H (in Nash equilibria), and to satisfy, at the same time, that , the effectivity function of , is a sub-correspondence of (which guarantees that is compatible with E). We also find sufficient conditions for the coincidence of the set of winning coalitions of and , and for . All our results are sharp as is shown by suitable examples. Received: 15 December 2000 / Accepted: 3 September 2001     

Work-team implementation

The authors describe the implementation of the Work-Team Concept at the Frigidaire plans in Jefferson, Iowa. By forming teams, plant staff have made significant improvements in worker safety, product quality, customer service, cost-effectiveness, and overall employee well-being.     

基于J2EE的SMS设计与实现

J2EE是一种全新概念的企业应用系统开发模型。文章探讨了基于J2EE的SMS系统开发过程中遇到的问题,给出了相应的解决方法,并且讨论了具体应用中各功能模块的设计与实现,为企业信息发布和提供服务给出了一个新的解决平台。     

Bayesian inference for pareto populations

Bayesian techniques for samples from classical, generalized and multivariate Pareto distributions are described. We place emphasis on choosing proper prior distributions that do not lead to anomalous posterior densities.     

Bayesian semiparametric stochastic volatility modeling

This paper extends the existing fully parametric Bayesian literature on stochastic volatility to allow for more general return distributions. Instead of specifying a particular distribution for the return innovation, nonparametric Bayesian methods are used to flexibly model the skewness and kurtosis of the distribution while the dynamics of volatility continue to be modeled with a parametric structure. Our semiparametric Bayesian approach provides a full characterization of parametric and distributional uncertainty. A Markov chain Monte Carlo sampling approach to estimation is presented with theoretical and computational issues for simulation from the posterior predictive distributions. An empirical example compares the new model to standard parametric stochastic volatility models.     

Bayesian semiparametric multivariate GARCH modeling

This paper proposes a Bayesian nonparametric modeling approach for the return distribution in multivariate GARCH models. In contrast to the parametric literature the return distribution can display general forms of asymmetry and thick tails. An infinite mixture of multivariate normals is given a flexible Dirichlet process prior. The GARCH functional form enters into each of the components of this mixture. We discuss conjugate methods that allow for scale mixtures and nonconjugate methods which provide mixing over both the location and scale of the normal components. MCMC methods are introduced for posterior simulation and computation of the predictive density. Bayes factors and density forecasts with comparisons to GARCH models with Student-t$t$ innovations demonstrate the gains from our flexible modeling approach.     

Solution of a problem concerning a 6-person simple game from “theory of games and economic behavior” by von Neumann and Morgenstern

A particular 6-person simple game is examined; the question concerning the existence of solutions in special form is answered negatively.Versione definitiva pervenuta il 4-2-81     

Two-agent interactive implementation

Review of Economic Design - This study investigates the implementation of social choice rules (SCRs) in a situation where a planner not only designs the mechanism, but also participates as a...     

Role of experimental randomization in Bayesian statistics: An asymptotic theory for a single Bayesian

Summary Many experimental situations with controllable, independent design variables have an associated null hypothesis ₀ of zero dependence of observations on the design variables. The necessity of randomization of the design variables for asymptotic discriminability between ₀ and its complement is considered in terms of likelihood ratios. Applications are made to stochastic processes and comparative experiments.

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Zusammenfassung Viele experimentelle Situationen mit kontrollierten, unabhängigen Planungsvariablen haben eine assoziierte Null-Hypothese ₀, die besagt, daß die Beobachtungen nicht von den Planungsvariablen abhängen. Die Notwendigkeit der Randomisierung der Planungsvariablen für asymptotische Diskriminanz zwischen ₀ und ihres Komplementes wird mit Hilfe des Likelihood-Verhältnisses untersucht. Anwendungen auf dem Gebiet stochastischer Prozesse und komparativer Experimente werden diskutiert.

     

On Bayesian routes to unit roots

This paper is a comment on P. C. B. Phillips, ‘To criticise the critics: an objective Bayesian analysis of stochastic trends’ [Phillips, (1991)]. Departing from the likelihood of an univariate autoregressive model different routes that lead to a posterior odds analysis of the unit root hypothesis are explored, where the differences in routes are due to the different choices of the prior. Improper priors like the uniform and the Jeffreys prior are less suited for Bayesian inference on a sharp null hypothesis as the unit root. A proper normal prior on the mean of the process is analysed and empirical results using extended Nelson-Plosser data are presented.     

Bayesian prediction with cointegrated vector autoregressions

A complete procedure for calculating the joint predictive distribution of future observations based on the cointegrated vector autoregression is presented. The large degree of uncertainty in the choice of cointegration vectors is incorporated into the analysis via the prior distribution. This prior has the effect of weighing the predictive distributions based on the models with different cointegration vectors into an overall predictive distribution. The ideas of Litterman [Mimeo, Massachusetts Institute of Technology, 1980] are adopted for the prior on the short run dynamics of the process resulting in a prior which only depends on a few hyperparameters. A straightforward numerical evaluation of the predictive distribution based on Gibbs sampling is proposed. The prediction procedure is applied to a seven-variable system with a focus on forecasting Swedish inflation.     

Bayesian incentive compatible parametrization of mechanisms

We consider a general scheme to construct Bayesian incentive-compatible mechanisms using a suitable ‘variable mechanism parametrization.’ The key idea is to perturb a given direct mechanism, which might not be truth revealing, introducing sufficient variability as a function of agents’ announcements to generate incentives for truthful revelation. We discuss a variable-price auction in a general setting as an example.     

On model selection in Bayesian regression

We discuss the problem of constructing a suitable regression model from a nonparametric Bayesian viewpoint. For this purpose, we consider the case when the error terms have symmetric and unimodal densities. By the Khintchine and Shepp theorem, the density of response variable can be written as a scale mixture of uniform densities. The mixing distribution is assumed to have a Dirichlet process prior. We further consider appropriate prior distributions for other parameters as the components of the predictive device. Among the possible submodels, we select the one which has the highest posterior probability. An example is given to illustrate the approach.     

Bayesian stochastic frontier analysis using WinBUGS

Markov chain Monte Carlo (MCMC) methods have become a ubiquitous tool in Bayesian analysis. This paper implements MCMC methods for Bayesian analysis of stochastic frontier models using the WinBUGS package, a freely available software. General code for cross-sectional and panel data are presented and various ways of summarizing posterior inference are discussed. Several examples illustrate that analyses with models of genuine practical interest can be performed straightforwardly and model changes are easily implemented. Although WinBUGS may not be that efficient for more complicated models, it does make Bayesian inference with stochastic frontier models easily accessible for applied researchers and its generic structure allows for a lot of flexibility in model specification.