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.     

Approximate Bayesian forecasting

Approximate Bayesian Computation (ABC) has become increasingly prominent as a method for conducting parameter inference in a range of challenging statistical problems, most notably those characterized by an intractable likelihood function. In this paper, we focus on the use of ABC not as a tool for parametric inference, but as a means of generating probabilistic forecasts; or for conducting what we refer to as ‘approximate Bayesian forecasting’. The four key issues explored are: (i) the link between the theoretical behavior of the ABC posterior and that of the ABC-based predictive; (ii) the use of proper scoring rules to measure the (potential) loss of forecast accuracy when using an approximate rather than an exact predictive; (iii) the performance of approximate Bayesian forecasting in state space models; and (iv) the use of forecasting criteria to inform the selection of ABC summaries in empirical settings. The primary finding of the paper is that ABC can provide a computationally efficient means of generating probabilistic forecasts that are nearly identical to those produced by the exact predictive, and in a fraction of the time required to produce predictions via an exact method.     

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 exploratory factor analysis

This paper develops and applies a Bayesian approach to Exploratory Factor Analysis that improves on ad hoc classical approaches. Our framework relies on dedicated factor models and simultaneously determines the number of factors, the allocation of each measurement to a unique factor, and the corresponding factor loadings. Classical identification criteria are applied and integrated into our Bayesian procedure to generate models that are stable and clearly interpretable. A Monte Carlo study confirms the validity of the approach. The method is used to produce interpretable low dimensional aggregates from a high dimensional set of psychological measurements.     

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.     

Bayesian quantile regression methods

This paper is a study of the application of Bayesian exponentially tilted empirical likelihood to inference about quantile regressions. In the case of simple quantiles we show the exact form for the likelihood implied by this method and compare it with the Bayesian bootstrap and with Jeffreys' method. For regression quantiles we derive the asymptotic form of the posterior density. We also examine Markov chain Monte Carlo simulations with a proposal density formed from an overdispersed version of the limiting normal density. We show that the algorithm works well even in models with an endogenous regressor when the instruments are not too weak. Copyright © 2009 John Wiley & Sons, Ltd.     

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.     

Noncausal Bayesian Vector Autoregression

We consider Bayesian analysis of the noncausal vector autoregressive model that is capable of capturing nonlinearities and effects of missing variables. Specifically, we devise a fast and reliable posterior simulator that yields the predictive distribution as a by‐product. We apply the methods to postwar US inflation and GDP growth. The noncausal model is found superior in terms of both in‐sample fit and out‐of‐sample forecasting performance over its conventional causal counterpart. Economic shocks based on the noncausal model turn out to be highly anticipated in advance. We also find the GDP growth to have predictive power for future inflation, but not vice versa. Copyright © 2016 John Wiley & Sons, Ltd.     

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。     

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     

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     

An urn-based Bayesian block bootstrap

Block bootstrap has been introduced in the literature for resampling dependent data, i.e. stationary processes. One of the main assumptions in block bootstrapping is that the blocks of observations are exchangeable, i.e. their joint distribution is immune to permutations. In this paper we propose a new Bayesian approach to block bootstrapping, starting from the construction of exchangeable blocks. Our sampling mechanism is based on a particular class of reinforced urn processes.     

Bayesian learning with variable prior

How much can be learned from a noisy signal about the state of the world not only depends on the accuracy of the signal, but also on the distribution of the prior. Therefore, we define a general information system as a tuple consisting of both a signal technology and a prior. In this paper we develop a learning order for general information systems and characterize the order in two different ways: first, in terms of the dispersion of posterior beliefs about state quantiles and, second, in terms of the value of learning for two different classes of decision makers. The first class includes all agents with quasi-linear quantile preferences, and the second class contains all agents with supermodular quantile preferences.     

Bayesian estimation of agent-based models

We consider Bayesian inference techniques for agent-based (AB) models, as an alternative to simulated minimum distance (SMD). Three computationally heavy steps are involved: (i) simulating the model, (ii) estimating the likelihood and (iii) sampling from the posterior distribution of the parameters. Computational complexity of AB models implies that efficient techniques have to be used with respect to points (ii) and (iii), possibly involving approximations. We first discuss non-parametric (kernel density) estimation of the likelihood, coupled with Markov chain Monte Carlo sampling schemes. We then turn to parametric approximations of the likelihood, which can be derived by observing the distribution of the simulation outcomes around the statistical equilibria, or by assuming a specific form for the distribution of external deviations in the data. Finally, we introduce Approximate Bayesian Computation techniques for likelihood-free estimation. These allow embedding SMD methods in a Bayesian framework, and are particularly suited when robust estimation is needed. These techniques are first tested in a simple price discovery model with one parameter, and then employed to estimate the behavioural macroeconomic model of De Grauwe (2012), with nine unknown parameters.     

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.