基于时间序列的BP神经网络预测鄂尔多斯市的GDP

国内生产总值(GDP)是制定经济发展战略、规划、年度计划以及各种宏观经济政策的重要依据,研究GDP预测方法具有重要的现实意义。在对鄂尔多斯市GDP数据序列分析的基础上,考虑到BP神经网络可以通过简单非线性函数的复合能够实现对于复杂函数的映射,本文采用BP神经网络方法来预测鄂尔多斯市的GDP,取得了较好的预测效果。     

Nonparametric learning rules from bandit experiments: The eyes have it!

How do people learn? We assess, in a model-free manner, subjectsʼ belief dynamics in a two-armed bandit learning experiment. A novel feature of our approach is to supplement the choice and reward data with subjectsʼ eye movements during the experiment to pin down estimates of subjectsʼ beliefs. Estimates show that subjects are more reluctant to “update down” following unsuccessful choices, than “update up” following successful choices. The profits from following the estimated learning and decision rules are smaller (by about 25% of average earnings by subjects in this experiment) than what would be obtained from a fully-rational Bayesian learning model, but comparable to the profits from alternative non-Bayesian learning models, including reinforcement learning and a simple “win-stay” choice heuristic.     

Intercountry feedback and spillover effects within the international supply and use framework: a Bayesian perspective*

This paper proposes a new framework for the estimation of product-level global and interregional feedback and spillover (FS) factor multipliers. The framework is directly based on interregional supply and use tables (SUTs) that could be rectangular and gives a possibility of taking account of the inherent input–output data uncertainty problems. A Bayesian econometric approach is applied to the framework using the first version of international SUTs in the World Input–Output Database. The obtained estimates of the global and intercountry FS output effects are discussed and presented at the world, country and product levels for the period of 1995–2009.     

Nowcasting China’s GDP Using a Bayesian Approach

Real time nowcasting is an assessment of current-quarter GDP from timely released economic and financial series before the GDP figure is disseminated. Providing a reliable current quarter nowcast in real time based on the most recently released economic and financial monthly data is crucial for central banks to make policy decisions and longer-term forecasting exercises. In this study, we use dynamic factor models to bridge monthly information with quarterly GDP and achieve reduction in the dimensionality of the monthly data. We develop a Bayesian approach to provide a way to deal with the unbalanced features of the dataset and to estimate latent common factors. We demonstrate the validity of our approach through simulation studies, and explore the applicability of our approach through an empirical study in nowcasting the China’s GDP using 117 monthly data series of several categories in the Chinese market. The simulation studies and empirical study indicate that our Bayesian approach may be a viable option for nowcasting the China’s GDP.     

Strategic knowledge sharing in Bayesian games

This paper provides a model for the study of direct, public and strategic knowledge sharing in Bayesian games. We propose an equilibrium concept which takes into account communication possibilities of exogenously certifiable statements and in which beliefs off the equilibrium path are explicitly deduced from consistent possibility correspondences, without making reference to perturbed games. Properties of such an equilibrium and of revised knowledge are examined. In particular, it is shown that our equilibrium is always a sequential equilibrium of the associated extensive form game with communication. Finally, sufficient conditions for the existence of perfectly revealing or non-revealing equilibria are characterized in some classes of games. Several examples and economic applications are investigated.     

我国非制造业上市公司财务失败预警模型的实证研究

财务失败预警模型在世界范围内的应用已经很广泛了,但其在我国却仍处于研究探索阶段.笔者对目前在实践中最常用的Altman非制造业上市公司财务失败预警模型(Z3模型)在我国上市公司中的应用进行了实证研究,并对其研究过程及结果进行了分析、总结和评价,最后根据其在我国的实际应用中所存在的问题提出了一些修改建议并通过具体的实例研究对修改结果做了进一步的验证。     

Half empty, half full and why we can agree to disagree forever

Aumann [Aumann R., 1976. Agreeing to disagree. Annals of Statisitics 4, 1236–1239] derives his famous we cannot agree to disagree result under the assumption that people are expected utility (=EU) decision makers. Motivated by empirical evidence against EU theory, we study the possibility of agreeing to disagree within the framework of Choquet expected utility (=CEU) theory which generalizes EU theory by allowing for ambiguous beliefs. As our first main contribution, we show that people may well agree to disagree if their Bayesian updating of ambiguous beliefs is psychologically biased in our sense. Remarkably, this finding holds regardless of whether people with identical priors apply the same psychologically biased Bayesian update rule or not. As our second main contribution, we develop a formal model of Bayesian learning under ambiguity. As a key feature of our approach the posterior subjective beliefs do, in general, not converge to “true” probabilities which is in line with psychological evidence against converging learning behavior. This finding thus formally establishes that CEU decision makers may even agree to disagree in the long-run despite the fact that they always received the same information.     

Bayesian–Walrasian equilibria: beyond the rational expectations equilibrium

In general rational expectations equilibrium (REE), as introduced in Radner (Econometrica 47:655–678, 1978) in an Arrow–Debreu–McKenzie setting with uncertainty, does not exist. Moreover, it fails to be fully Pareto optimal and incentive compatible and is also not implementable as a perfect Bayesian equilibrium of an extensive form game (Glycopantis et al. in Econ Theory 26:765–791, 2005). The lack of all the above properties is mainly due to the fact that the agents are supposed to predict the equilibrium market clearing price (as agent’s expected maximized utility is conditioned on the information that equilibrium prices reveal), which leads inevitably to the presumption that agents know all the primitives in the economy, i.e., random initial endowments, random utility functions and private information sets. To get around this problematic equilibrium notion, we introduce a new concept called Bayesian–Walrasian equilibrium (BWE) which has Bayesian features. In particular, agents try to predict the market-clearing prices using Bayesian updating and evaluate their consumption in terms of Bayesian price estimates, which are different for each individual. In this framework agents maximize expected utility conditioned on their own private information about the state of nature, subject to a Bayesian estimated budget constraint. Market clearing is not an intrinsic part of the definition of BWE. However, both in the case of perfect foresight and in the case of symmetric information BWE leads to a statewise market clearing; it then becomes an ex post Walrasian equilibrium allocation. This new BWE exists under standard assumptions, in contrast to the REE. In particular, we show that our new BWE exists in the well-known example in Kreps (J Econ Theory 14:32–43, 1977), where REE fails to exist. This work was done in the Spring of 2005, when EJB was a visiting professor at the University of Illinois.     

语域预测在听力教学中的应用

语域理论是系统功能语言学中的一个重要理论。系统功能语言学在语境下研究语域,语域和语境是互为预测关系。将语域预测应用到大学英语听力教学可以提高学生的语言意识和元语言能力,从而可以提高学生的听力理解水平。     

中国商品期货市场的假日效应研究——基于收益和波动的视角

为研究中国商品期货市场假日效应的存在性及其特征,本文从收益和波动出发,在构建学生分布随机波动模型的基础上采用贝叶斯MCMC模拟技术对中国铜、铝、橡胶、大豆、豆粕和小麦期货市场的假日效应进行了实证分析,研究结果显示:假日前和假日后信息对商品期货交易收益及其波动均具有显著的影响,对不同交易品种而言,其影响方向及影响程度均存在一定差异;更具体地,对各类假日分别进行分析发现,元旦、春节、劳动节和国庆节的假日前和假日后信息对商品期货收益及其波动均具有显著的影响,且比分类之前假日前和假日后信息的影响能力明显增强,其个性特征也更加突出。