Testing a Model of Trade Union Behaviour: An Application of a Multivariate Cointegration Analysis to UK Aggregate Wage Data

ABSTRACT The first part of the paper considers a behavioural model of trade unionism. Wages and employment are characterized as the outcome of a process by which the union maximizes an objective function containing wages and employment as arguments, and is constrained by a trade-off between these two variables as represented by the firm's labour demand function. In the second part, the model's equilibrium predictions for wages are estimated in the framework of a multivariate error correction model and tested for cointegration with data from Britain's manufacturing sector from 1967 to 1986.     

Bootstrap Algorithms for Testing and Determining the Cointegration Rank in VAR Models1

In this paper a bootstrap algorithm for a reduced rank vector autoregressive model with a restricted linear trend and independent, identically distributed errors is analyzed. For testing the cointegration rank, the asymptotic distribution under the hypothesis is the same as for the usual likelihood ratio test, so that the bootstrap is consistent. It is furthermore shown that a bootstrap procedure for determining the rank is asymptotically consistent in the sense that the probability of choosing the rank smaller than the true one converges to zero.     

Estimating and Testing Structural Changes in Multivariate Regressions

This paper considers issues related to estimation, inference, and computation with multiple structural changes that occur at unknown dates in a system of equations. Changes can occur in the regression coefficients and/or the covariance matrix of the errors. We also allow arbitrary restrictions on these parameters, which permits the analysis of partial structural change models, common breaks that occur in all equations, breaks that occur in a subset of equations, and so forth. The method of estimation is quasi‐maximum likelihood based on Normal errors. The limiting distributions are obtained under more general assumptions than previous studies. For testing, we propose likelihood ratio type statistics to test the null hypothesis of no structural change and to select the number of changes. Structural change tests with restrictions on the parameters can be constructed to achieve higher power when prior information is present. For computation, an algorithm for an efficient procedure is proposed to construct the estimates and test statistics. We also introduce a novel locally ordered breaks model, which allows the breaks in different equations to be related yet not occurring at the same dates.     

不完全信息下的垄断企业规制定价研究

本文研究了不完全信息和存在政府规制情况下,高成本经销商和低成本经销商达到分离均衡的产品定价和对经销商的转移支付;同时,比较研究了完全信息和不完全信息两种情况下的社会福利损失以及在不完全信息情况下,存在政府规制和不存在政府规制时的社会福利改善。     

Testing the Interaction Effects of Task Complexity in Computer Training Using the Social Cognitive Model

Using a Modified Social Cognitive Theory framework, this study examines the behavior modeling and lecture‐based training approaches to computer training. It extends the existing Social Cognitive Model for computer training by adding the task complexity construct to training method, prior performance, computer self‐efficacy, outcome expectations, and performance. A sample of 249 students from a large state university served as participants in a laboratory experiment that was conducted to determine the task complexity*training method and task complexity* self‐efficacy interaction effects on performance. Structural equation modeling with interaction effects was used to analyze the data. The results show that behavior modeling outperforms lecture‐based training in a measure of final performance when task complexity is high. Further, it is found that computer self‐efficacy has a greater positive effect on performance when task complexity is high than when task complexity is low. Prior performance is also found to be an important variable in the model.     

B-CAPM模型的GMM估计和检验

作为资本资产定价模型（CAPM）的发展之一,B-CAPM模型更适合于复杂多变的现实资本市场。本文首先分析从CAPM到B-CAPM的模型转化及其理论含义,然后迭代求出B-CAPM模型的零贝塔期望收益的极大似然估计值（MLE）,最后通过案例,实证运用GMM方法构建B-CAPM的估计和检验。结果表明,B-CAPM模型适用于证券市场收益和风险的度量以及有效性检验,GMM方法更符合实际。     

Revealed Preference Tests of the Cournot Model

The aim of this paper is to develop revealed preference tests for Cournot equilibrium. The tests are akin to the widely used revealed preference tests for consumption, but have to take into account the presence of strategic interaction in a game‐theoretic setting. The tests take the form of linear programs, the solutions to which also allow us to recover cost information on the firms. To check that these nonparametric tests are sufficiently discriminating to reject real data, we apply them to the market for crude oil.     

股票流动性风险测度模型的构建与实证分析

传统度量流动性风险的方法是计算股票的平均流动性水平,近年来逐渐考虑到了流动性的波动性,对流动性风险的度量更加接近实际.本文在以上两种衡量流动性风险方法的基础上建立了包含"横"与"纵"两维上的新的流动性风险测度模型--流动性"成本-风险"矩形和等流动性风险曲线,并以上海证券交易所上市的107只A股为样本进行实证检验,结果表明该模型能够比较全面真实的测度股票的流动性风险.     

金融ARCH模型的贝叶斯检验和模型选择

在金融时间序列分析中,检验ARCH效果和决定合适的阶是ARCH模型的重要研究主题,在贝叶斯框架下,本文使用贝叶斯因子来检验ARCH效果和选择ARCH模型合适的阶。在路径抽样的基础上,提出了计算ARCH模型贝叶斯因子的方法。最后,我们用一个具体的实例来论证了所提方法的有效性。     

上海地方财政科技投入与经济增长的协整分析

本文根据时间序列动态均衡关系分析方法,通过上海1985-2001年地方财政科技投入与经济增长的有关数据变量进行协整分析与因果关系检验,并建立了相应的误差修正模型,揭示了上海地方财政科技投入与经济增长的动态均衡关系.     

具有多元马氏需求的多产品多阶段库存优化模型

本文研究一类新的多产品库存控制策略,即具有多元马氏需求特征的多产品多阶段的订货点订货量(Q, R, SS)策略,该策略考虑市场需求在不同产品之间具有多元马氏转移特征,并考虑缺货因素设置安全库存。论文首先建立了多产品多阶段的多元马氏需求预测模型,并通过该模型确定了各种产品需求之间的关系。同时,在该模型的理论基础上,提出了多产品多阶段的总期望成本模型及其最优(Q, R, SS)策略,进而结合算例给出模型的最优策略的数值解。     

GAS-SKST-F模型及其在高频多元波动率预测中的应用

假定日收益率服从多元有偏学生t分布、已实现协方差矩阵服从矩阵F分布,本文构建了一种新的得分驱动模型：GAS-SKST-F模型。在该有偏厚尾多元波动率模型中,我们基于广义自回归得分(GAS)模型的基本思想对收益率和已实现协方差矩阵进行联合动态设定,协方差矩阵的更新过程依赖于收益率分布和已实现协方差矩阵分布联合似然函数的得分函数。已实现协方差测度在协方差矩阵的更新过程中发挥了重要的作用。基于20支上证50成分股高频数据的实证分析研究结果显示,与GAS-N-Wishart模型和GAS-tF模型相比,无论样本内还是样本外,GAS-SKST-F模型有着更加良好的样本内估计和样本外预测能力。     

多元随机风险传染模型及沪铜场内外风险传染实证

为了更合适的度量金融市场间风险传染整体效果,本文通过理论分析构造了风险传染方程,其风险传染项相比已有的系数风险传染项与协方差时变风险传染项更具实际意义,并借鉴多元GARCH建模原理建立了结合均值溢出、波动溢出与风险传染项的多元随机风险传染模型,设计了模型的MCMC迭代求解算法,满足解的完整性。最后,运用模型对沪铜场内外风险传染现象进行了实证,实证结果不仅验证了一系列已有研究结论,同时还给出了一些符合期货实情的新结论,如金融市场间风险传染类似金融市场波动存在集聚效应、沪铜与沪铝市场存在风险传染交替变化现象、市场行情的变化能提前反映于风险传染效果中等。这也充分表明新模型的有效性、实用性以及优越性。     

Bayesian Methodology for Model Uncertainty Using Model Performance Data

A simple and useful characterization of many predictive models is in terms of model structure and model parameters. Accordingly, uncertainties in model predictions arise from uncertainties in the values assumed by the model parameters (parameter uncertainty) and the uncertainties and errors associated with the structure of the model (model uncertainty). When assessing uncertainty one is interested in identifying, at some level of confidence, the range of possible and then probable values of the unknown of interest. All sources of uncertainty and variability need to be considered. Although parameter uncertainty assessment has been extensively discussed in the literature, model uncertainty is a relatively new topic of discussion by the scientific community, despite being often the major contributor to the overall uncertainty. This article describes a Bayesian methodology for the assessment of model uncertainties, where models are treated as sources of information on the unknown of interest. The general framework is then specialized for the case where models provide point estimates about a single‐valued unknown, and where information about models are available in form of homogeneous and nonhomogeneous performance data (pairs of experimental observations and model predictions). Several example applications for physical models used in fire risk analysis are also provided.     

Acceptance Sampling Based on Truncated Life Tests in the Birnbaum Saunders Model

We develop acceptance sampling plans assuming that the life test is truncated at a preassigned time. The lifetimes of the test units are assumed to follow the Birnbaum Saunders distribution. The minimum sample size necessary to ensure the specified average life is obtained and the operating characteristic values of the sampling plans and producer's risk are presented. An illustrative example is given.     

Developing and Testing a Facility Location Model for Canadian Nuclear Fuel Waste

The purpose of this paper is to develop and test a facility location model for the siting of a nuclear fuel waste disposal facility in Canada. The model is based on successful Canadian siting processes related to hazardous waste and low level radioactive waste facilities, as well as attributes of facility siting found in the literature. The proposed model was presented to a sample of participants in the federal environmental assessment review of the technical feasibility of the Canadian Nuclear Fuel Waste Disposal Concept (CNFWDC) held throughout Canada in 1990. Results demonstrate that despite the fact that over half of the survey respondents did not support the CNFWDC during the public hearings, the majority favorably rated the proposed facility location model. Components of the model that were tested included siting criteria and goals, decision-making groups, and siting safeguards. On the basis of these results, it is concluded that the siting of a nuclear fuel waste disposal facility must make the decentralization of decision-making authority to local communities and governments a priority.     

Testing for Causal Effects in a Generalized Regression Model With Endogenous Regressors

A unifying framework to test for causal effects in nonlinear models is proposed. We consider a generalized linear‐index regression model with endogenous regressors and no parametric assumptions on the error disturbances. To test the significance of the effect of an endogenous regressor, we propose a statistic that is a kernel‐weighted version of the rank correlation statistic (tau) of Kendall (1938). The semiparametric model encompasses previous cases considered in the literature (continuous endogenous regressors (Blundell and Powell (2003)) and a single binary endogenous regressor (Vytlacil and Yildiz (2007))), but the testing approach is the first to allow for (i) multiple discrete endogenous regressors, (ii) endogenous regressors that are neither discrete nor continuous (e.g., a censored variable), and (iii) an arbitrary “mix” of endogenous regressors (e.g., one binary regressor and one continuous regressor).     

The Incremental Group Testing Model for Gap Closing in Sequencing Long Molecules

In this paper, we propose the incremental group testing model for the gap closing problem, which assumes that we can tell the difference between the outcome of testing a subset S, and the outcome of testing S {x}. We also give improvements over currently best results in literature for some other models.     

A Model for Optimization of Biomarker Testing Frequency to Minimize Disease and Cost: Example of Beryllium Sensitization Testing

A common problem with medical surveillance programs using biomarkers is determining the optimal frequency of testing to minimize adverse health effects and cost. In the case of beryllium-exposed workers, frequency of testing for beryllium sensitization may be especially important. Recent studies indicate a lack of dose response for beryllium sensitization, but do support a dose response for the development of chronic beryllium disease (CBD). Though unproven, this implies that early identification of sensitization and immediate removal from exposure may reduce development of CBD. A model is proposed to project the optimal frequency of sensitization testing using the current beryllium lymphocyte proliferation test (BeLPT) to minimize disease-related costs, assuming that a positive BeLPT will precede CBD. Conversion rates for cumulative exposure to disease development were adapted from the literature and used with testing costs and cost of disease estimates in the model. The model was run assuming several test frequency regimes. Results support the use of periodic testing in line with the annual schedule proposed in the Final Chronic Beryllium Disease Prevention Program Rule (1999) following initial testing within three months of first beryllium exposure. The financial and health benefits of reducing the time from exposure to detection of early disease was also explored with the model and demonstrated as a highly desirable characteristic for an alternative test or improved BeLPT. Limitations of the approach are discussed as well as options for adapting this biomarker optimization methodology to consider biomarkers of other exposure-associated diseases.