基于微观结构视角的非经常性损益噪音研究

以证监会对非经常性损益的修订为事件,选择深市2004年度披露的非经常性损益的上市公司为研究对象,采用非经常性损益的定价误差方法以及加入虚拟变量的日内收益和隔夜收益的回归分析方法,并将非经常性损益区分为非经常性收益和非经常性损失后分组进行分析,从市场微观结构角度对非经常性收益和损失的信息噪音进行研究。研究结果表明,事件日后的非经常性损失定价误差明显降低,其信息噪音降低,而非经常性收益只有最大值组的定价误差明显降低。日间收益和隔夜收益的回归结果表明,由于非经常性收益和损失造成暂时偏离价值的价格会迅速得到修正,非经常性收益和损失的信息噪音降低。证监会对非经常性损益的修订在一定程度上降低了其信息噪音,政策实施后具有一定的效果。     

基于财务报告的内部控制缺陷影响因素研究

以2007、2008沪深两市2195家上市公司为研究样本,构建了一个关于内部控制缺陷影响因素的概念模型,使用Logit回归分析验证内部控制缺陷的影响因素。把影响内部控制缺陷的因素分为经营复杂性、会计风险和内部控制建设三个方面。在控制上市公司是否为国有和是否为ST或*ST后,发现存在内部控制缺陷的公司一般经营更加复杂,存在的会计风险更高,内部控制建设相对更不完善。     

开放式基金净值增长率被拉升了吗?——中国证券市场日历效应检验

本文旨在研究基金净值增长率在月末、季末、年末最后1个交易日是否被显著地拉升而表现出一定的日历效应.首先引入虚拟变量进行回归分析,然后讨论不同业绩组的日历末表现以及前后10个交易日的净值增长率走势.结果表明,实证支持基金日历末净值增长率异常增大的原假设.此外还发现,净值增长率在月末、季末前后10个交易日明显呈现一个由低点逐渐增大,然后回落的趋势.     

Dummy Endogenous Variables in Weakly Separable Models

In this paper, we consider the nonparametric identification and estimation of the average effect of a dummy endogenous regressor in models where the regressors are weakly but not additively separable from the error term. The model is not required to be strictly increasing in the error term, and the class of models considered includes limited dependent variable models such as discrete choice models. Conditions are established conditions under which it is possible to identify the average effect of the dummy endogenous regressor in a weakly separable model without relying on parametric functional form or distributional assumptions and without the use of large support conditions.     

Estimation of Nonlinear Models in the Presence of Measurement Error*

Techniques used in decision sciences and business research to estimate interactions between latent variables are limited in controlling for measurement error. This article uses a latent structure modeling approach that substantially controls for measurement error in nonlinear relationships. The results of this technique are compared to the results obtained applying hierarchical regression analysis and the impact of measurement error is assessed. The paper provides a unique assessment of the validity of the multi-attribute attitude model. The validity of the multiplicative rule in the model is supported.     

Correction to: Why perceived organizational and supervisory family support is important for organizations? Evidence from the field

Review of Managerial Science - Under Table 1, the explanation regarding the dummy variables that represent gender is reverse coded.     

Quantile Regression under Misspecification,with an Application to the U.S. Wage Structure

Quantile regression (QR) fits a linear model for conditional quantiles just as ordinary least squares (OLS) fits a linear model for conditional means. An attractive feature of OLS is that it gives the minimum mean‐squared error linear approximation to the conditional expectation function even when the linear model is misspecified. Empirical research using quantile regression with discrete covariates suggests that QR may have a similar property, but the exact nature of the linear approximation has remained elusive. In this paper, we show that QR minimizes a weighted mean‐squared error loss function for specification error. The weighting function is an average density of the dependent variable near the true conditional quantile. The weighted least squares interpretation of QR is used to derive an omitted variables bias formula and a partial quantile regression concept, similar to the relationship between partial regression and OLS. We also present asymptotic theory for the QR process under misspecification of the conditional quantile function. The approximation properties of QR are illustrated using wage data from the U.S. census. These results point to major changes in inequality from 1990 to 2000.     

PIECEWISE APPROXIMATION FUNCTIONS AN EDUCATIONAL NOTE*

This paper gives a survey of piecewise approximation functions. Aside from the use of dummy variables, little research has employed such functions. Piecewise functions permit the dependent variable of a functional relationship to take on different forms with respect to the independent variable on various subintervals of the domain of the independent variable. Three types of piecewise approximation functions are considered: dummy variables, piecewise linear regressions and cubic splines. The theoretical and empirical properties of such functions are discussed within the context of an illustrative example involving the increase in ridership over time on urban mass transit systems. Dummy variables take account of shifts in the structural relation in a discontinuous fashion. Piecewise regressions and cubic splines allow for structural shifts without discontinuities arising. Tests are performed to determine whether a linear, quadratic, or cubic piecewise continuous function should be employed. In addition a test is employed to see if structural change occurs in the functional relation at the shift points.     

Unemployment in a Bankruptcy Constrained Model

This paper proposes a model of labour demand based on a bankruptcy constrained firm. This implies two different regimes for the process generating labour demand: one when this bankruptcy constraint is not binding and one when it is. The same applies to unemployment. This unemployment model is applied to U.S. quarterly data, where account is being taken of the two regimes by dummy variables based on the turning points of the NBER business indicator. It appears that the variables affecting U.S. unemployment in the respective regimes are also the ones predicted by our theoretical model.     

Instrumental Variable Estimation of Nonlinear Errors‐in‐Variables Models

This paper establishes that instruments enable the identification of nonparametric regression models in the presence of measurement error by providing a closed form solution for the regression function in terms of Fourier transforms of conditional expectations of observable variables. For parametrically specified regression functions, we propose a root n consistent and asymptotically normal estimator that takes the familiar form of a generalized method of moments estimator with a plugged‐in nonparametric kernel density estimate. Both the identification and the estimation methodologies rely on Fourier analysis and on the theory of generalized functions. The finite‐sample properties of the estimator are investigated through Monte Carlo simulations.     

Goal Programming Approach for Regression Median*

This study presents a new robust estimation method that can produce a regression median hyper-plane for any data set. The robust method starts with dual variables obtained by least absolute value estimation. It then utilizes two specially designed goal programming models to obtain regression median estimators that are less sensitive to a small sample size and a skewed error distribution than least absolute value estimators. The superiority of new robust estimators over least absolute value estimators is confirmed by two illustrative data sets and a Monte Carlo simulation study.     

Target-guided algorithms for the container pre-marshalling problem

The container pre-marshalling problem (CPMP) aims to rearrange containers in a bay with the least movement effort; thus, in the final layout, containers are piled according to a predetermined order. Previous researchers, without exception, assumed that all the stacks in a bay are functionally identical. Such a classical problem setting is reexamined in this paper. Moreover, a new problem, the CPMP with a dummy stack (CPMPDS) is proposed. At terminals with transfer lanes, a bay includes a row of ordinary stacks and a dummy stack. The dummy stack is actually the bay space that is reserved for trucks. Therefore, containers can be shipped out from the bay. During the pre-marshalling process, the dummy stack temporarily stores containers as an ordinary stack. However, the dummy stack must be emptied at the end of pre-marshalling. In this paper, target-guided algorithms are proposed to handle both the classical CPMP and new CPMPDS. All the proposed algorithms guarantee termination. Experimental results in terms of the CPMP show that the proposed algorithms surpass the state-of-the-art algorithm.     

FORECAST MODIFICATION BASED UPON RESIDUAL ANALYSIS: A CASE STUDY OF CHECK VOLUME ESTIMATION

The inability to identify all causal variables in a linear regression demand model may result in serial correlation which is generally considered undesirable; but it may be possible to take advantage of such an event. This case study, based upon Chemical Bank of New York, investigates the use of simple and exponential smoothing for modifying initial estimates from a regression model by using prior forecast error patterns to obtain better forecasts. The smoothing approaches are combined with a regression model to test for improved performance in predicting daily check volumes.     

Accounting for Both Random Errors and Systematic Errors in Uncertainty Propagation Analysis of Computer Models Involving Experimental Measurements with Monte Carlo Methods

A Monte Carlo method is presented to study the effect of systematic and random errors on computer models mainly dealing with experimental data. It is a common assumption in this type of models (linear and nonlinear regression, and nonregression computer models) involving experimental measurements that the error sources are mainly random and independent with no constant background errors (systematic errors). However, from comparisons of different experimental data sources evidence is often found of significant bias or calibration errors. The uncertainty analysis approach presented in this work is based on the analysis of cumulative probability distributions for output variables of the models involved taking into account the effect of both types of errors. The probability distributions are obtained by performing Monte Carlo simulation coupled with appropriate definitions for the random and systematic errors. The main objectives are to detect the error source with stochastic dominance on the uncertainty propagation and the combined effect on output variables of the models. The results from the case studies analyzed show that the approach is able to distinguish which error type has a more significant effect on the performance of the model. Also, it was found that systematic or calibration errors, if present, cannot be neglected in uncertainty analysis of models dependent on experimental measurements such as chemical and physical properties. The approach can be used to facilitate decision making in fields related to safety factors selection, modeling, experimental data measurement, and experimental design.     

人民币汇率双向浮动宏观因素影响机制研究——基于多重政策目标协调视角的实证分析

本文通过引入结构性政策转变虚拟变量，构建了反映人民币汇率形成机制改革效应的MDV-TARCH-M模型，分析了国内外差值变量和货币政策调控指标对汇率长期和短期变动的影响，得到如下结论：首先，历次改革中，只有中间价形成机制调整对促进汇率双向浮动产生显著效果；其次，人民币汇率浮动区间扩大是我国经济结构调整、技术进步速度放缓与国际经贸关联日趋紧密的交互效应的具体表现；第三，国内利率主要调节实体经济，对汇率的影响被阻断，但过高国际利差不利于人民币升值；最后，建立在贸易生产与货币政策之上的两组变量反馈机制通过宏观经济调控准则相互关联。     

收入差距与中国城镇居民消费行为的实证研究

本文采用AIDS(Almost Ideal Demand System)模型,研究了收入差距对中国城镇居民消费行为影响的问题.根据我国城镇居民的消费热点:耐用消费品、医疗、交通、通讯、教育、娱乐、住房,并补充居民生活必需品:食品和衣着,本文将城镇居民的消费品种分为9大类、将城镇居民按收入分为7组,同时添加反映居民收入分组的虚拟变量,采用三阶段最小二乘法(3SLS)对联立方程进行估计.所建模型统计检验结果显示:模型能够有效地反映出不同收入居民间的消费行为的差异,并克服了采用分地区数据导致的估计拟合度不理想、参数显著性不足的缺点.主要研究结论是:收入差距对中国城镇居民的消费行为存在一定的影响,特别是耐用消费品,但在衣着、教育等消费上影响不大.     

交叉上市、治理环境与上市公司超额现金价值

从超额现金持有价值的角度,将治理环境和治理约束的改变引入到公司治理与公司价值的研究框架中。以1998年至2011年中国A股上市公司的非平衡面板数据为研究对象,采用Dittmar等提出的公司超额现金持有量估计方法,在回归方程中加入交叉上市的虚拟变量,对比分析A股与AB股、AH股以及发行ADR企业所处治理环境的不同对超额现金持有价值的影响。实证结果表明,中国上市公司的超额现金持有普遍存在折价现象;只在A股市场上市的企业超额现金资产的边际价值为负;既在A股市场上市同时又在B股市场或H股市场交叉上市的企业,超额现金资产的折价程度降低,且私有上市公司的折价降低程度更加明显。上述结果表明严格的治理环境和治理约束有助于改善公司治理和降低现金资产折价;中国的A股上市公司,特别是私有公司,可以通过交叉上市增加外部治理约束降低现金资产的折价程度,提升公司价值。     

A novel approach to regression analysis for the classification of quality attributes in the Kano model: an empirical test in the food and beverage industry

Since its introduction in the 1980s, Kano's two-dimensional model has become one of the most popular models with which to evaluate quality, finding a place in a wide range of industries. For decades, various approaches to regression analysis have been applied to explore asymmetric and non-linear relationships in the Kano model. Although a number of authors have questioned the use of these regression methods, there has been a lack of validity testing to evaluate their convergence with the results of the Kano questionnaire in classifying quality attributes. This study proposes a novel approach to regression analysis for the classification of quality attributes, including must-be, one-dimensional, attractive, and indifferent categories, as well as mixed-class distribution. Using popular tools and techniques for the measurement of customer satisfaction, the proposed approach is capable of simplifying the process of collecting data making it far easier to implement than the list of functional and dysfunctional questions initiated by Kano. An empirical study of a food and beverage chain showed that the proposed approach is capable of returning acceptable classification results, compared to the Kano questionnaire. A validity test indicated that the proposed approach significantly outperformed dummy variable regression and the moderated regression. In conclusion, the proposed approach provides a more practical implementation, while maintaining classification power on par with the Kano questionnaire.     

Optimal Two‐Sided Invariant Similar Tests for Instrumental Variables Regression

This paper considers tests of the parameter on an endogenous variable in an instrumental variables regression model. The focus is on determining tests that have some optimal power properties. We start by considering a model with normally distributed errors and known error covariance matrix. We consider tests that are similar and satisfy a natural rotational invariance condition. We determine a two‐sided power envelope for invariant similar tests. This allows us to assess and compare the power properties of tests such as the conditional likelihood ratio (CLR), the Lagrange multiplier, and the Anderson–Rubin tests. We find that the CLR test is quite close to being uniformly most powerful invariant among a class of two‐sided tests. The finite‐sample results of the paper are extended to the case of unknown error covariance matrix and possibly nonnormal errors via weak instrument asymptotics. Strong instrument asymptotic results also are provided because we seek tests that perform well under both weak and strong instruments.     

A quadratic interval logit model for forecasting bankruptcy

This paper proposes a quadratic interval logit model (or quadratic interval logistic regression analysis) based on a quadratic programming approach to deal with binary response variables. This model combines the advantages of logit (or logistic regression) and Tanaka's quadratic interval regression model. As a demonstration, we applied this model to forecasting corporate distress in the UK. The results show that this model can support the logit model to discriminate between groups, and it provides more information to researchers.