基于模糊层次分析法的购物评价效用排序模型研究

如何在海量的购物评价中筛选出效用较高的购物评价并及时展现给消费者,本文构建了基于模糊层次分析法(FAHP)的购物评价效用排序模型。以天猫平台某商品的1951条购物评价为样本,通过购物评价效用排序模型对样本进行实证研究。研究结果表明:追加评价的时间间隔,在一定范围内对评价效用具有正面影响;商品特征词中专有特征词对评价效用具有正面影响;由FAHP所构建的权重体系具有较强的稳定性。本文给企业提出的相关建议是,选取效用较高的购物评价进行整理回复,塑造企业形象;以效用较高的购物评价中反映的问题为突破口进行改进创新,拓展企业核心竞争力;适当增加对商品的专业性描述,提高商品可信度。     

灰色GM(1,1)模型在房价预测中的算法研究

本文利用灰色系统GM(1,1)模型来预测房地产价格,对模型的算法进行了调整与改进:对原始数据序列采用选取最近年份不同个数的样本数据(在序列中进行无间隔选取,即连续的)方式逐次建立相应的GM(1,1)模型,研究了选取的样本个数与误差之间的关系。通过具体算例即历年全国房屋的平均销售价格预测的验证,结果证明其误差与所选取最近年期的样本数据个数有一定的关系。可以根据所研究问题的特点适当选择样本个数,尽量减少预测的误差。     

研发支出费用化还是资本化

本文对长期研发支出各期稳定性进行了一套VAR（p）模型实证研究的设计。研究结果表明，企业研发支出非稳定性，证明了资本化和有条件资本化的合理性，并以此减少一些研发支出费用化还是资本化的争议。     

信用评估中的特征选择方法研究

特征（变量）选择是信用评估中常用的一种数据降维技术,然而传统的基于相关性的特征选择方法（CFS）在计算变量间相关系数时,本质上是一种线性分析方法,无法有效处理非线性关系的数据,导致不能准确估计变量间相关性的大小。本文在分析CFS方法的基础上,引入Gebelein最大相关系数（GMC）,提出了一种非线性相关性特征选择方法——基于Gebelein最大相关性特征选择方法（GCFS）。在此基础上,结合支持向量机（SVM）技术,构建了GCFS SVM评估模型。该模型能有效地识别变量间的非线性相关关系,更真实估计变量间相关系数大小,从而筛选出最优变量子集,最终提高模型评估预测能力。为验证本文所提方法的效果,通过对两个公开的信用数据集的实证研究,结果表明：与其他方法相比,本文提出的GCFS方法能显著改善信用评估模型预测性能,提高模型判别能力,本研究成果也为信用评估模型的构建提供了一种新的思路和有益的参考。     

中小企业信用风险模型的构建与验证

根据我国实情以及各种模型的特点,本文选择KMV模型作为度量中小企业信用风险的模型,根据筛选标准从沪深股市中选择样本企业进行研究,并确定各项变量,得出各样本公司的违约距离后,对其差异性进行比较,对实证检验结果进行分析,并阐述今后进一步研究的方向,包括改进KMV模型、中小企业信用风险影响因素建模分析、财务预警应用等。     

上市公司财务危机预警模型构建

本文以2005年发生ST的A股上市公司为研究对象,采用行业和总股本规模作为选择配对样本的标准,构建了上市公司财务危机预警模型。预警指标除选择常用的财务指标外还引入了现金流量指标,并运用费雪(Fisher)判别准则,通过实证研究建立线性判别函数,以期为预测上市公司财务危机提供参考。     

汇率预测:一个新的非参数支持向量回归方法

本文利用一个新的非参数支持向量回归(SVR)方法来预测基于非线性ARI模型的汇率时序变量,并且与最大似然法(MLE)和人工神经网络(ANN)的预测结果进行比较。从理论上讲,MLE和ANN方法仅侧重于样本内拟合,而SVR方法则同时考虑了拟合和预测,因此,其预测能力在现有方法中是最强大的。本文选择中国、韩国、印度和瑞士四种货币的日汇率来进行预测检验,实证结果支持SVR方法具有最强的预测能力。     

终极控制人、盈余管理方式与IPO业绩变化——来自不同市场板块的证据

本文首先运用应计盈余管理模型和真实盈余管理模型检验我国证券市场IPO前后盈余操纵动机的强度,并细分至终极控制人性质和资本市场两个层次,以观察盈余管理方式选择上所存在的差异,然后进一步分析了两种盈余管理方式对IPO后长期经营业绩的影响程度。实证结果表明:上市公司上市前后三年内每年均进行应计盈余管理,而每间隔一年会进行真实盈余管理;盈余管理程度和选择实施真实盈余管理意愿的强度均遵循"主板中小板创业板"的规律,且国有上市公司更倾向于采用真实盈余管理方式;在公司治理因素中,董事会持股比例和管理层持股比例是盈余管理程度和方式选择的两大重要参考因素。此外,与应计盈余管理相比,真实盈余管理对IPO后长期经营业绩的负面影响更大。     

移动设备在旅游移动信息服务中能用户接受行为实证研究

本文在理论分析和对特定游客群体（上海市大学生旅游者）进行深度访谈的基础上，构建了移动设备用户接受行为模型，通过实证调研的方法对所提出的结构方程模型进行了验证，并剖析了影响特定游客使用移动设备用于旅行的因素。结果表明，感知有用性、感知易用性、先前的旅游体验与游客满意度之间存在显著的正向影响，它们和使用意愿、使用行为等变量共同影响着游客忠诚度。最后，基于实证结果，对未来移动设备在旅游移动信息服务中的应用以及如何提升游客满意度与忠诚度提出了相关的建议。     

公司特征与社会责任报告发布--来自我国上市公司的经验证据*

文章以中国A股上市公司2009—2011年6301个样本为依据，采用二元Logistic回归模型进行分析，实证检验了影响中国上市公司社会责任报告发布的公司特征因素，结果表明，上市公司社会责任报告的发布：（1）与公司规模、重污染行业显著正相关；（2）与公司成长性、资产负债率、国有控股显著负相关；（3）与公司上市地点显著相关，与公司盈利能力没有明显的相关性。文章最后提出了一些政策建议。     

Evaluating estimation methods for ordinal data in structural equation modeling

This study examined the performance of two alternative estimation approaches in structural equation modeling for ordinal data under different levels of model misspecification, score skewness, sample size, and model size. Both approaches involve analyzing a polychoric correlation matrix as well as adjusting standard error estimates and model chi-squared, but one estimates model parameters with maximum likelihood and the other with robust weighted least-squared. Relative bias in parameter estimates and standard error estimates, Type I error rate, and empirical power of the model test, where appropriate, were evaluated through Monte Carlo simulations. These alternative approaches generally provided unbiased parameter estimates when the model was correctly specified. They also provided unbiased standard error estimates and adequate Type I error control in general unless sample size was small and the measured variables were moderately skewed. Differences between the methods in convergence problems and the evaluation criteria, especially under small sample and skewed variable conditions, were discussed.     

MODEL AVERAGING IN ECONOMICS: AN OVERVIEW

Standard practice in empirical research is based on two steps: first, researchers select a model from the space of all possible models; second, they proceed as if the selected model had generated the data. Therefore, uncertainty in the model selection step is typically ignored. Alternatively, model averaging accounts for this model uncertainty. In this paper, I review the literature on model averaging with special emphasis on its applications to economics. Finally, as an empirical illustration, I consider model averaging to examine the deterrent effect of capital punishment across states in the USA.     

中国劳务输出对进出口贸易影响的实证分析

本文采用修正的贸易引力模型,应用中国与175个国家(地区)1995~2004年的面板数据,就中国劳务输出对进出口贸易的影响进行实证分析。对样本总体的回归结果表明,我国对外劳务输出与出口之间存在互补关系,劳务输出能够带动出口增加;而劳务输出对进口没有明显影响,二者之间呈现不显著的替代关系。对样本国家(地区)按区域和收入分组的回归结果与对样本总体的回归结果基本一致:我国对大多数国家(地区)的劳务输出与出口之间存在正相关关系,特别是外派劳务最为集中的东亚和南亚,劳务输出对出口有着明显的拉动作用;在进口方面,除拉美和加勒比海国家外,对其余各组国家(地区)的劳务输出与进口之间均为负相关关系,而这种关系通常不显著。     

Likelihood inference in some finite mixture models

Parametric mixture models are commonly used in applied work, especially empirical economics, where these models are often employed to learn for example about the proportions of various types in a given population. This paper examines the inference question on the proportions (mixing probability) in a simple mixture model in the presence of nuisance parameters when sample size is large. It is well known that likelihood inference in mixture models is complicated due to (1) lack of point identification, and (2) parameters (for example, mixing probabilities) whose true value may lie on the boundary of the parameter space. These issues cause the profiled likelihood ratio (PLR) statistic to admit asymptotic limits that differ discontinuously depending on how the true density of the data approaches the regions of singularities where there is lack of point identification. This lack of uniformity in the asymptotic distribution suggests that confidence intervals based on pointwise asymptotic approximations might lead to faulty inferences. This paper examines this problem in details in a finite mixture model and provides possible fixes based on the parametric bootstrap. We examine the performance of this parametric bootstrap in Monte Carlo experiments and apply it to data from Beauty Contest experiments. We also examine small sample inferences and projection methods.     

固定交互效应面板截取模型的ECM算法

交互效应面板数据模型在社会经济问题的实证分析中具有很强的适用性,但现有研究主要集中于线性面板模型。本文将交互效应引入非线性的面板截取模型,并基于ECM算法,建立了有效估计量和识别程序。基于不同因子类型的仿真实验结果显示,ECM算法可以很好地识别面板截取样本中的非观测因子。ECM估计量具有良好的有限样本性质,与其他估计量相比具有更小的偏误和更快的收敛速度。尤其是当共同因子为低频平滑因子时,其表现最为理想。     

How to Choose a Working Model for Measuring the Statistical Evidence About a Regression Parameter

Consider using a likelihood ratio to measure the strength of statistical evidence for one hypothesis over another. Recent work has shown that when the model is correctly specified, the likelihood ratio is seldom misleading. But when the model is not, misleading evidence may be observed quite frequently. Here we consider how to choose a working regression model so that the statistical evidence is correctly represented as often as it would be under the true model. We argue that the criteria for choosing a working model should be how often it correctly represents the statistical evidence about the object of interest (regression coefficient in the true model). We see that misleading evidence about the object of interest is more likely to be observed when the working model is chosen according to other criteria (e.g., parsimony or predictive accuracy).     

Estimation Optimality of Corrected AIC and Modified Cp in Linear Regression

Model selection criteria often arise by constructing unbiased or approximately unbiased estimators of measures known as expected overall discrepancies (Linhart & Zucchini, 1986, p. 19). Such measures quantify the disparity between the true model (i.e., the model which generated the observed data) and a fitted candidate model. For linear regression with normally distributed error terms, the "corrected" Akaike information criterion and the "modified" conceptual predictive statistic have been proposed as exactly unbiased estimators of their respective target discrepancies. We expand on previous work to additionally show that these criteria achieve minimum variance within the class of unbiased estimators.     

The ZD-GARCH model: A new way to study heteroscedasticity

This paper proposes a first-order zero-drift GARCH (ZD-GARCH(1, 1)) model to study conditional heteroscedasticity and heteroscedasticity together. Unlike the classical GARCH model, the ZD-GARCH(1, 1) model is always non-stationary regardless of the sign of the Lyapunov exponent ${\gamma }_0$, but interestingly it is stable with its sample path oscillating randomly between zero and infinity over time when ${\gamma }_0=0$. Furthermore, this paper studies the generalized quasi-maximum likelihood estimator (GQMLE) of the ZD-GARCH(1, 1) model, and establishes its strong consistency and asymptotic normality. Based on the GQMLE, an estimator for ${\gamma }_0$, a $t$-test for stability, a unit root test for the absence of the drift term, and a portmanteau test for model checking are all constructed. Simulation studies are carried out to assess the finite sample performance of the proposed estimators and tests. Applications demonstrate that a stable ZD-GARCH(1, 1) model is more appropriate than a non-stationary GARCH(1, 1) model in fitting the KV-A stock returns in Francq and Zakoïan (2012).     

Cross-border mergers and acquisitions with heterogeneous firms: Technology vs. market motives

The most common form of foreign direct investment (FDI) is cross-border mergers and acquisitions (M&A). A common explanation for M&A activity identified in the industrial organization literature is that firms seek technological expertise. However, this has not been examined in the FDI literature. In this paper, I develop and estimate a model of cross-border M&A and focus on the technology seeking explanation. In particular, I develop a general equilibrium model of exporting, greenfield FDI, technology-seeking cross-border M&A, and market-seeking cross-border M&A with heterogeneous firms. The model predicts that firms from a larger country are more likely to acquire in a smaller country when M&A activity is driven by a technology-seeking motive, but the opposite is true when it is driven by a market-seeking motive. Using detailed data on worldwide M&A activity from 1985 to 2007, I find empirical evidence that cross-border M&A activity exhibits behavior consistent with this prediction.