Correction Constants for Present Values of Seasonal Cash Flows

In a recent Decision Sciences article, McMath (1990) developed the correction constants approach for eliminating the end-of-year bias in the present value of streams with subannual cash flows. A limitation of this approach is that it assumes subannual cash flows are level. In many types of businesses, subannual cash flows follow a predictable seasonal pattern and, consequently, a present value estimate based upon a level correction constant is biased. This article derives a general formula for determining correction constants for seasonal cash flow patterns, examines the direction and magnitude of the seasonal bias, and applies seasonal correction constants to a capital budgeting problem.     

A Sensitivity Analysis Approach for Some Deterministic Multi-Criteria Decision-Making Methods*

Often, data in multi-criteria decision making (MCDM) problems are imprecise and changeable. Therefore, an important step in many applications of MCDM is to perform a sensitivity analysis on the input data. This paper presents a methodology for performing a sensitivity analysis on the weights on the decision criteria and the performance values of the alternatives expressed in terms of the decision criteria. The proposed methodology is demonstrated on three widely used decision methods. These methods are the weighted sum model (WSM), the weighted product model (WPM), and the analytic hierarchy process (AHP). This paper formalizes a number of important issues on sensitivity analysis and derives some critical theoretical results. Also, a number of illustrative examples and computational experiments further illustrate the application of the proposed methodology.     

Marketing Category Forecasting: An Alternative of BVAR‐Artificial Neural Networks*

Analyzing scanner data in brand management activities presents unique difficulties due to the vast quantity of the data. Time series methods that are able to handle the volume effectively often are inappropriate due to the violation of many statistical assumptions in the data characteristics. We examine scanner data sets for three brand categories and examine properties associated with many time series forecasting methods. Many violations are found with respect to linearity, normality, autocorrelation, and heteroscedasticity. With this in mind we compare the forecasting ability of neural networks that require no assumptions to two of the more robust time series techniques. Neural networks provide similar forecasts to Bayesian vector autoregression (BVAR), and both outperform generalized autoregressive conditional herteroscedasticty (GARCH) models.     

Bridge regression: Adaptivity and group selection

In high-dimensional regression problems regularization methods have been a popular choice to address variable selection and multicollinearity. In this paper we study bridge regression that adaptively selects the penalty order from data and produces flexible solutions in various settings. We implement bridge regression based on the local linear and quadratic approximations to circumvent the nonconvex optimization problem. Our numerical study shows that the proposed bridge estimators are a robust choice in various circumstances compared to other penalized regression methods such as the ridge, lasso, and elastic net. In addition, we propose group bridge estimators that select grouped variables and study their asymptotic properties when the number of covariates increases along with the sample size. These estimators are also applied to varying-coefficient models. Numerical examples show superior performances of the proposed group bridge estimators in comparisons with other existing methods.     

中国投资者的风险偏好

 投资者如何分配家庭资产,为个体的风险偏好研究提供了重要的信息。本文采用奥尔多投资研究中心《城市投资者行为调查问卷》的调查结果,从投资者对风险资产需求这一角度,详细研究哪些因素会显著影响投资者的风险偏好,以及不同群体投资者风险偏好的异质性问题。研究结果表明：投资者的财富水平、受教育程度、健康状况、收入水平和是否抚养小孩都是影响投资者风险偏好的重要因素。同时,不同群体风险偏好的表现有差异。研究中国投资者风险偏好的异质性,可以为进一步研究投资者的金融资产决策、储蓄行为、财富积累过程以及不同群体对宏观经济政策的反应提供依据。     

企业技术效率的影响分析

 本文以上海市166家大中型企业五年的面板数据为研究对象,设定两种不同的生产函数和两种误差分布,利用随机前沿方法分别进行了技术效率的估计。我们发现不同测算方法的结果差异不大,且对企业的评价具有一致性。结果表明上海市工业企业的技术效率整体水平不高。通过研究影响企业技术效率的外生性因素,发现企业的所有制结构、规模、R&D经费支出与R&D人员投入、资本密集程度和企业的垄断程度等因素都对上海市工业企业的技术效率水平具有显著影响。基于研究结果,我们给出了提高企业技术效率的一些建议。     

统计数据项目、发布渠道与用户质量感知

 通过实施统计用户满意度调查,测量统计用户对于政府统计部门所生产各类统计数据项目的综合质量感知,及其在主要发布渠道的具体质量感知状况。基于调查数据的分析结果表明：各类统计数据项目的综合用户质量感知水平存在较为明显的差异,用户质量感知与其使用频率之间保持了一定的正相关关系;而各类统计项目在不同发布渠道的具体质量表现也不尽相同。据此可以有效确立统计数据质量管理工作的重点环节。     

基于家庭收入的保障性住房标准研究

 保障性住房是关系国计民生的重大问题,为使保障性住房充分发挥作用,其准入标准的确定尤为重要。目前我国保障性住房准入标准大都以人均收入作为参考指标,本文旨在测算并确立以家庭收入为参考指标的保障性住房准入标准。由于厦门市保障性住房在全国具有示范效应,故以厦门为例考察基于家庭收入的保障性住房准入标准。根据抽样调查的分组数据界定“中低收入组”,通过正态分布与均匀分布的转换,利用格点映射法,找出覆盖面为40%的收入上限值,即为保障性住房的准入标准。基于此标准,结合“按年人均收入限值”这一指标,将家庭组别按人口细分,划分出针对不同家庭规模的参考准入标准。     

On the Measurements of Board Composition: Poor Consistency and a Serious Mismatch of Theory and Operationalization

Over two dozen operationalizations of board composition can be identified from the empirical literature. A structural equations confirmatory factor analysis (LISREL 8.03) suggests that these operationalizations do not constitute a single construct of board independence. Instead, analyses strongly indicate three separate constructs. Common operationalizations of board composition, then, are neither tenable surrogates for one another nor are they interchangeable. Implications for empirical aggregation of studies, theory/measurement convergence, and the current corporate governance public policy debate are discussed.