Client Importance and Audit Quality at the Individual Audit Partner,Office, and Firm Levels

We investigate whether audit partner level data provides a more powerful measure than office or firm level measures of client importance. We find that the likelihood of issuing a going-concern opinion (any and first-time) increases, and the absolute value of discretionary accruals decreases, in relation to the proportion of audit fees to the total audit fees received by audit partners from all their clients. We also find that the likelihood of issuing a going-concern opinion (any and first-time) increases, and the absolute value of discretionary accruals decreases, in relation to the proportion of non-audit services fees from a client to total non-audit service fees, and the proportion of total audit and non-audit service fees from a client to total fees from all their clients at the office and firm levels. Our findings provide evidence to regulators, audit clients, and stakeholders that audit partners do not succumb to pressure from economically more important clients as audit quality has a positive association with client importance.     

Determination of derived importance of a tourism destination: A comparison of indirect methods

Two types of attribute importance, stated and derived, have been studied in marketing and tourism studies using Importance-Performance Analysis (IPA). Derived importance is thought to have an advantage over stated importance in reducing survey fatigue and social desirability biases, and thus is more predictable to an outcome variable. Derived importance is also used in a Relevance-Determinance Analysis to determine higher-impact core attributes or in an Importance Grid Analysis to explore the asymmetric relationship between attributes and overall satisfaction. Since derived importance is usually estimated through statistical methods, it is necessary to determine which method is the most appropriate. However, few studies have examined the appropriateness of indirect methods. To fill this research void, this paper judges the appropriateness of three statistical methods (i.e. multiple regression, partial correlation, and simple regression) based on data collected from Savannah, GA, using a new approach by which results of derived importance were compared against diagnostic attributes in the “keep up the good work” quadrant of IPA. Diagnostic attributes are attributes with higher stated importance and higher predictability of an outcome variable. Results show that urban forests and cultural heritage are such attributes, and simple regression is the best to infer attribute importance.     

BEYOND THE STARS

It is frequent to hear in economic seminars or read in academic papers that an effect is economically significant or economically important. Yet, the economic literature is vague on what economic importance means and how it should be measured. In this paper, I show that existing measures of economic importance are flawed and misused. I derive a new metric that measures, in percentage terms, the contribution of each explanatory variable to deviations in the dependent variable, ceteris paribus. As an illustration, the method is applied to study the determinants of migration and the determinants of fertility.     

Non-linear Gaussian sovereign CDS pricing models

Prior literature indicates that quadratic models and the Black–Karasinski model are very promising for CDS pricing. This paper extends these models and the Black [J. Finance 1995, 50, 1371–1376] model for pricing sovereign CDS’s. For all 10 sovereigns in the sample quadratic models best fit CDS spreads in-sample, and a four factor quadratic model can account for the joint effects on CDS spreads of default risk, default loss risk and liquidity risk with no restriction to factors correlation. Liquidity risk appears to affect sovereign CDS spreads. However, quadratic models tend to over-fit some CDS maturities at the expense of other maturities, while the BK model is particularly immune from this tendency. The Black model seems preferable because its out-of-sample performance in the time series dimension is the best.     

一种基于预测值量测转换的卡尔曼滤波跟踪算法

在雷达目标跟踪中，系统量测信息通常在球坐标系下获得。为了采用经典卡尔曼滤波算法实现有效目标跟踪，通常采用量测转换方法将非线性量测信息转换到直角坐标系中。针对传统量测转换方法基于量测值计算转换误差统计特性而导致的估计结果有偏问题，提出了一种基于预测值的量测转换方法，并将其与卡尔曼滤波算法相结合，获得了一种基于预测值量测转换的卡尔曼滤波跟踪算法。仿真结果表明，与现有的基于量测转换的卡尔曼滤波算法相比，该算法能在不提高运算量的情况下有效改善目标跟踪效果，跟踪精度提升约20%。     

对财务会计中重要性原则的探讨

财务会计原则中的重要性原则是属于西方财务会计的一条重要原则。对于重要性原则的正确认识和运用,能够使财务会计工作中核算费用大大降低,同时使财务会计工作数据信息更加准确,对于财务会计人员来讲是非常有利的一项原则。因此,论文就现如今财务会计性原则中的重要性原则作深入论述。     

农作物面积抽样单元空间变异性及其对抽样外推效率的影响

［目的］联合遥感和传统抽样方法的空间抽样技术是进行大区域农作物面积监测的有效手段。但传统抽样要求抽样单元间相互独立，并未考虑到区域农作物空间变异性的存在对农作物面积空间抽样效率是否有影响及其影响程度如何，从而限制了农作物面积监测的效率和精度。［方法］文章以吉林省德惠市为研究区，利用变异函数分别对10种抽样单元尺度内玉米和水稻种植面积的空间变异性进行定量评价。选择3种空间抽样方案（简单随机抽样、系统抽样、分层抽样），分别计算不同空间变异程度下的样本容量（n）、抽样相对误差（Re）和总体总值估计量的变异系数（CV（Y︿））作为空间抽样效率的评价指标，定量分析空间变异性对农作物面积空间抽样效率的影响，提出适宜空间变异农作物的空间抽样优化方案。［结果］(1)玉米和水稻面积的空间变异性（反映为基台值C0+C）均随着抽样单元尺度的增加逐渐而减小，其中500m×500m和2 500m×2 500m抽样单元尺度下农作物面积的空间自相关程度相对最小。(2)相同抽样比下，农作物面积在3种抽样方法下的相对误差和变异系数均随着空间变异性的减小（抽样单元尺度增加）逐渐增大，其中玉米和水稻在分层抽样条件下的相对误差和变异系数具有相对最小值，分别限制在10%和20%以内，其次为系统抽样（Re<40%，CV（Y︿）<88%）和简单随机抽样（Re<50%，CV（Y︿）<75%）。 在抽样方法被选定的情况下，两种农作物的分层抽样相对误差变化率随着抽样比的增加迅速降低，当抽样比增至5%时，其相对误差和变异系数的变化率基本趋于稳定，继续增加抽样比来提高抽样精度的效果甚微。 在3 500m×3 500m抽样单元尺度内，按5%抽样比确定的样本容量对玉米和水稻面积进行分层抽样时，可达到95%的抽样精度（Re<5%）。 综合农作物在不同抽样单元尺度下的空间变异特征和空间抽样效率，基于2 500m×2 500m的抽样单元尺度和5%抽样比进行分层抽样的空间变异性农作物空间抽样方案是一种高精度、低成本、代表性强的空间抽样方案。［结论］该研究为区域农作物面积抽样调查方法的改善和抽样效率的提高提供技术支持和参考依据。     

职工篮球对于企业发展的重要性

随着社会经济的不断发展和现代企业制度的逐渐完善,企业管理更加趋向于人性化和科学化,重视开发职工的生产潜力和生产价值。在企业发展的过程中,职工参加篮球活动能够更好地促进企业文化的建设,提高职工对企业文化的认同感,提高职工的工作积极性和主动性,增强企业内部的凝聚力,促进企业的可持续发展。论文主要针对职工篮球对企业发展的重要意义进行探究。     

A TVM-Copula-MIDAS-GARCH model with applications to VaR-based portfolio selection

This paper develops a novel time-varying multivariate Copula-MIDAS-GARCH (TVM-Copula-MIDAS-GARCH) model with exogenous explanatory variables to model the joint distribution of returns. The model accounts for mixed frequency factors that affect the time-varying dependence structure of financial assets. Furthermore, we examine the effectiveness of the proposed model in VaR-based portfolio selection. We conduct an empirical analysis on estimating the 90%, 95%, 99% VaRs of the portfolio constituted of the Shanghai Composite Index, Shanghai SE Fund Index, and Shanghai SE Treasury Bond Index. The empirical results show that the proposed TVM-Copula-MIDAS-GARCH model is effective to investigate the nonlinear time-varying dependence among those three indices and performs better in portfolio selection.     

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.