Heteroskedasticity-consistent covariance matrix estimators for spatial autoregressive models

In the presence of heteroskedasticity, conventional test statistics based on the ordinary least squares (OLS) estimator lead to incorrect inference results for the linear regression model. Given that heteroskedasticity is common in cross-sectional data, the test statistics based on various forms of heteroskedasticity-consistent covariance matrices (HCCMs) have been developed in the literature. In contrast to the standard linear regression model, heteroskedasticity is a more serious problem for spatial econometric models, generally causing inconsistent extremum estimators of model coefficients. This paper investigates the finite sample properties of the heteroskedasticity-robust generalized method of moments estimator (RGMME) for a spatial econometric model with an unknown form of heteroskedasticity. In particular, it develops various HCCM-type corrections to improve the finite sample properties of the RGMME and the conventional Wald test. The Monte Carlo results indicate that the HCCM-type corrections can produce more accurate results for inference on model parameters and the impact effects estimates in small samples.     

An extended likelihood framework for modelling discretely observed credit rating transitions

The estimation of the parameters of a continuous-time Markov chain from discrete-time observations, also known as the embedding problem for Markov chains, plays in particular an important role for the modeling of credit rating transitions. This missing data problem boils down to a latent variable setting and thus, maximum likelihood estimation is usually conducted using the expectation-maximization (EM) algorithm. We illustrate that the EM algorithm is likely to get stuck in local maxima of the likelihood function in this specific problem setting and adapt a stochastic approximation simulated annealing scheme (SASEM) as well as a genetic algorithm (GA) to combat this issue. Above that, our main contribution is to extend our method GA by a rejection sampling scheme, which allows one to derive stochastic monotone maximum likelihood estimates in order to obtain proper (non-crossing) multi-year probabilities of default. We advocate the use of this procedure as direct constrained optimization (of the likelihood function) will not be numerically stable due to the large number of side conditions. Furthermore, the monotonicity constraint enables one to combine structural knowledge of the ordinality of credit ratings with real-life data into a statistical estimator, which has a stabilizing effect on far off-diagonal generator matrix elements. We illustrate our methods by Standard and Poor’s credit rating data as well as a simulation study and benchmark our novel procedure against an already existing smoothing algorithm.     

Limitations of portfolio diversification through fat tails of the return Distributions: Some empirical evidence

This study investigates the level of risk due to fat tails of the return distribution and the changes of tail fatness (TF) through portfolio diversification. TF is not eliminated through portfolio diversification, and, interestingly, the positive tail has declining fatness until a certain level is reached, while the negative tail has rising fatness. This indicates that fat tails are highly relevant to common factors on systematic risk and that the relevance of common factors is higher for the negative tail compared to the positive tail. In the portfolio diversification effect, the declining fatness of the positive tail further reduces risk, but the rising fatness of the negative tail does not contribute to this effect. The asymmetry between the fatness of the positive and negative tails in the return distribution corresponds to the asymmetry of the trade-off relationship between loss avoidance and profit sacrifice that is expected as a consequence of portfolio diversification. Investors use portfolio diversification to reduce their risk of suffering high losses, but following this strategy means sacrificing high-profit potential. Our study provides empirical confirmation for the practical limitation of portfolio diversification and explains why investors with diversified portfolios suffer high losses from market crashes. An examination of the Northeast Asian stock markets of China, Japan, Korea, and Taiwan show identical results.     

空间目标的联合矩阵束极点估计

极点作为谐振区雷达目标的最主要电磁特性，具有很大的空间目标识别前景。但是在使用频域观测数据提取目标极点时，存在虚假极点和估计精度问题。针对上述问题，首先通过加窗和截断改进了后时响应信号的获取流程，抑制了虚假极点影响；然后采用最小描述长度法（MDL）估计极点个数，避免了定阶错误引起的虚假极点；最后采用联合矩阵束法估计极点，有效减少了单角度数据估计极点时存在的遗漏极点和虚假极点问题。FEKO软件验证表明，所提算法具有更高的精度和抗噪能力。     

Investor attention,firm-specific characteristic,and momentum: A case of the Korean stock market

This study examines the sources of negative momentum profits by combining investor attention and the properties of common and firm-specific factors. We choose the Korean stock market as a good case to characterize the negative momentum profits identified in Asia. In both portfolio and stock analyses, a method is devised to generate return data involving the property of each common and firm-specific factor within stock groups by investor attention. This study found significant negative momentum profits within the stock group with high investor attention. This momentum effect is highly dependent on the reversed performance of the past loser portfolio, not the continued performance of the past winner portfolio, and this reversal is strongly attributable to the properties of firm-specific factors, and not those of common factors. These results are robustly consistent regardless of changes in empirical design and the consideration of influence factors, market dynamics, and other stock markets.     

基于风险矩阵的企业采购风险评估

采购是企业经营的一个核心环节,是获取利润的重要来源。文章以企业采购中存在的风险为研究对象,以采购风险评价的一般分析为基础。将风险矩阵引人到企业采购的风险评估中,对企业采购进行风险评估,为企业采购提供一种结构性风险评估方法,并以某电力建设企业采购为例对风险矩阵的应用进行了说明。     

基于线性规划模型的河流水环境容量分配研究

以北运河为例,基于水动力及水质模型和年平均流量及排污数据,选取污染物中所占比重较大的化学需氧量(COD)和氨氮(NH_3—N)两个污染物因子,计算得到了北运河各支流及点源与控制断面之间的响应矩阵,在此基础上,采用线性规划模型对各点源或支流的水环境容量进行了优化分配。结果表明:北运河干流各点源及支流分配容量均为负值,需要采取措施进行削减,其中COD的平均削减率在30%~50%之间,而NH_3—N的平均削减率在40%~70%之间。在河流水环境容量分配问题的研究中,线性规划模型具有较好的适用性,是一种有效的优化方法。     

Pasternak地基上基坑开挖对邻近地下管线影响的解析分析

随着基建规模的扩大,基坑开挖引起周边地下管线变形问题仍需重点关注。首先计算基坑开挖对邻近管线产生的附加应力,然后基于Pasternak弹性地基梁模型,采用两阶段方法建立地下管线竖向位移平衡微分方程,利用有限差分法求解得到形式简洁的矩阵解析解,并把理论计算结果与两个工程实例监测数据进行比较。结果表明:本文计算结果与现场实测的地下管线变形规律基本一致,验证了该方法的合理性和准确性。计算模型概念简单,结果简洁明确,可作为分析和预测基坑开挖对邻近地下管线影响的一种新手段。     

非饱和泥岩土水特征曲线试验及数学模型研究

非饱和土对水分存在吸力,严重影响土体的工程特性。以一高速铁路地基泥岩为研究对象,应用滤纸法研究含水率及压实作用对其吸力的影响。研究结果表明:随着含水率的增加,基质吸力及总吸力呈非线性减小,减小过程分为骤减阶段、速率减小阶段和吸力稳定阶段;在低含水率情况下,土体中水分以气态形式进行迁移,此时接触滤纸量测的吸力为总吸力;接触滤纸的平衡含水率随着土体含水率的增加呈线性增加;干密度对低饱和度下土样的总吸力影响较大,同一饱和度下土样干密度越大,则基质吸力越大;在单对数坐标中,土水特征曲线近似呈"S"形,选用典型的SWCC模型对试验数据进行了拟合,拟合效果良好。试验结果可用于该类土体相关工程特性的预测及建模。     

滑坡滑带土非饱和蠕变特性模型研究

绝大多数滑坡的失稳过程实质为非饱和蠕变过程。在Singh-Mitchell研究成果的基础上,研究了低应力水平下蠕变变形与时间的关系,将蠕变过程划分为衰减蠕变、等速蠕变和加速蠕变3个阶段,并且将基质吸力作为一种新的应力变量反映到模型中,从而建立能够反映应力-吸力-应变-时间的非饱和蠕变模型。以三峡库区千将坪滑坡滑带土为研究对象,根据非饱和三轴蠕变试验数据对模型参数进行求解,并根据参数λ1、λ2与应力水平的关系,参数B、βDR与时间关系对模型进行修正,推导了适用于非饱和土的非线性蠕变模型。采用试验数据对模型进行验证,发现该模型能够较合理地预测滑坡滑带土的蠕变特性。