社会核算矩阵平衡方法研究——最小二乘交叉熵法

针对初始不平衡SAM与真实SAM关系未知的情形,本文提出了最小二乘交叉熵(LSCE)平衡法。基于最小二乘法(LS)、交叉熵法(CE)以及LSCE方法的仿真分析表明,CE与LS的相对稳健性取决于初始不平衡SAM的误差特征:当初始不平衡SAM的交易流量更接近于真实SAM时,LS较优;当初始不平衡SAM的系数矩阵更接近于真实SAM时,CE较优。LSCE方法同时考虑了SAM表流量和系数矩阵信息,故可得到精度介于LS和CE间的平衡SAM表,从而保证了平衡后SAM表的相对精度。     

社会核算矩阵及其平衡方法研究

社会核算矩阵(SAM)是国民经济核算的一种重要形式,由于其数据来源多样且数据依赖于统计时间,这就决定了必须找到一个比较科学的方法对社会核算矩阵进行数据的调整与平衡。RAS方法与交叉熵(CE)方法是应用较为广泛的两种方法。本文通过比较研究认为,RAS方法是CE方法的一个特例———最小交叉熵模型,而CE方法是RAS方法的一般化,二者在实践应用过程中的取舍取决于研究者研究的侧重点。理论研究表明,当研究者侧重研究名义流的变化时,无疑RAS方法是首选;而当我们拥有各方面的信息来源并且用于结构变化的分析时,无疑CE方法是首选。但二者在实际研究中的优度比较仍是值得研究的问题。     

随机存放点和体积下拣货路径问题研究

针对货物存放点和货物体积的不确定性,提出应用交叉熵法解决拣货车路径问题的方法,由于目标函数的复杂性,设计一种基于Monte-Carlo抽样求解路径期望距离的有效方法.为了提高标准交叉熵(CE)法的性能,设计了随分位值大小发生变化的更新Markov转移矩阵关键路径的自适应调整算法.计算结果验证了采用该方法解决此间题的鲁棒性和有效性.     

基于改进粒子群算法的SVR参数优化选择的研究基于改进粒子群算法的SVR参数优化选择的研究

支持向量回归机（SVR）模型的拟合精度和泛化能力取决于其相关参数的选取,由于在参数的选择范围内可选择的数量是无穷的,在多个参数中盲目搜索最优参数是需要极大的时间代价,并且很难逼近最优。因此提出了基于改进粒子群算法的SVR参数优化选择方法。仿真结果表明：该改进粒子群算法优化SVR参数方法可行、有效,由此得到的SVR模型具有更好的学习精度和推广能力。     

半非参数模型选择研究以及在上交所回购利率中的应用

基于模型动态稳定性的要求,本文改进了半非参数（SNP）模型的选择方法,使所选择的SNP模型既能很好地拟合真实样本又能模拟出与真实样本统计特征相近的时间序列。其次,本文得到了二维随机过程情形下赫米特展开项的理论结果。第三,实证结果表明：上交所旧质押式回购利率初始、插值后样本两组数据的最优SNP模型均为Semiparametric AR(1)-GARCH(1,1)（即11118000）模型,但是两者的系数估计值却不相同。最后,本文的实证结果表明了所提出的SNP模型选择改进方法的合理性与稳定性。     

基于SAM模型的H点设计

H点设计在汽车布置中起至关重要的作用，文章提出一种基于SAM模型的H点设计方法，对该方法的原理作详细的解析，并将设计位置与实际位置进行对比分析。结果表明：基于SAM模型的H点设计方法正确可靠。     

基于半绝对离差的发电商多阶段电能分配策略

在电力市场中,发电商需要提前对各个市场的电价进行预测以构造最优电能分配策略。现有文献一般假设发电商的收益的分布函数是连续的,但实际上这个假设未必成立。在此背景下,采用半绝对离差来度量发电商的风险,构建了发电商多阶段电能分配组合模型。发电商可以采用该模型将发电量多时段地合理分配到各市场中,在最大化期望利润同时最小化风险,从而为发电商进行多阶段电能分配与风险管理提供了新方法。最后提供的算例表明了所提方法是合理、可行的。     

经济核算类矩阵调整的JSD方法及扩展研究

现有的矩阵更新、平衡等调整方法多存在两种缺陷：一是度量新旧矩阵间差异的函数形式不对称,并非严格“距离”概念;二是要求矩阵元素非负导致使用范围受限。为改进上述不足,本文提出了包括Jensen–Shannon divergence（JSD）在内的若干基于对称距离优化的新方法,并统一进行保号、误差妥协等扩展以方便实际运用。在此基础上本文利用中国及其他28个国家的数据,对比分析了多种矩阵调整方法的实际效果,结果发现：第Ⅰ类JSD方法表现最突出且相对稳健,值得代替现在使用较广泛的RAS或交叉熵方法。     

随机存放点和体积下拣货路径问题研究

针对货物存放点和货物体积的不确定性,提出应用交叉熵法解决拣货车路径问题的方法,由于目标函数的复杂性,设计一种基于Monte—Carlo抽样求解路径期望距离的有效方法。为了提高标准交叉熵（cE）法的性能,设计了随分位值大小发生变化的更新Markov转移矩阵关键路径的自适应调整算法。计算结果验证了采用该方法解决此问题的鲁棒性和有效性。     

奇异方差矩阵的Markowitz''''s证券组合投资决策模型的最优化解法

基于Markowitz证券组合投资模型：min1/2W^tVW，s．t．W^te=1，W^tE（X）=μ0，分析方差矩阵V为一般对称矩阵时的情形，本文推广了证券组合投资模型的一个定理，并分类讨论了一般对称方差矩阵对应的证券组合投资模型的最优解，同时给出了求解最优证券组合的方法。     

Updating and Estimating a Social Accounting Matrix Using Cross Entropy Methods

The problem in estimating a social accounting matrix (SAM) for a recent year is to find an efficient and cost-effective way to incorporate and reconcile information from a variety of sources, including data from prior years. Based on information theory, the paper presents a flexible 'cross entropy' (CE) approach to estimating a consistent SAM starting from inconsistent data estimated with error, a common experience in many countries. The method represents an efficient information processing rule-using only and all information available. It allows incorporating errors in variables, inequality constraints, and prior knowledge about any part of the SAM. An example is presented, applying the CE approach to data from Mozambique, using a Monte Carlo approach to compare the CE approach to the standard RAS method and to evaluate the gains in precision from utilizing additional information.     

Variations in the value of reductions in commuting time

This paper applies the theory of consumer choice to investigate the variations in the value of a marginal reduction in commuting for motorists. Two models are developed: Model I includes leisure and goods in the utility function and Model II adds commuting time to the utility function. Model I predicts that the value of a marginal reduction in commuting time increases as income or commuting time increases and decreases as commuting time saved increases. Model II does not have clear qualitative implications. The empirical tests generally support Model I.     

基于熵权灰色组合预测模型的区域能源需求预测研究

本文在充分考虑传统GM(1,1)模型所存在缺陷的基础上,结合灰色预测模型的最新理论成果,提出了基于GM(1,1)模型,新陈代谢模型,离散DGM模型三种灰色预测模型的组合预测模型,采用熵值法来确定组合预测模型中各单项方法的权重,并以湖北省为例,对湖北省2001-2010年能源需求总量进行实证研究,结果表明基于灰色组合预测模型的预测精度明显高于三种单项预测方法的精度,证明了该模型用于区域能源需求预测的科学性和有效性,在此基础上对2011-2015年湖北省能源需求进行了预测。     

A unified entropic pricing framework of option: Using Cressie-Read family of divergences

The entropy valuation of option (Stutzer, 1996) provides a risk-neutral probability distribution (RND) as the pricing measure by minimizing the Kullback–Leibler (KL) divergence between the empirical probability distribution and its risk-neutral counterpart. This article establishes a unified entropic framework by developing a class of generalized entropy pricing models based upon Cressie-Read (CR) family of divergences. The main contributions of this study are: (1) this unified framework can readily incorporate a set of informative risk-neutral moments (RNMs) of underlying return extracted from the option market which accurately captures the characteristics of the underlying distribution; (2) the classical KL-based entropy pricing model is extended to a unified entropic pricing framework upon a family of CR divergences. For each of the proposed models under the unified framework, the optimal RND is derived by employing the dual method. Simulations show that, compared to the true price, each model of the proposed family can produce high accuracy for option pricing. Meanwhile, the pricing biases among the models are different, and we hence conduct theoretical analysis and experimental investigations to explore the driving causes.     

Bayesian measurement of productivity and efficiency in the presence of undesirable outputs: crediting electric utilities for reducing air pollution

Many studies have measured productivity change and efficiency when an undesirable output is a by-product. We flexibly treat the bad as a technology shifter of an input distance function and model a system of nonlinear equations subject to endogeneity. Theory dictates that we impose monotonicity on all inputs, outputs, and the bad. Since a Bayesian full-information likelihood approach can easily be misspecified, we utilize the Kim (J. Econometrics 107 (2002) 175) limited-information likelihood (LIL) derived by minimizing the entropy distance subject to the moment conditions from the Generalized Method of Moments (GMM) estimator. This represents an extension of the Bayesian Method of Moments approach of Zellner and Chen (Macroeconom. Dyn. 5 (2001) 673), Zellner and Tobias (Int. Econom. Rev. 42 (2001) 121), and Zellner (in: Bayesian Analysis in Econometrics and Statistics: The Zellner View and Papers, Edward Elgar, Cheltenham, 1997; J. Econometrics 83 (1998) 185) which uses entropy maximization but does not incorporate a specific likelihood. Using Bayes’ Theorem we combine traditional priors with the LIL, which has a mode at the standard multiple-equation GMM estimator, yielding a limited-information posterior distribution. We generalize the approach of Kim (J. Econometrics 107 (2002) 175) by incorporating an unknown covariance matrix in a Gibbs sampling framework and applying the methodology to nonlinear equations. This allows us to estimate shadow prices, technical efficiency, and productivity change for a panel of electric utilities, yielding results that differ substantially from those obtained using standard GMM.     

基于布莱克-斯科尔斯模型的扩张期权案例分析

本文首先介绍实物期权理论兴起的背景,在此基础上对实物期权进行分类。主要阐述了布莱克-斯科尔斯期权定价模型(简称B-S模型)。通过实例对比说明使用B-S模型对投资的扩张期权进行估价比传统的折现现金流量法更合理。     

Modelling oil price expectations: Evidence from survey data

Using Consensus Forecast survey data on WTI oil price expectations for 3- and 12-month horizons over the period November 1989 to December 2008, we find that the rational expectation hypothesis is rejected and that none of the traditional extrapolative, regressive and adaptive processes fits the data by itself. We suggest a mixed expectation model defined as a linear combination of these traditional processes, which we interpret as the aggregation of individual mixing behavior and of heterogenous groups of agents using these simple processes. This approach is consistent with the economically rational expectations theory. We show that the target oil price included in the regressive component of this model depends on the long-run marginal cost of crude oil production and on short term macroeconomic fundamentals whose effects are subject to structural changes. For the two horizons, estimation results provide evidence for our mixed expectation model incorporating this break-dependent target price.     

基于参数空间的ST-ECM模型的协整检验问题研究

在非线性平滑转移误差修正模型（ST-ECM）的协整检验中,由于存在未识别参数而使协整检验统计量构造困难,同时由于目前文献普遍使用的泰勒展开近似法并不能精确替代原始非线性模型,从而导致协整检验统计量功效较低。本文首先在遍历未识别参数的参数空间的基础上构造了ST-ECM模型协整检验的supF统计量,推导了supF统计量的极限分布并说明了其收敛性质。接着,蒙特卡洛仿真模拟结果显示,supF统计量在ST-ECM模型协整检验中具有良好的检验水平和功效,且supF统计量的功效明显优于EG统计量、F*NEC统计量和inft统计量。最后,本文对亚洲六个国家的利率期限结构预期假说进行了验证,结果表明中国、新加坡和泰国三个国家的利率期限结构预期假说成立且存在非线性调整效应,supF统计量较其他统计量具有更高的检验功效。     

Lower bound of cost efficiency measure in DEA with incomplete price information

The conventional cost efficiency model assumes that all the input prices are fixed and known exactly at each decision making unit. In practice, however, exact knowledge of prices is difficult and prices may be subject to variations over very short periods of time. In this paper, we develop a new DEA model, which can be transformed into a special case of bi-level linear program, to calculate the lower bound of CE efficiency from the pessimistic viewpoints based on the shortcomings of the existing approaches. As the input (price) cone of the pessimistic CE model tightens, the objective function converges to the traditional Farrell cost efficiency measure. Numerical examples are used to demonstrate the proposed approach and compare the results with those obtained with the existing approaches.