基于线性概率序列的报童模型模拟分析

经典的报童模型能很好地解决需求分布确定情形下对最优订购量的求解,但并不适用于需求分布不确定的情况.从实际情况出发,提出需求为线性正态分布序列的报童模型,采用线性回归的方式来确定模型参数,得出了线性正态分布序列下报童模型的解是一个随时间变化的线性序列的结论,并用计算机模拟的办法检验了此思路的可行性.     

信息与库存集成决策

企业一方面收集信息以减少需求的不确定性,另一方面采用安全库存以应对这种不确定性.如何平衡这两种决策,是企业面临的重要问题.利用报童模型来研究这一问题,其中需求的方差是决策变量.在需求是正态分布时,得到了最优决策,并分析了各个参数对最优解的影响.进一步,探讨了在决策者是风险规避时最优决策的变化情况,得出一些有益的管理启示.     

不同分布对ARCH类模型结果的影响——基于上证指数收益率的对比分析

本文应用ARCH类模型对1990～2006年的上证综指收益率序列进行分析,对t分布和正态分布下的ARCH类模型结果进行对比,发现不同的分布具有不同的结果,选择恰当的残差分布类型是正确使用ARCH类模型的前提。     

基于混合Logit模型的房地产公司信用风险预测研究

预测房地产企业信用风险对银行有效规避信贷违约风险有重要意义。文章分别在系数服从正态分布、对数正态分布和均匀分布的三种条件下，利用混合Logit模型预测了我国房地产上市公司的信用风险情况。结论表明：对于房地产上市公司的信用风险情况，混合Logit模型具有较高的识别与预测能力，三种分布下的混合Logit模型中，对数正态分布下的混合Logit模型拟合优度和综合预测准确度最好。     

基于JSU分布的广义自回归条件密度建模及应用

金融时间序列的分布对于全面、准确把握金融资产收益的动态行为具有重要的意义,而广义自回归条件密度(GARCD)建模为描述金融资产收益的概率密度函数提供了一种工具。本文在JSU分布的基础上,建立了GARCD-JSU模型,给出了模型的参数估计方法及模型拟合效果的检验方法。利用建立的GARCD-JSU模型不仅可以得到金融时间序列的时变概率密度函数,而且还可以测算出时变高阶矩的变化,从而克服了正态分布假定框架下仅从前二阶矩出发考虑金融时间序列分布特征的局限性。     

我国期货市场非线性特征的实证检验

一、问题的提出 主流的金融理论基于一种线性思维,假定收益率序列是一个I.I.D过程,并且用随机游走模型来描述价格的变动.根据中心极限定理,当样本容量趋于无穷大时,收益率会服从正态分布.传统金融理论对时间序列的计量分析都是建立在这种高斯假设之上.随着研究的深入,人们发现了许多违背线性假定的经验事实.如果收益率分布不服从正态假设,而且时间序列是非独立和非线性的,那么,我们就需要重新审视目前大部分检验方法.本文将对中国期货收益率序列进行实证分析,以验证其中是否存在非线性特征.     

论连续型需求分布下存货保险储备计算

本文在分析存货保险储备量、存储成本和缺货成本等的相互关系后，综述了现有关于最佳存货保险储备量的相关研究。研究论证了在存货的需求服从连续型随机变量分布时的最佳存货保险储备量的存在性，设计了相关的模型，给出了连续型存货需求分布下的保险储备计算公式，并在假定需求模型为正态分布时用公式进行了示例分析。     

浅析二项分布、泊松分布和正态分布之间的关系

二项分布、泊松分布和正态分布一直是学习和研究概率统计的基础。在一定条件下,这三个分布之间存在着密切关系。文章通过求极限分布,研究了二项分布与泊松分布、二项分布与正态分布之间的关系,并利用特征函数和分布函数相互唯一确定这一性质,分析了泊松分布和正态分布之间的关系。     

引入租赁的报童模型研究

针对易逝品的特点,对新形势下报童的决策进行研究,改进了报童模型,解决了报童既出售产品又出租该产品情形下的最优订货量问题,并通过算例进行了验证.     

浅析二项分布、泊松分布和正态分布之间的关系

二项分布、泊松分布和正态分布一直是学习和研究概率统计的基础.在一定条件下,这三个分布之间存在着密切关系.文章通过求极限分布,研究了二项分布与泊松分布、二项分布与正态分布之间的关系,并利用特征函数和分布函数相互唯一确定这一性质,分析了泊松分布和正态分布之间的关系.     

A multiple regime smooth transition Heterogeneous Autoregressive model for long memory and asymmetries

In this paper we propose a flexible model to describe nonlinearities and long-range dependence in time series dynamics. The new model is a multiple regime smooth transition extension of the Heterogeneous Autoregressive (HAR) model, which is specifically designed to model the behavior of the volatility inherent in financial time series. The model is able to simultaneously approximate long memory behavior, as well as describe sign and size asymmetries. A sequence of tests is developed to determine the number of regimes, and an estimation and testing procedure is presented. Monte Carlo simulations evaluate the finite-sample properties of the proposed tests and estimation procedures. We apply the model to several Dow Jones Industrial Average index stocks using transaction level data from the Trades and Quotes database that covers ten years of data. We find strong support for long memory and both sign and size asymmetries. Furthermore, the new model, when combined with the linear HAR model, is viable and flexible for purposes of forecasting volatility.     

The Holmström–Milgrom model: a simplified and illustrated version

In principal-agent theory, Holmström (1979, Bell Journal of Economics, 10, 74–91) offers the canonical model including a hidden one-shot action taken by an agent contracted to provide effort. Holmström's classical investigation led to an important body of applied literature. In investigating a specification in which effort is a sequence of actions, Holmström and Milgrom (1987, Econometrica, 55, 303–328) were able to provide a proof of the optimality of linear reward schemes (in the one-shot model, reward schemes are never linear). The sequence-of-actions model has a corresponding (static), very tractable companion. In this paper a simplified and illustrated version of this model is presented.     

Optimal prediction pools

We consider the properties of weighted linear combinations of prediction models, or linear pools, evaluated using the log predictive scoring rule. Although exactly one model has limiting posterior probability, an optimal linear combination typically includes several models with positive weights. We derive several interesting results: for example, a model with positive weight in a pool may have zero weight if some other models are deleted from that pool. The results are illustrated using S&P 500 returns with six prediction models. In this example models that are clearly inferior by the usual scoring criteria have positive weights in optimal linear pools.     

A financial planning model for presidential candidates

The necessity of entering a sequence of interrelated state primaries has forced presidential candidates to be much more deliberate in planning campaign finances. This paper presents a linear programming model for optimal allocation of time and money to each primary in order to maximize the number of delegates won. The model attempts to quantify and exploit the relationships between performance in early primaries and performance in later primaries, which has heretofore been labeled the “snowball effect.” Finally, the model, whose major use would be in overall strategic planning, is illustrated with an example.     

Chi-squared tests for evaluation and comparison of asset pricing models

This paper presents a general statistical framework for estimation, testing and comparison of asset pricing models using the unconstrained distance measure of Hansen and Jagannathan (1997). The limiting results cover both linear and nonlinear models that could be correctly specified or misspecified. We propose modified versions of the existing model selection tests and new pivotal specification and model comparison tests with improved finite-sample properties. In addition, we provide formal tests of multiple model comparison. The excellent size and power properties of the proposed tests are demonstrated using simulated data from linear and nonlinear asset pricing models.     

Heuristic solution procedures for a mixed-integer programming discriminant model

Mixed-integer programming models for the two-group discriminant problem appear to be more promising, in terms of accuracy, than are linear programming models, but at a substantial computational cost. This paper poses a particular mixed-integer model and suggests heuristics, based on linear programming, for obtaining suboptimal but “good” solutions to it. The heuristics are compared to the mixed-integer model using Monte Carlo simulation with Gaussian data.     

Reparameterized inverse gamma regression models with varying precision

In this article, we propose a mean linear regression model where the response variable is inverse gamma distributed using a new parameterization of this distribution that is indexed by mean and precision parameters. The main advantage of our new parametrization is the straightforward interpretation of the regression coefficients in terms of the expectation of the positive response variable, as usual in the context of generalized linear models. The variance function of the proposed model has a quadratic form. The inverse gamma distribution is a member of the exponential family of distributions and has some distributions commonly used for parametric models in survival analysis as special cases. We compare the proposed model to several alternatives and illustrate its advantages and usefulness. With a generalized linear model approach that takes advantage of exponential family properties, we discuss model estimation (by maximum likelihood), black further inferential quantities and diagnostic tools. A Monte Carlo experiment is conducted to evaluate the performances of these estimators in finite samples with a discussion of the obtained results. A real application using minerals data set collected by Department of Mines of the University of Atacama, Chile, is considered to demonstrate the practical potential of the proposed model.     

Efficient high-dimensional importance sampling

The paper describes a simple, generic and yet highly accurate efficient importance sampling (EIS) Monte Carlo (MC) procedure for the evaluation of high-dimensional numerical integrals. EIS is based upon a sequence of auxiliary weighted regressions which actually are linear under appropriate conditions. It can be used to evaluate likelihood functions and byproducts thereof, such as ML estimators, for models which depend upon unobservable variables. A dynamic stochastic volatility model and a logit panel data model with unobserved heterogeneity (random effects) in both dimensions are used to provide illustrations of EIS high numerical accuracy, even under small number of MC draws. MC simulations are used to characterize the finite sample numerical and statistical properties of EIS-based ML estimators.     

Nonparametric stochastic frontiers: A local maximum likelihood approach

This paper proposes a new approach to handle nonparametric stochastic frontier (SF) models. It is based on local maximum likelihood techniques. The model is presented as encompassing some anchorage parametric model in a nonparametric way. First, we derive asymptotic properties of the estimator for the general case (local linear approximations). Then the results are tailored to a SF model where the convoluted error term (efficiency plus noise) is the sum of a half normal and a normal random variable. The parametric anchorage model is a linear production function with a homoscedastic error term. The local approximation is linear for both the production function and the parameters of the error terms. The performance of our estimator is then established in finite samples using simulated data sets as well as with a cross-sectional data on US commercial banks. The methods appear to be robust, numerically stable and particularly useful for investigating a production process and the derived efficiency scores.     

平滑转换自回归模型的平稳性问题研究

根据时间序列宽平稳的定义,本文认为,平滑转换自回归模型的序列不是宽平稳序列,利用ADF统计量检验其平稳性是没有意义的;其次,依据马尔科夫链的遍历性,我们认为,STAR模型的序列是严平稳序列,且通过对模型系数的联合取值的限制保证了模型的平稳性。以一阶对数平滑转换自回归模型为例,其平稳的条件是,β与r符号相反,且|β+r|<1,β可以等于1,也可以绝对值小于1。