Linear prediction of temporal aggregates under model misspecification

This paper aims to demonstrate a possible aggregation gain in predicting future aggregates under a practical assumption of model misspecification. Empirical analysis of a number of economic time series suggests that the use of the disaggregate model is not always preferred over the aggregate model in predicting future aggregates, in terms of an out-of-sample prediction root-mean-square error criterion. One possible justification of this interesting phenomena is model misspecification. In particular, if the model fitted to the disaggregate series is misspecified (i.e., not the true data generating mechanism), then the forecast made by a misspecified model is not always the most efficient. This opens up an opportunity for the aggregate model to perform better. It will be of interest to find out when the aggregate model helps. In this paper, we study a framework where the underlying disaggregate series has a periodic structure. We derive and compare the efficiency loss in linear prediction of future aggregates using the adapted disaggregate model and aggregate model. Some scenarios for aggregation gain to occur are identified. Numerical results show that the aggregate model helps over a fairly large region in the parameter space of the periodic model that we studied.     

The Cox,Ross and Rubinstein tree model which includes counterparty credit risk and funding costs

The binomial asset pricing model of Cox, Ross and Rubinstein (CRR) is extensively used for the valuation of options. The CRR model is a discrete analog of the Black–Scholes–Merton (BSM) model. The 2008 credit crisis exposed the shortcomings of the oversimplified assumptions of the BSM model. Burgard and Kjaer extended the BSM model to include adjustments such as a credit value adjustment (CVA), a debit value adjustment (DVA) and a funding value adjustment (FVA). The aim of this paper is to extend the CRR model to include CVA, DVA and FVA and to prove that this extended CRR model coincides with the model that results from discretising the Burgard and Kjaer model. Our results are numerically implemented and we also show that as the number of time-steps increase in the derived tree structure model, the model converges to the model developed by Burgard and Kjaer.     

Analysis of marginally specified semi-nonparametric models for clustered binary data

Generalized linear mixed models are widely used for analyzing clustered data. If the primary interest is in regression parameters, one can proceed alternatively, through the marginal mean model approach. In the present study, a joint model consisting of a marginal mean model and a cluster-specific conditional mean model is considered. This model is useful when both time-independent and time-dependent covariates are available. Furthermore our model is semi-parametric, as we assume a flexible, smooth semi-nonparametric density of the cluster-specific effects. This semi-nonparametric density-based approach outperforms the approach based on normality assumption with respect to some important features of 'between-cluster variation'. We employ a full likelihood-based approach and apply the Monte Carlo EM algorithm to analyze the model. A simulation study is carried out to demonstrate the consistency of the approach. Finally, we apply this to a study of long-term illness data.     

商业模式批判

批判了商业模式的热潮,认为:商业模式同企业绩效间的关系并不明确,商业模式不等于竞争优势,其本质上只是一种思想和逻辑关系,因而不具备不可复制性,而且,类似于定位理论,商业模式思想有决定论倾向。好的商业模式通常是演化而来的,并非设计出来的。     

Queensland State Impact and Projection Model: The Household Sector

The Queensland Impact and Projection model is an integrated input–output econometric model of the Queensland economy. Its purpose is to complement a conventional input–output model for analyzing economic impacts at the state level. This paper provides an overview of some of the methods used to model the household sector in an input–output framework, before describing the approach taken in the Queensland model. Some results which support the empirical performance of the model are also provided. It is demonstrated that the integrated model is a viable alternative and improvement on the conventional input–output model. The results are consistent with the static input–output model and conform to expectations about how the economy responds in real impact situations.     

基于风险中性路径概率的三叉树期权定价模型

树方法是给经典期权进行定价的非常实用的数值方法,目前最流行的是二叉树模型。三叉树定价模型作为二叉树的一个扩展,其同样是在风险中性概率的基础上给经典期权进行定价,并且可以通过MATLAB实现。相比于二叉树而言,三叉树模型的定价结果具有更好的收敛性。除此之外,用三叉树模型对影响期权价格的一些因素进行敏感性分析,可以验证该模型的合理性。     

Bayesian modification indices for IRT models

Bayesian modification indices are presented that provide information for the process of model evaluation and model modification. These indices can be used to investigate the improvement in a model if fixed parameters are re-specified as free parameters. The indices can be seen as a Bayesian analogue to the modification indices commonly used in a frequentist framework. The aim is to provide diagnostic information for multi-parameter models where the number of possible model violations and the related number of alternative models is too large to render estimation of each alternative practical. As an example, the method is applied to an item response theory (IRT) model, that is, to the two-parameter model. The method is used to investigate differential item functioning and violations of the assumption of local independence.     

基于灰色神经网络组合模型的废旧产品回收预测

制造企业对废旧产品进行回收再利用,是节约资源和保护环境的有效方式。由于在产品回收过程中存在诸多不确定性因素,如何对产品回收量进行有效预测是亟待解决的关键问题。本文针对废旧产品回收的特点,将灰色GM（1,1）模型与BP神经网络预测方法结合,构建了灰色神经网络组合预测模型对产品回收量进行预测。该组合模型兼具两种预测方法的优点,预测效果优于各独立模型。案例分析表明了该种预测方法的有效性。     

Regression density estimation using smooth adaptive Gaussian mixtures

We model a regression density flexibly so that at each value of the covariates the density is a mixture of normals with the means, variances and mixture probabilities of the components changing smoothly as a function of the covariates. The model extends the existing models in two important ways. First, the components are allowed to be heteroscedastic regressions as the standard model with homoscedastic regressions can give a poor fit to heteroscedastic data, especially when the number of covariates is large. Furthermore, we typically need fewer components, which makes it easier to interpret the model and speeds up the computation. The second main extension is to introduce a novel variable selection prior into all the components of the model. The variable selection prior acts as a self-adjusting mechanism that prevents overfitting and makes it feasible to fit flexible high-dimensional surfaces. We use Bayesian inference and Markov Chain Monte Carlo methods to estimate the model. Simulated and real examples are used to show that the full generality of our model is required to fit a large class of densities, but also that special cases of the general model are interesting models for economic data.     

A two-level electricity demand model : Evaluation of the connecticut time-of-day pricing test

A two-stage budgeting model is developed for electricity demand where comsumption in each period is treated as a different commodity. A relative household demand model is first estimated, a consistent price index for electricity is constructed, and then a total electricity consumption model is estimated. Economic procedures are derived which permit application of the model to both time-of-day price situations and also declining vlock price situatiions which result in non-linear budget sets. The model is applied to both types of situations- the data from the Connecticut time-of-day pricing test as well as data from the declining block rate situation of the prevoius year. The model is also tested in a forecasting application to time-of-day customers.     

Money,random matching and endogenous growth: A quantitative analysis

In this study, we develop a search-and-matching monetary growth model to analyze the effects of inflation on economic growth and social welfare by introducing endogenous economic growth via capital externality into a two-sector search-and-matching model. We find that the channel through which inflation affects economic growth in the search-and-matching model is different from the traditional cash-in-advance model. To facilitate the calibration, we obtain an empirical estimate of the effects of inflation on economic growth using panel regressions. In the simulation analysis, we quantitatively evaluate the welfare effect of inflation in the search-and-matching endogenous growth model and compare it to a search-and-matching exogenous growth model. We find that the welfare effect of inflation is nonlinear in the endogenous growth model whereas it is linear in the exogenous growth model. Furthermore, we find that the welfare cost of inflation under endogenous growth is up to four times as large as the welfare cost of inflation under exogenous growth.     

An application of the generalized Poisson difference distribution to the Bayesian modelling of football scores

The analysis of sports data, in particular football match outcomes, has always produced an immense interest among the statisticians. In this paper, we adopt the generalized Poisson difference distribution (GPDD) to model the goal difference of football matches. We discuss the advantages of the proposed model over the Poisson difference (PD) model, which was also used for the same purpose. The GPDD model, like the PD model, is based on the goal difference in each game that allows us to account for the correlation without explicitly modelling it. The main advantage of the GPDD model is its flexibility in the tails by considering shorter as well as longer tails than the PD distribution. We carry out the analysis in a Bayesian framework in order to incorporate external information, such as historical knowledge or data, through the prior distributions. We model both the mean and the variance of the goal difference and show that such a model performs considerably better than a model with a fixed variance. Finally, the proposed model is fitted to the 2012–2013 Italian Serie A football data, and various model diagnostics are carried out to evaluate the performance of the model.     

基于粗糙集和支持向量机的商业银行信用风险评估模型

结合粗糙集理论的属性约简和支持向量机(SVM)的分类机理,提出一种数据分类的混合算法;建立了基于此算法的商业银行信用风险评估模型。模型以粗糙集属性约简作为预处理器,删除冗余属性和冲突对象,但不损失有效信息;然后基于SVM进行分类建模和预测。实证表明,创建的模型分类性能良好,降低SVM分类过程的复杂度,一定程度上避免了训练模型的过拟合现象。通过与SVM和神经网络模型的比较,证实该方法用于信用风险评估的有效性。     

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.     

Closing the Canadian Input–Output Model: Homogeneous vs Non-homogeneous Household Sector Specifications

This paper addresses, on both theoretical and empirical fronts, the task of endogenizing the household sector in a rectangular input–output (IO) model. The formulation of Miyazawa (1976) for square models is extended to the development of an impact matrix for rectangular models. This allows for the numerous household revenue sources to be built into the model. A closed model with non-homogeneous households is developed where individuals are modelled individually and by industry. The perform ance of the non-homogeneous household sector model is compared with a model that has a homogeneous household sector. The model with the non-homogeneous household sector is more sensitive to changes in wages and salaries than to changes in final demand.     

How to Choose a Working Model for Measuring the Statistical Evidence About a Regression Parameter

Consider using a likelihood ratio to measure the strength of statistical evidence for one hypothesis over another. Recent work has shown that when the model is correctly specified, the likelihood ratio is seldom misleading. But when the model is not, misleading evidence may be observed quite frequently. Here we consider how to choose a working regression model so that the statistical evidence is correctly represented as often as it would be under the true model. We argue that the criteria for choosing a working model should be how often it correctly represents the statistical evidence about the object of interest (regression coefficient in the true model). We see that misleading evidence about the object of interest is more likely to be observed when the working model is chosen according to other criteria (e.g., parsimony or predictive accuracy).     

基于离散选择模型的推荐系统改进算法

准确地预估用户的点击率,并根据该概率对商品排序以供用户选择在推荐系统领域有着重要的意义。推荐系统中常用的因子分解机等机器学习模型一般只考虑用户选择单个商品的概率,忽略了候选商品之间的相互影响,离散选择模型则考虑将商品候选集作为整体进行考虑。提出了使用深度学习模型来改进离散选择模型,模型使用相对特征层、注意力机制等网络结构帮助深度学习模型进行不同商品间的特征比较,研究结果表明引入离散选择模型的深度学习模型表现优于梯度提升决策树、因子分解机等模型。     

The Cox–Aalen model for recurrent‐event data with a dependent terminal event

Recurrent events with a dependent terminal event arise frequently in a wide variety of fields. In this paper, we propose a new joint model to analyze these data and model the dependence between recurrent and terminal events through shared gamma frailty. Specifically, a Cox–Aalen rate frailty model is specified for the recurrent event, and an additive hazards frailty model is specified for the terminal event. An estimating equation approach is developed for the parameters in the joint model, and the asymptotic properties of the proposed estimators are established. Simulation studies demonstrate that the proposed estimators perform well with finite samples. An application to a medical cost study of chronic heart failure patients is illustrated.     

浅析OSI七层协议在互联网中的效应

OSI参考模型;OSI七层模型称为开放式系统互联参考模型OSI七层模型是一种框架性的设计方法;OSI七层模型通过七个层次化的结构模型使不同的系统、不同的网络之间实现可靠的通讯,因此其最主要的功能就是帮助不同类型的主机实现数据传输。     

A conceptual framework for product lifecycle modelling

Product lifecycle modelling is to define and represent product lifecycle data and to maintain data interdependencies. To build a complete, reusable and highly consistent product lifecycle information model, the product lifecycle is divided into five stages: requirement analysis, conceptual design, engineering design, manufacturing, and service and support. Accordingly, five stage product models (requirement analysis model, conceptual design model, engineering design model, manufacturing model, and service and support model) are discussed. To integrate all information of a product lifecycle and support networked manufacturing mode, the key elements of product lifecycle modelling are discussed and a framework of product lifecycle modelling is proposed. Further, the relationship and evolvement of product models at different stages are described. Finally, a Web-based integration framework is proposed to support interoperability of distributed product data sources.