Detecting nonlinearity in time series by model selection criteria

This article analyzes the use of model selection criteria for detecting nonlinearity in the residuals of a linear model. Model selection criteria are applied for finding the order of the best autoregressive model fitted to the squared residuals of the linear model. If the order selected is not zero, this is considered as an indication of nonlinear behavior. The BIC and AIC criteria are compared to some popular nonlinearity tests in three Monte Carlo experiments. We conclude that the BIC model selection criterion seems to offer a promising tool for detecting nonlinearity in time series. An example is shown to illustrate the performance of the tests considered and the relationship between nonlinearity and structural changes in time series.     

Performance of information criteria for selection of Hawkes process models of financial data

We test three common information criteria (IC) for selecting the order of a Hawkes process with an intensity kernel that can be expressed as a mixture of exponential terms. These processes find application in high-frequency financial data modelling. The information criteria are Akaike’s information criterion, the Bayesian information criterion and the Hannan–Quinn criterion. Since we work with simulated data, we are able to measure the performance of model selection by the success rate of the IC in selecting the model that was used to generate the data. In particular, we are interested in the relation between correct model selection and underlying sample size. The analysis includes realistic sample sizes and parameter sets from recent literature where parameters were estimated using empirical financial intra-day data. We compare our results to theoretical predictions and similar empirical findings on the asymptotic distribution of model selection for consistent and inconsistent IC.     

营销管理：品牌整合传播(BIC)的营销模式

随着我国开放程度的进一步提高，企业以争夺市场为主导的管理模式以一种新的形式展开，其中又以品牌形象和品牌价值为核心的品牌管理为特征，企业的品牌价值在当前新的竞争时期具有更高的战略价值，品牌资源已完全转化为企业的战略性资源。如何通过加强品牌关系来提高品牌资产的价值，是新经济背景下各企业面临的重要课题。本文正是以品牌这一战略资源为依托，通过对整合营销传播(IMC)理论的研究，探索创立品牌整合传播(BIC)的营销实践模式。     

Combining exponential smoothing forecasts using Akaike weights

Simple forecast combinations such as medians and trimmed or winsorized means are known to improve the accuracy of point forecasts, and Akaike’s Information Criterion (AIC) has given rise to so-called Akaike weights, which have been used successfully to combine statistical models for inference and prediction in specialist fields, e.g., ecology and medicine. We examine combining exponential smoothing point and interval forecasts using weights derived from AIC, small-sample-corrected AIC and BIC on the M1 and M3 Competition datasets. Weighted forecast combinations perform better than forecasts selected using information criteria, in terms of both point forecast accuracy and prediction interval coverage. Simple combinations and weighted combinations do not consistently outperform one another, while simple combinations sometimes perform worse than single forecasts selected by information criteria. We find a tendency for a longer history to be associated with a better prediction interval coverage.     

动态联盟商务智能中心构建研究

论文在分析动态联盟商务决策所面临问题的基础上,论述了建立动态联盟商务智能中心的必要性和可能性,设计了动态联盟商务智能中心的组织架构,探讨了动态联盟商务智能中心的构成要素,研究了商务智能中心对动态联盟商务决策的支持作用。     

浅析利用garch模型对中国股票的拟合检验

本文选择上交所公开交易的中国医药（600056）股票进行garch模型拟合检验,通过似然比检验和赤池信息准则（AIC）、贝叶斯信息准则（BIC）进行模型选择,检验结果为garch（1,1）模型最优,用此模型得出参数估计。     

On Model Selection Curves

Many popular methods of model selection involve minimizing a penalized function of the data (such as the maximized log-likelihood or the residual sum of squares) over a set of models. The penalty in the criterion function is controlled by a penalty multiplier λ which determines the properties of the procedure. In this paper, we first review model selection criteria of the simple form “Loss + Penalty” and then propose studying such model selection criteria as functions of the penalty multiplier. This approach can be interpreted as exploring the stability of model selection criteria through what we call model selection curves. It leads to new insights into model selection and new proposals on how to select models. We use the bootstrap to enhance the basic model selection curve and develop convenient numerical and graphical summaries of the results. The methodology is illustrated on two data sets and supported by a small simulation. We show that the new methodology can outperform methods such as AIC and BIC which correspond to single points on a model selection curve.     

Akaike-type criteria and the reliability of inference: Model selection versus statistical model specification

Since the 1990s, the Akaike Information Criterion (AIC) and its various modifications/extensions, including BIC, have found wide applicability in econometrics as objective procedures that can be used to select parsimonious statistical models. The aim of this paper is to argue that these model selection procedures invariably give rise to unreliable inferences, primarily because their choice within a prespecified family of models (a) assumes away the problem of model validation, and (b) ignores the relevant error probabilities. This paper argues for a return to the original statistical model specification problem, as envisaged by Fisher (1922), where the task is understood as one of selecting a statistical model in such a way as to render the particular data a truly typical realization of the stochastic process specified by the model in question. The key to addressing this problem is to replace trading goodness-of-fit against parsimony with statistical adequacy as the sole criterion for when a fitted model accounts for the regularities in the data.     

基于Bayes理论的空间计量模型选择框架——以中国电信服务外溢性分析为例

空间计量经济学发展了一系列的空间计量模型,但在实证研究中如何根据实际问题选用最佳模型,尚无统一的筛选框架。而且既有文献中用于模型选择的检验主要针对大样本情形,但现实并不总是满足大样本的前提条件。文章结合贝叶斯理论处理有限样本的优势构建了针对空间计量经济模型进行筛选的统一框架。空间计量模型Bayes筛选框架的突出优势是在处理嵌套模型与非嵌套模型两种情形时具有逻辑一致性,对大、小样本条件均适用,而且筛选指标具有可直接计算性,易于操作。算例计算结果表明:相对其他所给模型而言,整合了网络技术联系及经济联系的空间误