Variance Decomposition Analysis for Nonlinear Economic Models1

In this paper, we propose a new method called the total variance method and algorithms to compute and analyse variance decomposition for nonlinear economic models. We provide theoretical and empirical examples to compare our method with the only existing method called generalized forecast error variance decomposition (GFEVD). We find that the results from the two methods are different when shocks are multiplicative or interacted in nonlinear models. We recommend that when working with nonlinear models researchers should use the total variance method in order to see the importance of indirect variance contributions and to quantify correctly the relative variance contribution of each structural shock.     

Variance Decomposition Analysis for Nonlinear Economic Models1

In this paper, we propose a new method called the total variance method and algorithms to compute and analyse variance decomposition for nonlinear economic models. We provide theoretical and empirical examples to compare our method with the only existing method called generalized forecast error variance decomposition (GFEVD). We find that the results from the two methods are different when shocks are multiplicative or interacted in nonlinear models. We recommend that when working with nonlinear models researchers should use the total variance method in order to see the importance of indirect variance contributions and to quantify correctly the relative variance contribution of each structural shock.     

Strong Consistency of the Maximum Likelihood Estimator in Generalized Linear and Nonlinear Mixed-Effects Models

Generalized linear and nonlinear mixed-effects models are used extensively in biomedical, social, and agricultural sciences. The statistical analysis of these models is based on the asymptotic properties of the maximum likelihood estimator. However, it is usually assumed that the maximum likelihood estimator is consistent, without providing a proof. A rigorous proof of the consistency by verifying conditions from existing results can be very difficult due to the integrated likelihood. In this paper, we present some easily verifiable conditions for the strong consistency of the maximum likelihood estimator in generalized linear and nonlinear mixed-effects models. Based on this result, we prove that the maximum likelihood estimator is consistent for some frequently used models such as mixed-effects logistic regression models and growth curve models.     

Estimated Precision for Predictions from Generalized Linear Models in Sociological Research

In this paper I present a general method forconstructing confidence intervals for predictionsfrom the generalized linear model in sociologicalresearch. I demonstrate that the method used forconstructing confidence intervals for predictions inclassical linear models is indeed a special case ofthe method for generalized linear models. I examinefour such models – the binary logit, the binaryprobit, the ordinal logit, and the Poissonregression model – to construct confidence intervalsfor predicted values in the form of probability,odds, Z score, or event count. The estimatedconfidence interval for an event prediction, whenapplied judiciously, can give the researcher usefulinformation and an estimated measure of precisionfor the prediction so that interpretation ofestimates from the generalized linear model becomeseasier.     

Three Corrected Score Tests for Generalized Linear Models with Dispersion Covariates

We develop three corrected score tests for generalized linear models with dispersion covariates, thus generalizing the results of Cordeiro , Ferrari and Paula (1993) and Cribari-Neto and Ferrari (1995) . We present, in matrix notation, general formulae for the coefficients which define the corrected statistics. The formulae only require simple operations on matrices and can be used to obtain analytically closed-form corrections for score test statistics in a variety of special generalized linear models with dispersion covariates. They also have advantages for numerical purposes since our formulae are readily computable using a language supporting numerical linear algebra. Two examples, namely, iid sampling without covariates on the mean or dispersion parameter oand one-way classification models, are given. We also present some simulations where the three corrected tests perform better than the usual score test, the likelihood ratio test and its Bartlett corrected version. Finally, we present a numerical example for a data set discussed by Simonoff and Tsai (1994) .     

Inference of Dynamic Generalized Linear Models: On-Line Computation and Appraisal

The purpose of this paper is to provide a critical discussion on real-time estimation of dynamic generalized linear models. We describe and contrast three estimation schemes, the first of which is based on conjugate analysis and linear Bayes methods, the second based on posterior mode estimation, and the third based on sequential Monte Carlo sampling methods, also known as particle filters. For the first scheme, we give a summary of inference components, such as prior/posterior and forecast densities, for the most common response distributions. Considering data of arrivals of tourists in Cyprus, we illustrate the Poisson model, providing a comparative analysis of the above three schemes.     

基于广义交叉谱密度方法的线性和非线性格兰杰因果关系检验

鉴于现行的均值格兰杰因果关系检验或者无法检验非线性的格兰杰因果关系,或者存在“维数灾难”问题,我们利用Chung 和 Hong (2007) 的广义交叉谱方法提出了一个能统一检验线性和非线性均值格兰杰因果关系的检验统计量。我们的广义交叉谱检验统计量渐近服从一个标准正态分布,它不但能考虑所有滞后阶的信息,而且避免了“维数灾难”问题。蒙特卡罗试验结果表明广义交叉谱检验具有良好的有限样本表现。     

Scaling for Residual Variance Components of Ordered Category Responses in Generalised Linear Mixed Multilevel Models

A method is proposed that enables changes in variance components to be computed from the results of fitting ordered response generalised models with multilevel and random effects. This deals with the rescaling of the response that occurs as we add new features to a developing model.     

非线性误差校正模型中的阈值协整检验——基于阈值协整向量未知的扩展

Kapetanios等(2006)假定阈值协整向量已知,在误差校正模型中使用指数函数刻画非线性调节效应,并使用F懈统计量检验非线性阈值协整.本文基于Kapetanios等(2006)的模型设定,将阈值协整向量由已知扩展为未知,并借鉴Hansen和Seo(2002)的方法估计阈值协整向量和构造F*NEC统计量检验非线性阈值协整.仿真试验表明:本文方法估计的阈值协整向量具有近似无偏、对称的分布和相对较高的精度,并且其随样本容量的变化特征符合一致性.进一步,在有限样本下,F*NEC 与FNEC的水平扭曲没有显著差异,但F*NEC的检验势高于FNEC.     

Nonlinear Granger Causality: Guidelines for Multivariate Analysis

We propose an extension of the bivariate nonparametric Diks–Panchenko Granger non‐causality test to multivariate settings. We first show that the asymptotic theory for the bivariate test fails to apply to the multivariate case, because the kernel density estimator bias and variance cannot both tend to zero at a sufficiently fast rate. To overcome this difficulty we propose to reduce the order of the bias by applying data sharpening prior to calculating the test statistic. We derive the asymptotic properties of the ‘sharpened’ test statistic and investigate its performance numerically. We conclude with an empirical application to the US grain market, using the price of futures on heating degree days as an additional conditioning variable. Copyright © 2015 John Wiley & Sons, Ltd.     

Generalized Symmetry Models for Hypercubic Concordance Tables

Frequency data obtained classifying a sample of 'units' by the same categorical variable repeatedly over 'components', can be arranged in a hypercubic concordance table (h.c.t.) . This kind of data naturally arises in a number of different areas such as longitudinal studies, studies using matched and clustered data, item-response analysis, agreement analysis. In spite of the substantial diversity of the mechanisms that can generate them, data arranged in a h.c.t. can all be analyzed via models of symmetry and quasi-symmetry, which exploit the special structure of the h.c.t. The paper extends the definition of such models to any dimension, introducing the class of generalized symmetry models , which provides a unified framework for inference on categorical data that can be represented in a h.c.t., Within this framework it is possible to derive the common structure which underlies these models and clarify their meaning; their usefulness in applied work is illustrated by a re-analysis of two real examples.     

Interpoint Distance Test of Homogeneity for Multivariate Mixture Models

Finite mixtures offer a rich class of distributions for modelling of a variety of random phenomena in numerous fields of study. Using the sample interpoint distances (IPDs), we propose the IPD‐test statistic for testing the hypothesis of homogeneity of mixture of multivariate power series distribution or multivariate normal distribution. We derive the distribution of the IPDs that are drawn from a finite mixture of the multivariate power series distribution and multivariate normal distribution. Based on the empirical distribution of the IPDs, we construct a bootstrap test of homogeneity for other multivariate finite mixture models. The IPD test is applied to mixture models for matrix‐valued distributions and a test of homogeneity for Wishart mixture is presented. Numerical comparisons show that IPD test has accurate type I errors and is more powerful in most multivariate cases than the expectation–maximization (EM) test and modified likelihood ratio test.     

多元曲线漂移模型与曲线预测

本文通过观察商业公司的销售曲线、证券市场的价格曲线等曲线族,提炼出多元非参数回归曲线漂移模型。它是一种多元自建模型,其中每一条曲线都可以建立一个多元非参数回归方程,并借助于参数与一条参考曲线联结成一个曲线族。本文研究了如何综合使用多元核函数、投影寻踪回归、交叉核实等方法来拟合这样的曲线族并进行曲线预测。     

Comparison of Sampling Schemes for Dynamic Linear Models

Hyperparameter estimation in dynamic linear models leads to inference that is not available analytically. Recently, the most common approach is through MCMC approximations. A number of sampling schemes that have been proposed in the literature are compared. They basically differ in their blocking structure. In this paper, comparison between the most common schemes is performed in terms of different efficiency criteria, including efficiency ratio and processing time. A sample of time series was simulated to reflect different relevant features such as series length and system volatility.     

Log–Linear Mixed Models for Categorical Data

A method for construction of mixed models for categorical responses is described and its use is exemplified for three experimental designs: the repeated–measurements design, the change–over design and a design used for measurement of inter–observer agreement. The method may be viewed as an extension of the standard log–linear model methodology.     

Measuring Spot Variance Spillovers when (Co)variances are Time‐varying – The Case of Multivariate GARCH Models

We propose global and disaggregated spillover indices that allow us to assess variance and covariance spillovers, locally in time and conditionally on time‐t information. Key to our approach is the vector moving average representation of the half‐vectorized ‘squared’ multivariate GARCH process of the popular BEKK model. In an empirical application to a four‐dimensional system of broad asset classes (equity, fixed income, foreign exchange and commodities), we illustrate the new spillover indices at various levels of (dis)aggregation. Moreover, we demonstrate that they are informative of the value‐at‐risk violations of portfolios composed of the considered asset classes.     

钢结构箱型柱施工费用预估模式的研究

过去钢构制作进度预估及成本估价皆为经验法,导致施工进程须依个案加以规划。本研究利用施工现场生产力数据与影响因子,希望能建置一套施工费用预估模式。研究结果发现箱型柱制作作业具有高度重复及线性循环等特性。因此,本研究以线性回归方法建置钢结构箱型柱作业生产力预估模型。由于建置施工费用预估模式能够规划有效进程、方便管控预算成本及控制施工成本,因此可供钢结构厂预估最佳工期与成本,以减少预算损失~[1]。