Description of purchase incidence by multivariate heterogeneous Poisson processes

In this paper the application of bivariate Poisson heterogeneous models to budget data is studied. This study was motivated from inconsistencies that we encountered when univariate Poisson based models were applied to cumulative data sets. Application of a multivariate Poisson based model is a possible solution to this problem. In this paper we will study the feasibility of estimators based on these models.     

Approximate inference in multivariate nonlinear functional relationships

An estimation procedure based on estimating equations is presented for the parameters in a multivariate functional relationship model, where all observations are subject to error. The covariance matrix of the observational errors may be parametrized and is allowed to be different for different sets of observations. Estimators are defined for the unknown relation parameters and error parameters.
For linear models (i.e. where the model function is linear in the incidental parameters) the estimators are consistent and asymptotically normal. A consistent expression for the covariance matrix of the estimators is derived. The results are valid for general error distributions.
For nonlinear models the estimators are based on locally linear approximations to the model function. The afore mentioned properties of the estimators are now only approximately valid. The adequacy of the approximate inference, based on asymptotic theory for the linearized model, needs at least informal check. Some examples are given to illustrate the estimation procedure.     

Seemingly Unrelated Negative Binomial Regression

This paper discusses the specification and estimation of seemingly unrelated multivariate count data models. A new model with negative binomial marginals is proposed. In contrast to a previous model based on the multivariate Poisson distribution, the new model allows for over-dispersion, a phenomenon that is frequently encountered in economic count data. Semi-parametric estimation is possible if some of the assumption of the fully specified model are violated.     

The generality/specificity issue in consumer innovativeness research

The purpose of the present study was to explore the role of personality in shaping consumer innovativeness by testing a model of the hierarchical relationships between a global (broad or abstract) personality trait, its domain-specific manifestation in a consumer context, and overt consumer behavior. A survey of 465 adult consumers measured global innovativeness, domain-specific innovativeness for two product categories (clothing and electronics) and self-reported purchase of new clothing and electronic items. Three hypotheses were tested. First, global innovativeness is more highly correlated with domain-specific innovativeness than it is with the purchase of new items. Second, domain-specific innovativeness is more highly correlated with the purchase of new items than is global innovativeness. Finally, the association between global innovativeness and new product purchase is mediated by domain-specific innovativeness. All three hypotheses were supported for both product categories.     

The Three Basic Types of Residuals for a Linear Model

We consider residuals for the linear model with a general covariance structure. In contrast to the situation where observations are independent there are several alternative definitions. We draw attention to three quite distinct types of residuals: the marginal residuals, the model‐specified residuals and the full‐conditional residuals. We adopt a very broad perspective including linear mixed models, time series and smoothers as well as models for spatial and multivariate data. We concentrate on defining these different residual types and discussing their interrelationships. The full‐conditional residuals are seen to play several important roles.     

Bayesian Inference in Spatial Sample Selection Models

In this study, we consider Bayesian methods for the estimation of a sample selection model with spatially correlated disturbance terms. We design a set of Markov chain Monte Carlo algorithms based on the method of data augmentation. The natural parameterization for the covariance structure of our model involves an unidentified parameter that complicates posterior analysis. The unidentified parameter – the variance of the disturbance term in the selection equation – is handled in different ways in these algorithms to achieve identification for other parameters. The Bayesian estimator based on these algorithms can account for the selection bias and the full covariance structure implied by the spatial correlation. We illustrate the implementation of these algorithms through a simulation study and an empirical application.     

Multivariate rotated ARCH models

This paper introduces a new class of multivariate volatility models which is easy to estimate using covariance targeting, even with rich dynamics. We call them rotated ARCH (RARCH) models. The basic structure is to rotate the returns and then to fit them using a BEKK-type parameterization of the time-varying covariance whose long-run covariance is the identity matrix. This yields the rotated BEKK (RBEKK) model. The extension to DCC-type parameterizations is given, introducing the rotated DCC (RDCC) model. Inference for these models is computationally attractive, and the asymptotics are standard. The techniques are illustrated using data on the DJIA stocks.     

Multivariate choices and identification of social interactions

This paper considers the identification of social interaction effects in the context of multivariate choices. First, we generalize the theoretical social interaction model to allow individuals to make interdependent choices in different activities. Based on the theoretical model, we propose a simultaneous equation network model and discuss the identification of social interaction effects in the econometric model. We also provide an empirical example to show the empirical salience of this model. Using the Add Health data, we find that a student's academic performance is not only affected by academic performance of his peers but also affected by screen‐related activities of his peers.     

Investigating the impact of excess zeros on hurdle‐generalized Poisson regression model with right censored count data

Typically, a Poisson model is assumed for count data. In many cases, there are many zeros in the dependent variable, thus the mean is not equal to the variance value of the dependent variable. Therefore, Poisson model is not suitable anymore for this kind of data because of too many zeros. Thus, we suggest using a hurdle‐generalized Poisson regression model. Furthermore, the response variable in such cases is censored for some values because of some big values. A censored hurdle‐generalized Poisson regression model is introduced on count data with many zeros in this paper. The estimation of regression parameters using the maximum likelihood method is discussed and the goodness‐of‐fit for the regression model is examined. An example and a simulation will be used to illustrate the effects of right censoring on the parameter estimation and their standard errors.     

REALIZED BETA GARCH: A MULTIVARIATE GARCH MODEL WITH REALIZED MEASURES OF VOLATILITY

We introduce a multivariate generalized autoregressive conditional heteroskedasticity (GARCH) model that incorporates realized measures of variances and covariances. Realized measures extract information about the current levels of volatilities and correlations from high‐frequency data, which is particularly useful for modeling financial returns during periods of rapid changes in the underlying covariance structure. When applied to market returns in conjunction with returns on an individual asset, the model yields a dynamic model specification of the conditional regression coefficient that is known as the beta. We apply the model to a large set of assets and find the conditional betas to be far more variable than usually found with rolling‐window regressions based exclusively on daily returns. In the empirical part of the paper, we examine the cross‐sectional as well as the time variation of the conditional beta series during the financial crises. Copyright © 2014 John Wiley & Sons, Ltd.     

Analysis and short-term predictions of non-technical loss of electric power based on mixed effects models

In this paper we estimate, analyze and predict short-term non-technical loss (NTL) of electric power of Brazilian energy service companies based on different assumptions for the covariance structure of the errors and controlling for socio-economic confounding variables. Although the correlation among repeated responses is not usually of intrinsic interest, it is an important aspect of the data that must properly be accounted for to produce valid inferences in longitudinal or panel data analysis. In the extended linear mixed effects model, the covariance matrix of the response vector is comprised by two subcomponents, a random effect component that can represent between group variation and a intraclass or within group component. So, in order to adequately treat the longitudinal character of NTL data, we use the decomposition of these variance components to evaluate different architectures to the within group errors. Using data of 59 Brazilian distributing utilities from 2004 to 2012, we fit a conditionally independent errors model and three other models with autoregressive-moving average parametrization to the intraclass disturbances. Finally, we compare models using the MAD and MAPE metrics in the prediction of NTL for the year of 2013. The findings suggest that the approach can be satisfactorily implemented in future statistical analysis of NTL.     

On the existence of maximum likelihood estimates in random effects models for clustered multivariate binary data

We consider a class of random effects models for clustered multivariate binary data based on the threshold crossing technique of a latent random vector. Components of this latent vector are assumed to have a Laird–Ware structure. However, in place of their Gaussian assumptions, any specified class of multivariate distribution is allowed for the random effects, and the error vector is allowed to have any strictly positive pdf. A well known member of this class of models is the multivariate probit model with random effects. We investigate sufficient and necessary conditions for the existence of maximum likelihood estimates for the location and the association parameters. Implications of our results are illustrated through some hypothetical examples.     

Poisson Regression for Clustered Data

We compare five methods for parameter estimation of a Poisson regression model for clustered data: (1) ordinary (naive) Poisson regression (OP), which ignores intracluster correlation, (2) Poisson regression with fixed cluster‐specific intercepts (FI), (3) a generalized estimating equations (GEE) approach with an equi‐correlation matrix, (4) an exact generalized estimating equations (EGEE) approach with an exact covariance matrix, and (5) maximum likelihood (ML). Special attention is given to the simplest case of the Poisson regression with a cluster‐specific intercept random when the asymptotic covariance matrix is obtained in closed form. We prove that methods 1–5, except GEE, produce the same estimates of slope coefficients for balanced data (an equal number of observations in each cluster and the same vectors of covariates). All five methods lead to consistent estimates of slopes but have different efficiency for unbalanced data design. It is shown that the FI approach can be derived as a limiting case of maximum likelihood when the cluster variance increases to infinity. Exact asymptotic covariance matrices are derived for each method. In terms of asymptotic efficiency, the methods split into two groups: OP & GEE and EGEE & FI & ML. Thus, contrary to the existing practice, there is no advantage in using GEE because it is substantially outperformed by EGEE and FI. In particular, EGEE does not require integration and is easy to compute with the asymptotic variances of the slope estimates close to those of the ML.     

Heteroskedasticity-consistent covariance matrix estimators for spatial autoregressive models

In the presence of heteroskedasticity, conventional test statistics based on the ordinary least squares (OLS) estimator lead to incorrect inference results for the linear regression model. Given that heteroskedasticity is common in cross-sectional data, the test statistics based on various forms of heteroskedasticity-consistent covariance matrices (HCCMs) have been developed in the literature. In contrast to the standard linear regression model, heteroskedasticity is a more serious problem for spatial econometric models, generally causing inconsistent extremum estimators of model coefficients. This paper investigates the finite sample properties of the heteroskedasticity-robust generalized method of moments estimator (RGMME) for a spatial econometric model with an unknown form of heteroskedasticity. In particular, it develops various HCCM-type corrections to improve the finite sample properties of the RGMME and the conventional Wald test. The Monte Carlo results indicate that the HCCM-type corrections can produce more accurate results for inference on model parameters and the impact effects estimates in small samples.     

财政支出结构对经济增长的外溢性研究——基于中国省际面板数据的实证检验

研究中国财政支出结构对经济增长的外溢效应,对于促进中国经济的长期平稳较快发展具有重要的理论和现实意义.本文基于一个三部门模型,从财政支出结构与经济区域相结合的角度,运用中国1997～ 2010年的省际面板数据进行实证研究.研究结果表明:(1)中国财政支出整体具有显著的外溢效应,其中购买性支出对经济增长具有正外溢效应,而转移性支出对经济增长具有负外溢效应;(2)区域发展水平对财政支出结构外溢效应的影响有所不同;(3)整体上看,购买性支出部门不具备要素生产率方面的优势,而转移性支出部门的生产效率优势较为明显.     

Comparisons of Bayesian estimation procedures for two-way contingency tables without interaction

Bayesian and empirical Bayesian estimation methods are reviewed and proposed for the row and column parameters in two-way Contingency tables without interaction. Rasch's multiplicative Poisson model for misreadings is discussed in an example. The case is treated where assumptions of exchangeability are reasonable a priori for the unknown parameters. Two different types of prior distributions are compared, It appears that gamma priors yield more tractable results than lognormal priors.     

贝叶斯非线性分层模型在多元索赔准备金评估中的应用

本文将贝叶斯非线性分层模型应用于基于不同业务线的多元索赔准备金评估中,设计了一种合适的模型结构,将非线性分层模型与贝叶斯方法结合起来,应用WinBUGS软件对精算实务中经典流量三角形数据进行建模分析,并使用MCMC方法得到了索赔准备金完整的预测分布。这种方法扩展并超越了已有多元评估方法中最佳估计和预测均方误差估计的研究范畴。在贝叶斯框架下结合后验分布实施推断对非寿险公司偿付能力监管和行业决策具有重要作用。     

Exact Skewness–Kurtosis Tests for Multivariate Normality and Goodness‐of‐Fit in Multivariate Regressions with Application to Asset Pricing Models*

We study the problem of testing the error distribution in a multivariate linear regression (MLR) model. The tests are functions of appropriately standardized multivariate least squares residuals whose distribution is invariant to the unknown cross‐equation error covariance matrix. Empirical multivariate skewness and kurtosis criteria are then compared with a simulation‐based estimate of their expected value under the hypothesized distribution. Special cases considered include testing multivariate normal and stable error distributions. In the Gaussian case, finite‐sample versions of the standard multivariate skewness and kurtosis tests are derived. To do this, we exploit simple, double and multi‐stage Monte Carlo test methods. For non‐Gaussian distribution families involving nuisance parameters, confidence sets are derived for the nuisance parameters and the error distribution. The tests are applied to an asset pricing model with observable risk‐free rates, using monthly returns on New York Stock Exchange (NYSE) portfolios over 5‐year subperiods from 1926 to 1995.     

The specification of least informative error distributions

Maximum likelihood estimation and most statistical tests require a full specification of the error distribution in a model. Under suitable parametric restrictions we can derive least informative specifications. The autoregressive processes prove to be least informative under a few simple variance and covariance restrictions. For the singular multivariate error distribution in a sum-constrained model, several least informative error distributions are obtained using different parametric assumptions on the covariance structure. A combined maximum entropy — maximum likelihood approach provides an alternative to other recent proposals for covariance estimation in small samples.     

Understanding relationship quality and online purchase intention in e-tourism: A qualitative application

This study explores the formation of relationship quality and online purchase intention in the service context of tourism, and a model is also introduced. This study tries to conceptualize a model based on a mediating mechanism of relationship quality that may be applied to understand online purchase intention in tourism. Learning the critical role of relationship quality in e-tourism can guide tour service providers to design different strategies for different online customers, and consequently achieve high customer online purchase to tourist service and product. In the proposed model of this study, the perceived incentive, perceived service quality, perceived ease of purchasing and perceived usefulness influence customer online purchase intention indirectly via the mediation of relationship quality. Additionally, five propositions are derived accordingly based on the proposed model and literature review. Finally, discussion, limitation and future research are also provided.