MM Algorithm for General Mixed Multinomial Logit Models

This paper develops a new technique for estimating mixed logit models with a simple minorization–maximization (MM) algorithm. The algorithm requires minimal coding and is easy to implement for a variety of mixed logit models. Most importantly, the algorithm has a very low cost per iteration relative to current methods, producing substantial computational savings. In addition, the method is asymptotically consistent, efficient and globally convergent. Copyright © 2016 John Wiley & Sons, Ltd.     

A Bayesian Multiple Comparison Procedure for Order-Restricted Mixed Models

A Bayesian hierarchical mixed model is developed for multiple comparisons under a simple order restriction. The model facilitates inferences on the successive differences of the population means, for which we choose independent prior distributions that are mixtures of an exponential distribution and a discrete distribution with its entire mass at zero. We employ Markov Chain Monte Carlo (MCMC) techniques to obtain parameter estimates and estimates of the posterior probabilities that any two of the means are equal. The latter estimates allow one both to determine if any two means are significantly different and to test the homogeneity of all of the means. We investigate the performance of the model-based inferences with simulated data sets, focusing on parameter estimation and successive-mean comparisons using posterior probabilities. We then illustrate the utility of the model in an application based on data from a study designed to reduce lead blood concentrations in children with elevated levels. Our results show that the proposed hierarchical model can effectively unify parameter estimation, tests of hypotheses and multiple comparisons in one setting.     

Lasso for Instrumental Variable Selection: A Replication Study

Recently, Lasso methods have been applied to economic questions. In a seminal paper, Belloni et al. (Econometrica; 80 (6): 2369–2429) make use of (post‐)Lasso for instrumental variable selection in a setting where the number of instruments p is large or might even exceed the number of observations n—a situation which is prevalent in many current applications. We replicate their simulation study with the statistical package R (R Development Core Team ( 2008 )) and, moreover, analyze in more detail the importance of the choice of the penalization parameter, a crucial component in applications. Copyright © 2015 John Wiley & Sons, Ltd.     

Comparison of Classical and Bayesian Approaches for Intervention Analysis

Intervention analysis has been recently the subject of several studies, mainly because real time series present a wide variety of phenomena that are caused by external and/or unexpected events. In this work, transfer functions are used to model different forms of intervention to the mean level of a time series. This is performed in the framework of state-space models. Two canonical forms of intervention are considered: pulse and step functions. Static and dynamic explanation of the intervention effects, normal and non-normal time series, detection of intervention, and study of the effect of outliers are also discussed. The performance of the two approaches is compared in terms of point and interval estimation through Monte Carlo simulation. The methodology was applied to real time series and showed satisfactory results for the intervention models used.     

A Review and Empirical Comparison of Bayesian and Classical Approaches to Inference on Efficiency Levels in Stochastic Frontier Models with Panel Data

This paper appliesa large number of models to three previously-analyzed data sets,and compares the point estimates and confidence intervals fortechnical efficiency levels. Classical procedures include multiplecomparisons with the best, based on the fixed effects estimates;a univariate version, marginal comparisons with the best; bootstrappingof the fixed effects estimates; and maximum likelihood givena distributional assumption. Bayesian procedures include a Bayesianversion of the fixed effects model, and various Bayesian modelswith informative priors for efficiencies. We find that fixedeffects models generally perform poorly; there is a large payoffto distributional assumptions for efficiencies. We do not findmuch difference between Bayesian and classical procedures, inthe sense that the classical MLE based on a distributional assumptionfor efficiencies gives results that are rather similar to a Bayesiananalysis with the corresponding prior.     

Mixed Hidden Markov Models for Longitudinal Data: An Overview

In this paper we review statistical methods which combine hidden Markov models (HMMs) and random effects models in a longitudinal setting, leading to the class of so‐called mixed HMMs. This class of models has several interesting features. It deals with the dependence of a response variable on covariates, serial dependence, and unobserved heterogeneity in an HMM framework. It exploits the properties of HMMs, such as the relatively simple dependence structure and the efficient computational procedure, and allows one to handle a variety of real‐world time‐dependent data. We give details of the Expectation‐Maximization algorithm for computing the maximum likelihood estimates of model parameters and we illustrate the method with two real applications describing the relationship between patent counts and research and development expenditures, and between stock and market returns via the Capital Asset Pricing Model.     

Finding Bayesian Optimal Designs for Nonlinear Models: A Semidefinite Programming‐Based Approach

This paper uses semidefinite programming (SDP) to construct Bayesian optimal design for nonlinear regression models. The setup here extends the formulation of the optimal designs problem as an SDP problem from linear to nonlinear models. Gaussian quadrature formulas (GQF) are used to compute the expectation in the Bayesian design criterion, such as D‐, A‐ or E‐optimality. As an illustrative example, we demonstrate the approach using the power‐logistic model and compare results in the literature. Additionally, we investigate how the optimal design is impacted by different discretising schemes for the design space, different amounts of uncertainty in the parameter values, different choices of GQF and different prior distributions for the vector of model parameters, including normal priors with and without correlated components. Further applications to find Bayesian D‐optimal designs with two regressors for a logistic model and a two‐variable generalised linear model with a gamma distributed response are discussed, and some limitations of our approach are noted.     

混合地理加权回归模型中的空间相关性检验和参数估计研究

对混合地理加权回归模型,提出新的空间相关性检验统计量,利用三阶矩?2逼近方法导出了其检验p-值的近似计算公式,模拟结果显示该检验统计量在检测空间相关性方面具有满意的功效。为了处理数据中可能同时存在的空间相关性和空间异质性,引入一类新的混合地理加权空间滞后回归模型,模拟结果表明该估计方法具有较高的可靠性和稳健性,与全局空间滞后回归模型比较,混合地理加权空间滞后回归模型在处理空间异质性方面具有更优良的表现。     

Measure for Measure: Exact F Tests and the Mixed Models Controversy

We consider exact F tests for the hypothesis of null random factor effect in the presence of interaction under the two factor mixed models involved in the mixed models controversy. We show that under the constrained parameter ( CP ) model, even in unbalanced data situations, MSB/MSE (in the usual ANOVA notation) follows an exact F distribution when the null hypothesis holds. We also obtain an exact F test for what is generally (and erroneously) assumed to be an equivalent hypothesis under the unconstrained parameter ( UP ) model. For unbalanced data, such a corresponding test statistic does not coincide with MSB/MSAB (the test statistic advocated for balanced data cases). We compute the power of the exact test under different imbalance patterns and show that although the loss of power increases with the degree of imbalance, it still remains reasonable from a practical point of view.     

Semi- and Non-parametric Bayesian Analysis of Duration Models with Dirichlet Priors: A Survey

The object of this paper is to review the main results obtained in semi- and non-parametric Bayesian analysis of duration models. Standard nonparametric Bayesian models for independent and identically distributed observations are reviewed in line with Ferguson's pioneering papers. Recent results on the characterization of Dirichlet processes and on nonparametric treatment of censoring and of heterogeneity in the context of mixtures of Dirichlet processes are also discussed. The final section considers a Bayesian semiparametric version of the proportional hazards model.     

Whistleblowing: A Study of Managerial Differences

A whistleblowing survey was completed by a sample of upper-level, middle-level, and first-level managers. A variety of individual, organizational, communication climate, and moral perception variables related to whistleblowing were examined across managerial levels using an ANOVA analysis. Results indicate significant differences with respect to most variables examined. Recommendations are made for practitioners and future research.     

Economic Transition and Growth: A Replication

Phillips and Sul (Journal of Applied Econometrics 2009, 24 , 1153–1185) provide an algorithm to identify convergence clubs in a dynamic factor model of economic transition and growth. We provide a narrow replication of their key results, using the open source R software instead of the original GAUSS routines. We are able to exactly replicate their results on convergence clubs, corresponding point estimates and standard errors. We comment on minor differences between their reported results and their clustering algorithm. We propose simple adjustments of the original algorithm to make manual intervention unnecessary. The adjustments allow automated application of the algorithm to other data. Copyright © 2016 John Wiley & Sons, Ltd.     

公司治理结构模式的国际比较及其对我国的启示

由于各国融资制度、股权结构与要素市场的差异，形成了三种不同的公司治理结构模式，并且这三种治理模式在经济全球化背景下呈现出一定程度的趋同。我国公司治理主导目标模式的选择。应切合我国的实际国情与吻合公司治理模式国际趋同的演变趋势，定位于“基于多方监控主体并存的、以内部监控为主”的治理模式。     

Logit versus discriminant analysis: A specification test and application to corporate bankruptcies

Two of the most widely used statistical techniques for analyzing discrete economic phenomena are discriminant analysis (DA) and logit analysis. For purposes of parameter estimation, logit has been shown to be more robust than DA. However, under certain distributional assumptions both procedures yield consistent estimates and the DA estimator is asymptotically efficient. This suggests a natural Hausman specification test of these distributional assumptions by comparing the two estimators. In this paper, such a test is proposed and an empirical example involving corporate bankruptcies is provided. The finite-sample properties of the test statistic are also explored through some sampling experiments.     

Strategies for Developing an Environmentally Sustainable Supply Chain: Differences Between Manufacturing and Service Sectors

This research illuminates the debate on whether there are differences between the manufacturing and service sectors in the matter of developing a sustainable environmental supply chain. Over the past 5 years a survey has been conducted with 800 large European companies, of which half are in the manufacturing sector and half in the service sector. The hypotheses within the survey are related to strategies for developing an environmental supply chain. They were derived from a literature review and were tested by means of a chi‐square test. The survey questionnaire enabled the respondents to give some viewpoints about the hypotheses. In this way, strategies for developing the supply chain such as ISO 14001, the Eco‐Management and Audit Scheme (EMAS), Life Cycle Assessment (LCA), auditing, waste management systems, reverse logistics, environmental indicators, remanufacturing and reuse have been investigated. Results show interesting and unexpected differences between manufacturing and service sectors that can lead to further research, practical implications and even suggestions for the surveyed companies. For instance, the viewpoints of manufacturing and service industries differ over ISO 14001 and EMAS implementation in the supply chain. In addition, service industries approach the implementation of auditing, reverse logistics, reuse and remanufacturing in a way different from that of manufacturing. Other strategies are considered essential by both sectors. Copyright © 2013 John Wiley & Sons, Ltd and ERP Environment.     

随机仓库布局问题模型与算法研究

通过讨论随机条件下仓库布局问题．建立了随机仓库布局问题机会约束规划模型，并设计出基于随机模拟的禁忌搜索算法求解模型，最后利用算例来验证算法的有效性。     

Multiple Local Maxima in Restricted Likelihoods and Posterior Distributions for Mixed Linear Models

Scattered reports of multiple maxima in posterior distributions or likelihoods for mixed linear models appear throughout the literature. Less scrutinised is the restricted likelihood, which is the posterior distribution for a specific prior distribution. This paper surveys existing literature and proposes a unifying framework for understanding multiple maxima. For those problems with covariance structures that are diagonalisable in a specific sense, the restricted likelihood can be viewed as a generalised linear model with gamma errors, identity link and a prior distribution on the error variance. The generalised linear model portion of the restricted likelihood can be made to conflict with the portion of the restricted likelihood that functions like a prior distribution on the error variance, giving two local maxima in the restricted likelihood. Applying in addition an explicit conjugate prior distribution to variance parameters permits a second local maximum in the marginal posterior distribution even if the likelihood contribution has a single maximum. Moreover, reparameterisation from variance to precision can change the posterior modality; the converse also is true. Modellers should beware of these potential pitfalls when selecting prior distributions or using peak‐finding algorithms to estimate parameters.