New innovational outlier unit root test with a break at an unknown time

The Perron test which is based on a Dickey–Fuller test regression is a commonly employed approach to test for a unit root in the presence of a structural break of unknown timing. In the case of an innovational outlier (IO), the Perron test tends to exhibit spurious rejections in finite samples when the break occurs under the null hypothesis. In the present paper, a new Perron-type IO unit root test is developed. It is shown in Monte Carlo experiments that the new test does not over-reject the null hypothesis. Even for the case of a level and slope break for trending data, the empirical size is near its nominal level. The test distribution equals the case of a known break date. Furthermore, the test is able to identify the true break date very accurately even for small breaks. As an application serves the Nelson–Plosser data set.     

Modelling financial satisfaction across life stages: A latent class approach

We explore the determinants of financial satisfaction using a modelling framework which allows the drivers of financial satisfaction to vary across life stages. Given that financial satisfaction is measured as an ordered variable, our modelling approach is based on a latent class ordered probit model with an ordered probit class assignment function. Our analysis of household survey data indicates that four life stages are supported by the data. Our results suggest that such flexibility is important in understanding the drivers of financial satisfaction over the life cycle since there is a substantial amount of parameter heterogeneity across the four classes.     

中国内地与香港经济周期协同性的门限性质检验——兼论两地经济周期协同性中的美国因素

运用M arkov区制转移模型,对中国内地与香港经济周期协同性的门限性质以及美国对两地经济周期协同性的影响进行分析。结果显示:一方面,两地经济周期的协同性依赖于区制状态的"门限性质",即在不同的经济周期区制内呈现出不同的协同性水平。另一方面,两地经济周期的协同程度隐含了美国作为两地之间经济冲击的传递渠道以及两地共同的外部冲击源的影响。当剥离美国经济的影响之后,两地经济周期的协同程度较为微弱。因此,促进两地经济周期的长期趋同,应科学甄别和合理利用美国经济对两地经济周期协同性的影响。     

关键事件技术及其应用——以制造商转换行为的研究为例

作为一种定性研究方法,关键事件技术特别适用于理论发展和管理有效性等研究目的。目前,该方法被广泛应用于教育、管理、零售、服务接触以及关系管理等领域进行探索性研究,其信度和效度也一再得到检验和证明。本文首先介绍了关键事件技术,并将其运用到制造商转换行为的研究之中。研究表明:价格、产品质量、物流能力、公司采购战略、服务质量是影响制造商转换的最为重要的原因。而且,不同的转换原因对制造商的满意度有不同的影响。在进一步分析的基础上,本文还得出85%的制造商转换属于复杂转换以及关系对顾客保留的影响可能与供应商所能提供的顾客价值有关等等富有意义的结论。     

Instrumental variable based variable selection for generalized linear models with endogenous covariates

We study the variable selection problem for a class of generalized linear models with endogenous covariates. Based on the instrumental variable adjustment technology and the smooth-threshold estimating equation (SEE) method, we propose an instrumental variable based variable selection procedure. The proposed variable selection method can attenuate the effect of endogeneity in covariates, and is easy for application in practice. Some theoretical results are also derived such as the consistency of the proposed variable selection procedure and the convergence rate of the resulting estimator. Further, some simulation studies and a real data analysis are conducted to evaluate the performance of the proposed method, and simulation results show that the proposed method is workable.     

从学生误译看翻译中逻辑思维转换的意义

由于所处的地理环境和文化传统不同,英语民族和汉语民族在逻辑思维方式上存在不少差异。在英汉翻译过程中,从理解原文到组织译文,英汉两种思维模式很可能会互相干扰。根深蒂固的母语思维可能影响译者对原文的理解,同时英语思维定式也会对译文的组织产生干扰。即是说,逻辑思维转换与运用的正确与否是导致误译的主要原因之一,能否在英汉两种思维模式之间顺利转换是翻译成功的重要因素。     

The victim–offender overlap in late adulthood

This study contributes to the general knowledge of the victim–offender overlap by determining whether the phenomenon exists among older adults and whether known correlates of crime and victimization explain the relationship. Cross-sectional survey data from telephone interviews conducted with individuals 60 years and older (N = 2,000) residing in Arizona and Florida are used to estimate confirmatory factor models for both victimization and criminal offending. The results from a series of multivariate regression models show that victimization is associated with criminal offending. While factors such as low self-control, depression, and spending time in commercial drinking establishments partially attenuate the victimization–crime link, the statistically significant relationship persists in a multivariate context. Further testing indicates that the observed findings are robust across measurement and modeling strategies. Coupled with prior research, the results support the argument that the victim–offender overlap exists (and is difficult to explain) over the life course.     

A copula-based Markov chain model for the analysis of binary longitudinal data

A fully parametric first-order autoregressive (AR(1)) model is proposed to analyse binary longitudinal data. By using a discretized version of a copula, the modelling approach allows one to construct separate models for the marginal response and for the dependence between adjacent responses. In particular, the transition model that is focused on discretizes the Gaussian copula in such a way that the marginal is a Bernoulli distribution. A probit link is used to take into account concomitant information in the behaviour of the underlying marginal distribution. Fixed and time-varying covariates can be included in the model. The method is simple and is a natural extension of the AR(1) model for Gaussian series. Since the approach put forward is likelihood-based, it allows interpretations and inferences to be made that are not possible with semi-parametric approaches such as those based on generalized estimating equations. Data from a study designed to reduce the exposure of children to the sun are used to illustrate the methods.     

Statistical analysis of error for fourth-order ordinary differential equation solvers

We develop an autoregressive integrated moving average (ARIMA) model to study the statistical behavior of the numerical error generated from three fourth-order ordinary differential equation solvers: Milne's method, Adams–Bashforth method and a new method that randomly switches between the Milne and Adams–Bashforth methods. With the actual error data based on three differential equations, we desire to identify an ARIMA model for each data series. Results show that some of the data series can be described by ARIMA models but others cannot. Based on the mathematical form of the numerical error, other statistical models should be investigated in the future. Finally, we assess the multivariate normality of the sample mean error generated by the switching method.     

Markov regime switching in mean and in fractional integration parameter

We propose a specific general Markov-regime switching estimation both in the long memory parameter d and the mean of a time series. We employ Viterbi algorithm that combines the Viterbi procedures in two state Markov-switching parameter estimation. It is well-known that existence of mean break and long memory in time series can be easily confused with each other in most cases. Thus, we aim at observing the deviation and interaction of mean and d estimates for different cases. A Monte Carlo experiment reveals that the finite sample performance of the proposed algorithm for a simple mixture model of Markov-switching mean and d changes with respect to the fractional integrating parameters and the mean values for the two regimes.