Parametric bootstrap inferences for the growth curve models with intraclass correlation structure

This paper presents parametric bootstrap (PB) approaches for hypothesis testing and interval estimation of the fixed effects and the variance component in the growth curve models with intraclass correlation structure. The PB pivot variables are proposed based on the sufficient statistics of the parameters. Some simulation results are presented to compare the performance of the proposed approaches with the generalized inferences. Our studies show that the PB approaches perform satisfactorily for various cell sizes and parameter configurations, and tends to outperform the generalized inferences with respect to the coverage probabilities and powers. The PB approaches not only have almost exact coverage probabilities and Type I error rates, but also have the shorter expected lengths and the higher powers. Furthermore, the PB procedure can be simply carried out by a few simulation steps. Finally, the proposed approaches are illustrated by using a real data example.     

Identification-robust moment-based tests for Markov switching in autoregressive models

ABSTRACT

This paper develops tests of the null hypothesis of linearity in the context of autoregressive models with Markov-switching means and variances. These tests are robust to the identification failures that plague conventional likelihood-based inference methods. The approach exploits the moments of normal mixtures implied by the regime-switching process and uses Monte Carlo test techniques to deal with the presence of an autoregressive component in the model specification. The proposed tests have very respectable power in comparison with the optimal tests for Markov-switching parameters of Carrasco et al. (2014 Carrasco, M., Hu, L., Ploberger, W. (2014). Optimal test for Markov switching parameters. Econometrica 82(2):765–784.[Crossref], [Web of Science ®] , [Google Scholar]), and they are also quite attractive owing to their computational simplicity. The new tests are illustrated with an empirical application to an autoregressive model of USA output growth.     

中国蔬菜价格波动与通货膨胀——基于波动来源的分解

利用Census X12季节调整和H-P滤波法,将蔬菜价格波动来源分解为趋势变动、季节变动、循环变动和不规则变动要素。利用Bootstrap因果检验与VAR模型,考察了蔬菜价格波动来源的分解因素与中国通货膨胀的关联性。结果表明,蔬菜价格波动影响消费者物价指数的主要渠道是通过季节变动和不规则变动要素;季节变动因素对消费者物价指数的影响呈季节周期性;不规则变动对消费者物价指数的冲击在最初时最显著,随后逐渐减弱。政策含义为,降低公众的通胀预期、促进蔬菜跨区域流通、控制蔬菜运输的物流成本、健全政府灾害天气应急响应机制均有利于减缓CPI上涨。     

应用范成法加工对称凸轮轮廓的研究

对称凸轮轮廓的加工方法有多种,但各有不足,为了克服原有的加工方法的缺陷,提出了范成法加工对称凸轮轮廓曲线的方法,并阐明了范成法加工对称凸轮轮廓曲线的原理及实现方法.利用范成法加工对称凸轮,不但解决了其他加工方法加工接拍长、加工成本高甚至无法加工的问题,而且加工精度高,操作简单可靠,可广泛用于各种批量对称凸轮的加工生产.     

英语中名词性间接回指的认知语义解析

根据兰盖克的认知语法,英语中名词性间接回指的语义本质是基体—侧重有机结合体。名词性间接回指含有两个基本构成元素,即先行项和名词性回指项,但二者不共指。回指项的语义预设了先行项的意义,先行项的语义就成为名词性间接回指的基体。分析不同类型的名词性间接回指的语义特征,并指出名词性间接回指的语义是存在于认知主体大脑中的心理实体,它必然受到认知主体的认知域的限制,所以名词性间接回指的解读存在一定的个体差异性。     

Bootstrapping Malmquist Indices for Danish Seiners in the North Sea and Skagerrak

In connection with assessing how an ongoing development in fisheries management may change fishing activity, evaluation of Total Factor Productivity (TFP) change over a period, including efficiency, scale and technology changes, is an important tool. The Malmquist index, based on distance functions evaluated with Data Envelopment Analysis (DEA), is often employed to estimate TFP changes. DEA is generally gaining attention for evaluating efficiency and capacity in fisheries. One main criticism of DEA is that it does not have any statistical foundation, i.e. that it is not possible to make inference about DEA scores or related parameters. The bootstrap method for estimating confidence intervals of deterministic parameters can however be applied to estimate confidence intervals for DEA scores. This method is applied in the present paper for assessing TFP changes between 1987 and 1999 for the fleet of Danish seiners operating in the North Sea and the Skagerrak.     

Contemporary American stepparenthood: Integrating cohabiting and nonresident stepparents

Our understanding of contemporary stepfamily life requires incorporating emerging trends in cohabitation and nonresident stepparenting into our `traditional' definition of a stepparent (married adult with resident stepchildren). Using the National Survey of Families and Households, I provide a demographic profile of stepparents that includes cohabiting and nonresident stepparents. Adopting this revised view of stepparenthood alters our knowledge of both the prevalence and composition of stepparent families. The `traditional' definition of a stepparent is shown to describe less thanhalf of all stepparents today. Results also highlight diversity in stepparents' parenting obligations and sociodemographic characteristics. This revised view of stepparenthood has implications for future research on and policy targeted at stepfamilies.     

从目的论视角探析企业简介翻译错误及其成因

根据Nord对翻译错误的分类和我们的调研,认为当前国内企业简介英语译文存在的问题主要有语用、文化和语言三方面错误。其主要原因在于译者缺乏明确的翻译目的,没能把译文读者放在首位,只是一味追求译文与原文在形式上的对等。企业简介翻译应当遵循三个原则：目的原则、译文读者导向原则和合适原则。     

Benchmark Dose Analysis via Nonparametric Regression Modeling

Estimation of benchmark doses (BMDs) in quantitative risk assessment traditionally is based upon parametric dose‐response modeling. It is a well‐known concern, however, that if the chosen parametric model is uncertain and/or misspecified, inaccurate and possibly unsafe low‐dose inferences can result. We describe a nonparametric approach for estimating BMDs with quantal‐response data based on an isotonic regression method, and also study use of corresponding, nonparametric, bootstrap‐based confidence limits for the BMD. We explore the confidence limits’ small‐sample properties via a simulation study, and illustrate the calculations with an example from cancer risk assessment. It is seen that this nonparametric approach can provide a useful alternative for BMD estimation when faced with the problem of parametric model uncertainty.     

Robust bootstrap forecast densities for GARCH returns and volatilities

Bootstrap procedures are useful to obtain forecast densities for both returns and volatilities in the context of generalized autoregressive conditional heteroscedasticity models. In this paper, we analyse the effect of additive outliers on the finite sample properties of these bootstrap densities and show that, when obtained using maximum likelihood estimates of the parameters and standard filters for the volatilities, they are badly affected with dramatic consequences on the estimation of Value-at-Risk. We propose constructing bootstrap densities for returns and volatilities using a robust parameter estimator based on variance targeting implemented together with an adequate modification of the volatility filter. We show that the performance of the proposed procedure is adequate when compared with available robust alternatives. The results are illustrated with both simulated and real data.