中国新凯恩斯主义菲利普斯曲线的经验研究

 本文构建了附加利率的新凯恩斯主义菲利普斯曲线模型,并运用广义矩估计的研究方法,运用中国1997 -2008年的季度数据对所构建的模型进行了估计与检验。经验结果表明,利率作为通货膨胀的驱动因素在统计和经济意义上都具有显著性,并且较国外许多国家更明显;当期通货膨胀动态变化受通货膨胀惯性和预期的共同影响,而预期起主导作用;厂商的定价行为既有前瞻性,又有后顾性,但后顾性处于主导地位。     

Multilevel models for longitudinal data

Summary.  Repeated measures and repeated events data have a hierarchical structure which can be analysed by using multilevel models. A growth curve model is an example of a multilevel random-coefficients model, whereas a discrete time event history model for recurrent events can be fitted as a multilevel logistic regression model. The paper describes extensions to the basic growth curve model to handle auto-correlated residuals, multiple-indicator latent variables and correlated growth processes, and event history models for correlated event processes. The multilevel approach to the analysis of repeated measures data is contrasted with structural equation modelling. The methods are illustrated in analyses of children's growth, changes in social and political attitudes, and the interrelationship between partnership transitions and childbearing.     

Functional analysis of high-content high-throughput imaging data

High-content automated imaging platforms allow the multiplexing of several targets simultaneously to generate multi-parametric single-cell data sets over extended periods of time. Typically, standard simple measures such as mean value of all cells at every time point are calculated to summarize the temporal process, resulting in loss of time dynamics of the single cells. Multiple experiments are performed but observation time points are not necessarily identical, leading to difficulties when integrating summary measures from different experiments. We used functional data analysis to analyze continuous curve data, where the temporal process of a response variable for each single cell can be described using a smooth curve. This allows analyses to be performed on continuous functions, rather than on original discrete data points. Functional regression models were applied to determine common temporal characteristics of a set of single cell curves and random effects were employed in the models to explain variation between experiments. The aim of the multiplexing approach is to simultaneously analyze the effect of a large number of compounds in comparison to control to discriminate between their mode of action. Functional principal component analysis based on T-statistic curves for pairwise comparison to control was used to study time-dependent compound effects.     

Piecewise deterministic Markov processes applied to fatigue crack growth modelling

In this paper, we use a particular piecewise deterministic Markov process (PDMP) to model the evolution of a degradation mechanism that may arise in various structural components, namely, the fatigue crack growth. We first derive some probability results on the stochastic dynamics with the help of Markov renewal theory: a closed-form solution for the transition function of the PDMP is given. Then, we investigate some methods to estimate the parameters of the dynamical system, involving Bogolyubov's averaging principle and maximum likelihood estimation for the infinitesimal generator of the underlying jump Markov process. Numerical applications on a real crack data set are given.     

低碳经济背景下碳排放的库兹涅茨曲线研究

 经济增长与环境质量之间的关系是环境经济学研究的热点问题。环境库兹涅茨曲线则是研究该问题较为普遍的方法。传统的环境库兹涅茨曲线的研究模式是事先假定曲线存在,然后利用固定模式的参数模型进行拟合估计,但这种做法的不足之处在于我们并不能够确定各国各地区的经济增长与环境质量存在这种关系。本文在低碳经济背景下,采用非参数模型,研究二氧化碳作为环境代理指标的碳排放的环境库兹涅茨曲线。研究发现：我国的经济增长与环境质量之间不存在倒“U”型曲线关系,FDI与城市化对我国的环境质量的影响统计意义显著,但经济意义不显著,而贸易、能源强度则对我国的环境具有恶化作用。     

Investigate Data Dependency for Dynamic Gene Regulatory Network Identification through High-dimensional Differential Equation Approach

Gene regulation plays a fundamental role in biological activities. The gene regulation network (GRN) is a high-dimensional complex system, which can be represented by various mathematical or statistical models. The ordinary differential equation (ODE) model is one of the popular dynamic GRN models. We proposed a comprehensive statistical procedure for ODE model to identify the dynamic GRN. In this article, we applied this model to different segments of time course gene expression data from a simulation experiment and a yeast cell cycle study. We found that the two cell cycle and one cell cycle data provided consistent results, but half cell cycle data produced biased estimation. Therefore, we may conclude that the proposed model can quantify both two cell cycle and one cell cycle gene expression dynamics, but not for half cycle dynamics. The findings suggest that the model can identify the dynamic GRN correctly if the time course gene expression data are sufficient enough to capture the overall dynamics of underlying biological mechanism.     

金砖国家经济增长源泉的异质性及强度研究——基于互联网、金融发展和法治水平的视角

国家的异质性导致了各国经济增长源泉具有较强的差异性。运用2003年至2013年金砖国家经济发展数据,构建面板数据门槛回归模型,研究互联网、金融发展及法治水平对金砖国家经济增长的影响。实证结果表明:互联网对金砖国家经济增长促进作用并不显著,现阶段金融发展对经济增长推动作用有限。但随着法治水平的提高,金融发展对经济增长的推动作用呈J字型增长,法治水平的提高有利于推动金砖国家金融发展。加强互联网、金融及法治建设将为金砖国家经济增长注入新的动力。     

Generalized growth curve models for longitudinal data in application to a randomized controlled trial

Growth curve analysis is beneficial in longitudinal studies, where the pattern of response variables measured repeatedly over time is of interest, yet unknown. In this article, we propose generalized growth curve models under a polynomial regression framework and offer a complete process that identifies the parsimonious growth curves for different groups of interest, as well as compares the curves. A higher order of a polynomial degree generally provides more flexible regression, yet it may suffer from the complicated and overfitted model in practice. Therefore, we employ the model selection procedure that chooses the optimal degree of a polynomial consistently. Consideration of a quadratic inference function (Qu et al., 2000) for estimation on regression parameters is addressed and estimation efficiency is improved by incorporating the within-subject correlation commonly existing in longitudinal data. In biomedical studies, it is of particular interest to compare multiple treatments and provide an effective one. We further conduct the hypothesis test that assesses the equality of the growth curves through an asymptotic chi-square test statistic. The proposed methodology is employed on a randomized controlled longitudinal dataset on depression. The effectiveness of our procedure is also confirmed with simulation studies.     

Longitudinal data analysis of animal growth via multivariate dynamic models

We propose models to analyze animal growth data with the aim of estimating and predicting quantities of biological and economical interest such as the maturing rate and asymptotic weight. It is also studied the effect of environmental factors of relevant influence in the growth process. The models considered in this paper are based on an extension and specialization of the dynamic hierarchical model (Gamerman & Migon, 1993) to a non–linear growth curve setting, where some of the growth curve parameters are considered exchangeable among the units. The inference for these models are approximate conjugate analysis based on Taylor series expansions and linear Bayes procedures     

THE REDUCED-RANK GROWTH CURVE MODEL FOR DISCRIMINANT ANALYSIS OF LONGITUDINAL DATA

This paper presents a method of discriminant analysis especially suited to longitudinal data. The approach is in the spirit of canonical variate analysis (CVA) and is similarly intended to reduce the dimensionality of multivariate data while retaining information about group differences. A drawback of CVA is that it does not take advantage of special structures that may be anticipated in certain types of data. For longitudinal data, it is often appropriate to specify a growth curve structure (as given, for example, in the model of Potthoff & Roy, 1964). The present paper focuses on this growth curve structure, utilizing it in a model-based approach to discriminant analysis. For this purpose the paper presents an extension of the reduced-rank regression model, referred to as the reduced-rank growth curve (RRGC) model. It estimates discriminant functions via maximum likelihood and gives a procedure for determining dimensionality. This methodology is exploratory only, and is illustrated by a well-known dataset from Grizzle & Allen (1969).     

Two Samples Tests for Functional Data

Data in many experiments arises as curves and therefore it is natural to use a curve as a basic unit in the analysis, which is in terms of functional data analysis (FDA). Functional curves are encountered when units are observed over time. Although the whole function curve itself is not observed, a sufficiently large number of evaluations, as is common with modern recording equipment, is assumed to be available. In this article, we consider the statistical inference for the mean functions in the two samples problem drawn from functional data sets, in which we assume that functional curves are observed, that is, we consider the test if these two groups of curves have the same mean functional curve when the two groups of curves without noise are observed. The L ²-norm based and bootstrap-based test statistics are proposed. It is shown that the proposed methodology is flexible. Simulation study and real-data examples are used to illustrate our techniques.     

Identification of Multivariate Responders/Non-Responders Using Bayesian Growth Curve Latent Class Models

In this paper, we propose a multivariate growth curve mixture model that groups subjects based on multiple symptoms measured repeatedly over time. Our model synthesizes features of two models. First, we follow Roy and Lin (2000) in relating the multiple symptoms at each time point to a single latent variable. Second, we use the growth mixture model of Muthén and Shedden (1999) to group subjects based on distinctive longitudinal profiles of this latent variable. The mean growth curve for the latent variable in each class defines that class's features. For example, a class of "responders" would have a decline in the latent symptom summary variable over time. A Bayesian approach to estimation is employed where the methods of Elliott et al (2005) are extended to simultaneously estimate the posterior distributions of the parameters from the latent variable and growth curve mixture portions of the model. We apply our model to data from a randomized clinical trial evaluating the efficacy of Bacillus Calmette-Guerin (BCG) in treating symptoms of Interstitial Cystitis. In contrast to conventional approaches using a single subjective Global Response Assessment, we use the multivariate symptom data to identify a class of subjects where treatment demonstrates effectiveness. Simulations are used to confirm identifiability results and evaluate the performance of our algorithm. The definitive version of this paper is available at onlinelibrary.wiley.com.     

A nonlinear regression model for describing averaged growth curves of mice

A general four parameter growth curve is presented as a model for the growth curve of a group of mice for which averaged weights of the group are available. Several data sets of mice weights obtained from experiments performed at the National Center for Toxicological Research are analyzed. The results are compared with traditional models for growth curves. Both additive and multiplicative error models are analyzed. It is shown that for this data the four parameter model gives a much better fit than traditional growth curve models and should be given serious consideration in model fitting.     

中国碳排放库兹涅茨曲线存在性研究

经济增长与环境恶化之间的矛盾直接推动了碳排放库兹涅茨曲线的研究。本文基于1995-2009年中国省际碳排放面板数据,利用面板协整和误差修正模型对中国碳排放库兹涅茨曲线的存在性进行实证研究,结果发现：动态固定效应下的面板误差修正模型最优,碳排放与经济增长间存在长期稳定的倒U型关系,拐点为人均国内生产总值29847.29元,且存在短期误差修正机制;与权威机构时序数据回归结果比较,动态面板模型结果具有稳健性。     

A Joint Modeling Approach for Longitudinal Outcomes and Non-ignorable Dropout under Population Heterogeneity in Mental Health Studies

The paper proposes a joint mixture model to model non-ignorable drop-out in longitudinal cohort studies of mental health outcomes. The model combines a (non)-linear growth curve model for the time-dependent outcomes and a discrete-time survival model for the drop-out with random effects shared by the two sub-models. The mixture part of the model takes into account population heterogeneity by accounting for latent subgroups of the shared effects that may lead to different patterns for the growth and the drop-out tendency. A simulation study shows that the joint mixture model provides greater precision in estimating the average slope and covariance matrix of random effects. We illustrate its benefits with data from a longitudinal cohort study that characterizes depression symptoms over time yet is hindered by non-trivial participant drop-out.KEYWORDS: Latent growth curve, MNAR drop-out, survival analysis, finite mixture model, mental health     

Bayesian Analysis of a Growth Curve Model with a General Autoregressive Covariance Structure

In this paper we consider from maximum likelihood and Bayesian points of view the generalized growth curve model when the covariance matrix has a Toeplitz structure. This covariance is a generalization of the AR(1) dependence structure. Inferences on the parameters as well as the future values are included. The results are illustrated with several real data sets.     

结构调整、政府规模对“科技进步与经济增长”关系的影响——基于中介效应和调节效应的分析

基于中国1990—2011年相关数据,在验证科技进步对经济增长促进作用的基础上,引入结构调整中介变量和政府规模调节变量,深入剖析结构调整、政府规模与科技进步、经济增长的内在关系,研究表明:结构调整在"科技进步与经济增长"关系中中介效应显著,科技进步可以促进结构优化,加速经济调整和发展;政府规模在"科技进步与经济增长"的关系中调节效应显著,政府规模可以调节"科技进步与经济增长"的关系。     

进口投入品与中国企业的就业变动

基于中国微观工业企业数据,本文统计分析了中国企业参与进口贸易的基本情况,并实证分析了进口投入品对中国企业就业变动的影响。研究发现：与非贸易企业、纯出口企业和纯进口企业相比,有出口行为的进口企业的就业规模最大、就业增长最快、就业创造最多、就业破坏最少,就业增长稳定性也最好;进口强度、进口产品数、来自低收入国家进口、资本品进口、异质产品进口对企业就业有显著正向影响;相对于一般贸易方式进口,加工贸易方式进口对企业就业增长效果更为显著;进口投入品仅对有出口行为的进口企业就业增长影响显著,而对纯进口企业没有显著影响。今后,我国扩大进口贸易的相关政策应该进一步的战略性细化,针对不同类型商品进口、从不同国家和地区的进口制定有差异的政策,并高度重视有出口行为的进口企业在就业方面的贡献。     

Statistical methods in optimal curve fitting

Many optimal curve fitting and approximation problems have the same structure as certain estimation problems involving random processes. This structural correspondence has many useful consequences for curve fitting problems, including recursive algorithms and computable error bounds. The basic facts of this correspondence are reviewed and some new results on error bounds and optimal sampling are presented.     

An insight into technology diffusion of tractor through Weibull growth model

Most of the technological innovation diffusion follows an S-shaped curve. But, in many practical situations this may not hold true. To this end, Weibull model was proposed to capture the diffusion of new technological innovation, which does not follow any specific pattern. Nonlinear growth models play a very important role in getting an insight into the underlying mechanism. These models are generally ‘mechanistic’ as the parameters have meaningful interpretation. The nonlinear method of estimation of parameters of Weibull model fails to converge. Taking this problem into consideration, we propose the use of a powerful technique of genetic algorithm for parameter estimation. The methodology is also validated by simulation study to check whether parameter estimates are closer to the real value. For illustration purpose, we model the tractor density time-series data of India as a whole and some major states of India. It is seen that fitted Weibull model is able to capture the technology diffusion process in a reasonable manner. Further, comparison is also made with Logistic and Gompertz model; and is found to perform better for the data sets under consideration.