Confidence intervals based on the deviance statistic for the hyperparameters in state space models

The main objective of this work is to evaluate the performance of confidence intervals, built using the deviance statistic, for the hyperparameters of state space models. The first procedure is a marginal approximation to confidence regions, based on the likelihood test, and the second one is based on the signed root deviance profile. Those methods are computationally efficient and are not affected by problems such as intervals with limits outside the parameter space, which can be the case when the focus is on the variances of the errors. The procedures are compared to the usual approaches existing in the literature, which includes the method based on the asymptotic distribution of the maximum likelihood estimator, as well as bootstrap confidence intervals. The comparison is performed via a Monte Carlo study, in order to establish empirically the advantages and disadvantages of each method. The results show that the methods based on the deviance statistic possess a better coverage rate than the asymptotic and bootstrap procedures.     

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.     

Inferences for a Partially Varying Coefficient Model With Endogenous Regressors

In this article, we propose a new class of semiparametric instrumental variable models with partially varying coefficients, in which the structural function has a partially linear form and the impact of endogenous structural variables can vary over different levels of some exogenous variables. We propose a three-step estimation procedure to estimate both functional and constant coefficients. The consistency and asymptotic normality of these proposed estimators are established. Moreover, a generalized F-test is developed to test whether the functional coefficients are of particular parametric forms with some underlying economic intuitions, and furthermore, the limiting distribution of the proposed generalized F-test statistic under the null hypothesis is established. Finally, we illustrate the finite sample performance of our approach with simulations and two real data examples in economics.     

A comparative study of confidence intervals to assess biosimilarity from analytical data

Assessment of analytical similarity of tier 1 quality attributes is based on a set of hypotheses that tests the mean difference of reference and test products against a margin adjusted for standard deviation of the reference product. Thus, proper assessment of the biosimilarity hypothesis requires statistical tests that account for the uncertainty associated with the estimations of the mean differences and the standard deviation of the reference product. Recently, a linear reformulation of the biosimilarity hypothesis has been proposed, which facilitates development and implementation of statistical tests. These statistical tests account for the uncertainty in the estimation process of all the unknown parameters. In this paper, we survey methods for constructing confidence intervals for testing the linearized reformulation of the biosimilarity hypothesis and also compare the performance of the methods. We discuss test procedures using confidence intervals to make possible comparison among recently developed methods as well as other previously developed methods that have not been applied for demonstrating analytical similarity. A computer simulation study was conducted to compare the performance of the methods based on the ability to maintain the test size and power, as well as computational complexity. We demonstrate the methods using two example applications. At the end, we make recommendations concerning the use of the methods.     

论青年女性流动人口初育间隔与人口控制——来自厦门流动人口家庭问卷调查的启示

青年女性流动人口实际生育行为的控制对于人口规模与结构的控制,起着至关重要的作用。而生育时间的选择作为影响流动人口生育实践的一个重要因素,体现在对各孩次生育间隔的控制上。本文在描述分析的基础上,应用Cox比例风险模型,主要对厦门市青年女性流动人口初育间隔的影响因素进行估计和检验,并在总结分析的基础上对流动人口控制问题提出几点对策思考。     

"中国信心":当代中华民族凝聚力的现实解读

"中国信心"不仅仅是一个新闻词汇或政治口号,而是引领当代中国社会发展的民族精神和时代精神的精华.在内涵上,"中国信心"是民族性与时代性的辩证统一,是中国特色社会主义理论体系的重要内容;在本质上,"中国信心"是成就感与使命感的高度结合,是社会主义精神文明建设的时代展示;在功能定位上,"中国信心"是当代中华民族凝聚力增强的鲜明亮点,对提升国家文化软实力具有重要意义.     

经济周期、模型再造与中国经济增长波动的论证——中国“十一五“经济增长速度与波动区间

发达国家经济学家经济增长周期理论虽然基本没有完全脱离实际,但都存在片面性,因此必须依据马克思的思维方式才能建立起科学的经济增长与周期分析模型。周期性研究目的在于理论联系实际指导中国的具体经济增长,通过分析影响中国一定时期内经济的适度速度、波动区间及持续时间,规划时期内投资、消费与外贸,才能推进经济适度快速增长和时间的持续。     

New evidence about effects of reproductive variables on child mortality in sub-Saharan Africa

There is still considerable uncertainty about how reproductive factors affect child mortality. This study, based on Demographic and Health Survey data from 28 countries in sub-Saharan Africa, shows that mortality is highest for firstborn children with very young mothers. Other children with young mothers, or of high birth order, also experience high mortality. Net of maternal age and birth order, a short preceding birth interval is associated with above average mortality. These patterns change, however, if time-invariant unobserved mother-level characteristics of importance for both mortality and fertility are controlled for in a multilevel–multiprocess model. Most importantly, there are smaller advantages associated with longer birth intervals and being older at first birth. The implications of alternative reproductive ‘strategies’ are discussed, taking into account that if the mother is older at birth, the child will also be born in a later calendar year, when mortality may be lower.     

Does community social embeddedness promote generalized trust? An experimental test of the spillover effect

Despite the theoretical relevance attributed to the spillover effect, little empirical research has focused on testing its causal validity. Addressing this gap in the literature, I propose a novel experimental design to test if the overall density of social links in a community promotes trustworthy and trusting behaviors with absolute strangers. Controlling for social integration (i.e. the individual number of social connections), I found that density fosters higher levels of trust. In particular, results show that people in denser communities are more likely to trust their unknown fellow citizens, encouraging isolated subjects to engage with strangers. However, evidence did not support the idea that community social embeddedness causes an increase of trustworthiness, indicating that the spillover effect works only with respect to trust.     

Approximate Bayesian inference for latent Gaussian models by using integrated nested Laplace approximations

Summary.  Structured additive regression models are perhaps the most commonly used class of models in statistical applications. It includes, among others, (generalized) linear models, (generalized) additive models, smoothing spline models, state space models, semiparametric regression, spatial and spatiotemporal models, log-Gaussian Cox processes and geostatistical and geoadditive models. We consider approximate Bayesian inference in a popular subset of structured additive regression models, latent Gaussian models , where the latent field is Gaussian, controlled by a few hyperparameters and with non-Gaussian response variables. The posterior marginals are not available in closed form owing to the non-Gaussian response variables. For such models, Markov chain Monte Carlo methods can be implemented, but they are not without problems, in terms of both convergence and computational time. In some practical applications, the extent of these problems is such that Markov chain Monte Carlo sampling is simply not an appropriate tool for routine analysis. We show that, by using an integrated nested Laplace approximation and its simplified version, we can directly compute very accurate approximations to the posterior marginals. The main benefit of these approximations is computational: where Markov chain Monte Carlo algorithms need hours or days to run, our approximations provide more precise estimates in seconds or minutes. Another advantage with our approach is its generality, which makes it possible to perform Bayesian analysis in an automatic, streamlined way, and to compute model comparison criteria and various predictive measures so that models can be compared and the model under study can be challenged.