金融结构对产业结构影响的时变特征研究——基于SUR和TVPSS模型的实证检验

金融结构与产业结构的关系一直是学术界的研究热点.文章利用中国1998—2017年的年度数据,构建似不相关回归模型从金融结构规模、效率及深化的角度分析金融结构对产业结构合理化和高级化的影响,建立时变参数状态空间模型描绘了金融结构对产业结构合理化和高级化的动态冲击.实证分析结果表明:金融结构规模提高产业结构合理化水平,促进了产业结构高级化;金融结构效率提高产业结构合理化水平,抑制了产业结构高级化;金融结构深化降低产业结构合理化水平,促进了产业结构高级化.金融结构规模、金融结构效率及金融结构深化对产业结构合理化和高级化的冲击均呈现出时变特征;金融结构对产业结构合理化的影响滞后于其对产业结构高级化的影响.金融结构对产业结构的冲击波幅呈现出前期波动大、后期较为平缓的状态,部分金融结构变量对产业结构的动态冲击呈现出"长尾"现象.当前的中国金融结构已经不适合当前的产业结构,需调整金融结构,以提升产业结构合理化水平和高级化水平.     

Part-time employment and worker health in the United States

A growing body of research has highlighted the consequences of part-time employment for workers’ health and well-being. However, these studies have yielded inconsistent results and relied on cross-sectional data. In addition, relatively little empirical research has explored whether the effect of working part-time on health varies by gender, particularly in the United States. Using longitudinal data from three waves of the General Social Survey panel (2010–2012–2014), our study examined the association between part-time employment and perceived health among U.S employees, and whether this association varied by gender. The results showed that part-time workers were less likely to report poor self-rated health than full-time workers, especially among males. The pattern of results was consistent across empirical approaches—including generalized estimating equations and random effects models, with an extensive set of covariates. Taken together, these findings suggest that for U.S. employees, working part-time appears to be beneficial or at least not detrimental to perceived health, which warrants further investigation.     

A population sample registry for public health survey investigations: methods,lessons, and successes

Public health relies on sample surveys to monitor the health of populations and investigate factors that contribute to population health and illness. Such investigations require surveying individuals who are selected in ways that support statistical inference to the population. Faced with rising costs and declining participation, survey researchers are exploring economical ways to recruit samples that validly represent larger populations. The current report describes an innovative resource, the Colorado Adult Population Sample–Survey Research Registry, which maintains survey access to respondents from a probability-based state health survey. We describe recruitment and retention strategies, explore representativeness, report several studies that used the registry, and discuss future directions.     

The exponentiated power exponential regression model with different regression structures: application in nursing data

We define the exponentiated power exponential distribution and propose a regression model with different systematic structures based on the new distribution. We show that the new regression model can be applied to dispersion data since it represents a parametric family of models that includes as sub-models some widely-known regression models. It then can be used more effectively in the analysis of real data. We use maximum likelihood estimation and derive the appropriate matrices for assessing local influence on the parameter estimates under different perturbation schemes. Some global-influence measurements are also investigated and simulation studies are performed to evaluate the accuracy of the estimates. We provide an application of the regression model with four systematic structures to nursing activities score data in the Unit of the Medical Clinic of University of São Paulo (USP) Hospital.     

Cox regression of clustered event times with covariates missing not at random

Motivated by a recent tuberculosis (TB) study, this paper is concerned with covariates missing not at random (MNAR) and models the potential intracluster correlation by a frailty. We consider the regression analysis of right‐censored event times from clustered subjects under a Cox proportional hazards frailty model and present the semiparametric maximum likelihood estimator (SPMLE) of the model parameters. An easy‐to‐implement pseudo‐SPMLE is then proposed to accommodate more realistic situations using readily available supplementary information on the missing covariates. Algorithms are provided to compute the estimators and their consistent variance estimators. We demonstrate that both the SPMLE and the pseudo‐SPMLE are consistent and asymptotically normal by the arguments based on the theory of modern empirical processes. The proposed approach is examined numerically via simulation and illustrated with an analysis of the motivating TB study data.     

What influences the size of child maintenance payments? A register‐based study of maintenance payments in Finland

The aim of this study was to explore how particular economic and demographic factors contribute to the level of the child maintenance payment (CMP) paid by the non‐resident parent. For this study, we used 5‐year longitudinal panel data from years 2009 to 2013 consisting of over 80,000 non‐resident parents from the Finnish Tax Administration and The Finnish Population Register Centre. Results from regression models indicate that the single biggest factor affecting the size of CMPs is the number of dependent children. We found that the non‐resident parent's higher income is associated with higher CMPs and that non‐resident fathers pay on average larger CMPs than non‐resident mothers, even after accounting for differences in income. Unexpectedly, we found that the age of the dependent child did not predict changes in CMPs. This suggests that once formal CMP‐contracts are determined between the parents, they are seldom changed. Our results suggest that some degree of mandatory periodic review for maintenance contracts is worth considering.     

Probabilistic frontier regression models for binary type output data

This paper proposes a probabilistic frontier regression model for binary type output data in a production process setup. We consider one of the two categories of outputs as ‘selected’ category and the reduction in probability of falling in this category is attributed to the reduction in technical efficiency (TE) of the decision-making unit. An efficiency measure is proposed to determine the deviations of individual units from the probabilistic frontier. Simulation results show that the average estimated TE component is close to its true value. An application of the proposed method to the data related to the Indian public sector banking system is provided where the output variable is the indicator of level of non-performing assets. Individual TE is obtained for each of the banks under consideration. Among the public sector banks, Andhra bank is found to be the most efficient, whereas the United Bank of India is the least.     

Oracle GMM estimation for misspecified models via thresholding

We propose a thresholding generalized method of moments (GMM) estimator for misspecified time series moment condition models. This estimator has the following oracle property: its asymptotic behavior is the same as of any efficient GMM estimator obtained under the a priori information that the true model were known. We propose data adaptive selection methods for thresholding parameter using multiple testing procedures. We determine the limiting null distributions of classical parameter tests and show the consistency of the corresponding block-bootstrap tests used in conjunction with thresholding GMM inference. We present the results of a simulation study for a misspecified instrumental variable regression model and for a vector autoregressive model with measurement error. We illustrate an application of the proposed methodology to data analysis of a real-world dataset.