南京市劳动力资源供求状况研究

近两年，随着改革的深入，南京市的就业状况越来越严重。为了对这一状况有所了解，本文将利用数量分析的方法作一些分析与预测，并提出一些解决问题的对策。一、劳动力资源供求状况分析1、总供给：劳动力的总供给取决于总人口、劳动力资源占总人口的比重以及劳动力参与系数（需就业的人数与劳动力资源之比）等三个因素。（1）利用1989—1997年南京市总人口的样本数据，建立总人口经济计量模型：（1989年总人口为496．14万，1990一1997年总人口见表一）式中T为趋势变量；括号中的数据是各回归系数的t统计量。利用模型（1）求出1998—2000年…     

中国系统性金融风险对宏观经济的影响研究

基于2007年1月至2017年12月月度数据，本文首先选取金融机构极值风险、金融体系间的传染效应、金融市场的波动性和不稳定性、流动性和信用风险4个层面的14个代表性指标测度了系统性金融风险；然后运用分位数回归度量了单个系统性风险指标对宏观经济的影响；最后运用偏最小二乘分位数回归法构建一个系统性金融风险综合指标进一步实证分析系统性金融风险对宏观经济的影响。研究结果表明：①单个系统性金融风险指数中机构极值风险类别下的指标对宏观经济的影响最大，其中金融体系巨灾风险指数影响效果最显著；②运用偏最小二乘分位数回归构造的系统性金融风险综合指标较之单个系统性金融风险指标，能够更稳健地反映系统性金融风险对宏观经济的影响状况；③从测度效果来看，单个系统性风险指标和系统性金融风险综合指标在下尾分布（0.2分位数）的结果明显优于中间分布（0.5分位数）和上尾分布（0.8分位数）。     

中国要素替代弹性估计

要素替代弹性是经济研究中的重要参数,对经济增长和要素分配研究至关重要。本文基于一般要素增强型CES生产函数,利用1978-2011年省级面板数据,以变系数面板模型估计我国替代弹性时间序列。主要发现为：我国替代弹性明显小于1（0.23-0.55之间）,改革时期基本呈上升趋势;此时劳动增强型技术表现出资本偏向,构成近期我国劳动份额持续下降的重要原因。最后,对该领域未来发展方向进行展望。     

代际视角下机会不平等与农村居民收入差距

运用中国健康与营养调查(CHNS)1989-2011年的数据,采用夏普里值分解法考察了父代收入对农村子代收入差距的贡献及其变化。研究发现:1989-2011年间,父代收入对子代收入差距的贡献率一直较大。随着中国市场化程度以及经济水平的提高,尽管父代收入对农村子代收入差距的贡献率在2009年前总体上呈现出下降的趋势,但是2009年后其贡献率迅速上升。与此同时,教育因素的贡献率处于较低水平,这表明近年来机会不平等对收入差距的影响有所扩大。     

我国统计数据质量的评估方法研究————————趋势模拟评估法及其应用

 本文采用组合模型的形式对时间序列数据的变化特点建模，在模型通过各种检验、具有良好统计预测功能的基础上，从检验异常值的角度来分析预测值与实际值之间差异的程度，找出离群数据，利用数理统计中检验实验观测数据异常值的方法，对离群数据的误差进行统计上的显著检验，从而评估统计数据的质量。文章以我国国内生产总值（GDP）为研究对象，选取我国1978-2003年间的GDP作为样本，运用趋势模拟评估法来评估我国2004年国内生产总值的准确性。对我国经济指标的时间序列数据进行了实证分析。     

月度数据季节因素调整和预测

 内容提要：由于受气候条件、节假日、人们的风俗习惯、人口和国民经济增长等因素的影响,客运量呈现出周期性的增长趋势变化。为客运部门更好地安排客运计划,本文通过指数平滑法中的Holt-Winters模型将时间序列数据分解为季节波动和趋势波动。并对我国铁路、民航、水运和公路的2002-2009年的客运量数据进行拟合。结果表明,铁路和民航客运量数据具有明显的线性趋势和季节性特征,并进一步得出其波峰和波谷到达的时间;模型对铁路、民航、水运和公路客运量均有非常好的拟合效果,其平均绝对百分百误差（MAPE）依次为5.536%,7.49%、6.070%和3.633%。在此基础上对我国2010年各月份的客运量进行了科学预测。     

开放环境下的中国自然利率估算

文章借助LW半结构模型，在开放环境下估算了2002—2022年中国的自然利率水平，研究发现：（1）中国过去的自然利率基于外循环定价，国内资金市场对全球经济存在高度依存关系；（2）2012年以来中国自然利率呈现长期下降趋势，这与全球主要经济体自然利率运动规律一致，预计这一下降趋势长期不可逆转；（3）2019年自然利率水平为1.93%，与主要经济体之间仍保持了一定的利差；（4）2020—2022年自然利率与潜在增速分别出现0.65和2.46个百分点的最大跌幅。预计2023年潜在增速可能会回到4.5%～5.0%区间，自然利率也有望回升到1.6%以上。基于自然利率的绝对水平，名义利率零下限（ZLB）在短期不会形成事实威胁，传统货币政策仍保持了相当的政策空间。     

供求冲击与中国经济波动： 基于SVAR模型的甄别分析

 本文首先利用经全国经济普查信息修正后的季度数据推算得到1992年第1季度到2008年第3季度的实际GDP和GDP减缩指数，然后借鉴Blanchard和Quah（1989）提出的方法构建二元SVAR模型，对驱动中国经济波动的供求冲击进行甄别分析。结果发现：①无论是在长期还是在短期，2/3以上的产出波动可以归因于供给冲击的影响；②对于价格波动，短期内需求冲击和供给冲击的贡献几乎相当，而长期内需求冲击能够解释70％左右；③总的来说，供给冲击和需求冲击在中国经济波动中具有几乎同等的重要性。上述结论具有重要的宏观操作政策含义。     

具有核化函数的部分线性模型及其应用

本文基于再生核希尔伯特空间中的再生核，将核技巧与高斯-赛责尔迭代算法相结合，提出了具有核化函数的部分线性模型（PLMKF）及其算法收敛性条件等相关内容，具体包括：（1）基于OLS的PLMKF；（2）基于岭估计的PLMKF；（3）基于GLS的PLMKF；（4）基于多核学习的PLMKF。它们构成了PLMKF家族，具有一定的相互转化关系。在数值模拟中，本文验证了各个算法的有效性，比较了基于OLS与GLS、单核与多核的PLMKF模拟结果。实际应用中，在大幅外推情景下，PLMKF仍保持了良好的泛化能力，预测精度高于PLM、GAM和SVR。     

人均GDP与各产业之间的时间序列分析

文章收集了1980—2010年我国第一产业增加值、第二产业增加值、第三产业增加值和人均GDP的时间序列数据，利用最小二乘法以及统计软件Eviews进行时间序列建模。实证分析结果显示：（1）模型整体效果很好；（2）随机误差显著地存在异方差；（3）随机误差项存在正的一阶正相关；（4）存在滞后一期的自回归模型。     

A trajectory analysis of body mass index for Finnish children

The aim of this study is to investigate the early development of body mass index (BMI), a standard tool for assessing the body shape and average level of adiposity for children and adults. The main aim of the study is to identify the primary trajectories of BMI development and to investigate the changes of certain growth characteristics over time. Based on our longitudinal data of 4223 Finnish children, we took anthropometric measurements from birth up to 15 years of age for birth years 1974, 1981, 1991 and 1995, but only up to 11 years of age for the birth year 2001. As a statistical method, we utilized trajectory analysis with the methods of nonparametric regression. We identified four main trajectories of BMI growth. Two of these trajectories do not seem to follow the normal growth pattern. The highest growth track appears to yield to a track that may yield to overweight and the low birth BMI track shows that the girls’ track differs that of boys on the same track, and on the normal tracks. The so-called adiposity rebound time decreased over time and started earlier for those on the overweight track. According to our study, this kind of acceleration of growth might be more of a general phenomenon that also relates to the other phases of BMI development. The major change seems to occur especially for those children on high growth tracks.     

Level and change of group-focused enmity in Germany: unconditional and conditional latent growth curve models with four panel waves

In this study, we investigate and explain the level and change of six elements of group-focused enmity (GFE; see Zick et al. in J. Soc. Issues 64(2):363–383, 2008) in Germany between 2002 and 2006: racism, xenophobia, anti-Semitism, homophobia, exclusion of homeless people and support for rights of the established. For the data analysis, a representative 4-year panel study of the adult non-immigrant German population collected during the years 2002–2006 is used, and the development of each GFE component is tested by using an unconditional second-order latent growth curve model (LGM) (with full information maximum likelihood, FIML). We find that the level of 5 of the 6 components (racism, xenophobia, anti-Semitism, homophobia, exclusion of homeless people) displays an increase at the beginning of the observed period followed by a decrease. However, the sixth aspect, rights of the established, displays a continuous linear increase over time. The different developmental pattern stands in contrast to Allport’s (The nature of prejudice. Perseus Books, Cambridge, 1954) hypothesis for the strong link between the components and their development over time. We try to explain this different developmental pattern by several sociodemographic characteristics. This is performed by using a conditional second-order latent growth curve model.     

Acute Middle Ear Infection in Small Children: A Bayesian Analysis Using Multiple Time Scales

The study is based on a sample of 965 children living in Oulu region (Finland), who were monitored for acute middle ear infections from birth to the age of two years. We introduce a nonparametrically defined intensity model for ear infections, which involves both fixed and time dependent covariates, such as calendar time, current age, length of breast-feeding time until present, or current type of day care. Unmeasured heterogeneity, which manifests itself in frequent infections in some children and rare in others and which cannot be explained in terms of the known covariates, is modelled by using individual frailty parameters. A Bayesian approach is proposed to solve the inferential problem. The numerical work is carried out by Monte Carlo integration (Metropolis-Hastings algorithm).     

Statistical inference for oscillation processes

ABSTRACT

A new model for time series with a specific oscillation pattern is proposed. The model consists of a hidden phase process controlling the speed of polling and a nonparametric curve characterizing the pattern, leading together to a generalized state space model. Identifiability of the model is proved and a method for statistical inference based on a particle smoother and a nonparametric EM algorithm is developed. In particular, the oscillation pattern and the unobserved phase process are estimated. The proposed algorithms are computationally efficient and their performance is assessed through simulations and an application to human electrocardiogram recordings.     

Computing least trimmed squares regression with the forward search

Least trimmed squares (LTS) provides a parametric family of high breakdown estimators in regression with better asymptotic properties than least median of squares (LMS) estimators. We adapt the forward search algorithm of Atkinson (1994) to LTS and provide methods for determining the amount of data to be trimmed. We examine the efficiency of different trimming proportions by simulation and demonstrate the increasing efficiency of parameter estimation as larger proportions of data are fitted using the LTS criterion. Some standard data examples are analysed. One shows that LTS provides more stable solutions than LMS.     

数字经济、人工智能制造与劳动力错配

文章基于2013—2019年中国30个省份的面板数据,先运用模拟退火优化投影寻踪模型测算中国数字经济发展指数,再采用双向固定效应模型和广义空间两阶段最小二乘法验证数字经济和人工智能制造对劳动力资源错配的影响效应。结果表明,2013—2019年我国数字经济发展水平整体呈现稳中有进的趋势,年均增速为11.8%。尽管数字经济发展先行区主要位于东部沿海地区,但近年来西部地区各省份年均增速较高,已形成"东部领跑,西部追赶"的空间分异格局。数字经济能显著缓解劳动力错配,人工智能制造在其中发挥着重要的中介作用,这一结论经过稳健性检验后依然成立。     

“十二五”至2030年我国经济增长前景展望

 综合考虑未来国内外影响经济增长的因素及其变化,本文采用可计算一般均衡模型模拟了中国经济增长的三种情景。结果显示“十二五”期间经济增长速度可望接近8%左右,中长期内中国经济仍然具有较快增长的潜力;未来经济增长的主要风险在于经济发展的协调性较差,资源环境压力不断加大;能否发挥经济增长潜力的关键在于能否实现发展方式转变。     

Multilevel zero-inflated negative binomial regression modeling for over-dispersed count data with extra zeros

Count data with excess zeros often occurs in areas such as public health, epidemiology, psychology, sociology, engineering, and agriculture. Zero-inflated Poisson (ZIP) regression and zero-inflated negative binomial (ZINB) regression are useful for modeling such data, but because of hierarchical study design or the data collection procedure, zero-inflation and correlation may occur simultaneously. To overcome these challenges ZIP or ZINB may still be used. In this paper, multilevel ZINB regression is used to overcome these problems. The method of parameter estimation is an expectation-maximization algorithm in conjunction with the penalized likelihood and restricted maximum likelihood estimates for variance components. Alternative modeling strategies, namely the ZIP distribution are also considered. An application of the proposed model is shown on decayed, missing, and filled teeth of children aged 12 years old.     

Bayesian growth curve models with the generalized error distribution

To deal with the longitudinal data with both leptokurtic and platykurtic errors, we extend growth curve models using the generalized error distribution (GED) model. The Metropolis–Hastings algorithm is used to estimate the GED model parameters in the Bayesian framework. The application of the GED model is illustrated through the analysis of mathematical development data. Results show that the GED model can correctly identify the deviation from normal of the error distributions.     

中国各地区人口特征和房价波动的动态关系

本文分析了人口特征、金融市场和房地产市场三者的相互影响机制，基于2002-2015年中国大陆31个省市自治区直辖市的年度数据，建立了面板平滑转换模型，将人口密度作为异质变量构建计量模型来研究房地产市场的非线性影响因素，研究中国省市人口特征对房价波动的影响机制。实证结果表明：人均GDP对房价的影响随人口密度增加呈现非线性提升效应；人口密度小的城市房地产价格上涨比人口密度大的城市更像是“货币现象”；当人口密度较小时，地区中老年人口占比越大，房价有下降趋势，反映了房地产“年轻人推动房价上涨”的直观趋势，但是极少人口密度比较大城市例外。