The effects of population aging on industrial structure upgrading:Empirical analysis of provincial and threshold characteristics in China

Global population aging trends are intensifying,presenting multifaceted economic and social challenges for countries worldwide.As the world’s largest developing country,China has entered a phase of extreme demographic aging,posing significant questions about its impact on the ongoing upgrading of industrial structures.How does this demographic shift influence the upgrading of industrial structures,and does technological innovation mitigate or exacerbate this impact?The empirical results indicate that population aging impedes upgrading the industrial structure,while technological innovation positively affects the relationship between the two.Moreover,using technological innovation as a threshold variable,the impact of population aging on industrial structure upgrading evolves in a“gradient”manner from“impediment”to“insignificant”to“promotion”as the technological innovation levels increase.These findings offer practical guidance for tailoring industrial policies to different stages of technological advancement.

Threshold autoregression models for forecasting El Nino events

-In this paper, monthly mean SST data in a large area are used. After the spacial average of the data is carried out and the secular monthly means are substracted, a time series (Jan. 1951-Dec. 1985) of SST anomalies of the cold tongue water area in the eastern tropical Pacific Ocean is obtained. On the basis of the time series, an autoregression model, a self-exciting threshold autoregression model and an open loop autoregression model are developed respectively. The interannual variations are simulated by means of those models. The simulation results show that all the three models have made very good hindcasting for the nine El Nino events since 1951. In order to test the reliability of the open loop threshold model, extrapolated forecast was made for the period of Jan. 1986-Feb. 1987. It can be seen from the forecasting that the model could forecast well the beginning and strengthening stages of the recent El Nino event (1986-1987). Correlation coefficients of the estimations to observations are respectively 0. 84, 0. 88 and 0. 89. It is obvious that all the models work well and the open loop threshold one is the best. So the open loop threshold autoregression model is a useful tool for monitoring the SSTinterannual variation of the cold tongue water area in the Eastern Equatorial Pacific Ocean and for estimating the El Nino strength.

Tail Behavior of Threshold Models with Innovations in the Domain of Attraction of the Double Exponential Distribution

We consider a two-regime threshold autoregressive model where the driving noises are sequences of independent and identically distributed random variables with common distribution function which belongs to the domain of attraction of double exponential distribution. If in addition, for each and where denotes the convolution of the distribution function and we determine the tail behavior of the process and give the exact values of the coefficient.

Mean Threshold and ARNN Algorithms for Identification of Eye Commands in an EEG-Controlled Wheelchair

This paper represented Autoregressive Neural Network (ARNN) and meant threshold methods for recognizing eye movements for control of an electrical wheelchair using EEG technology. The eye movements such as eyes open, eyes blinks, glancing left and glancing right related to a few areas of human brain were investigated. A Hamming low pass filter was applied to remove noise and artifacts of the eye signals and to extract the frequency range of the measured signals. An autoregressive model was employed to produce coefficients containing features of the EEG eye signals. The coefficients obtained were inserted the input layer of a neural network model to classify the eye activities. In addition, a mean threshold algorithm was employed for classifying eye movements. Two methods were compared to find the better one for applying in the wheelchair control to follow users to reach the desired direction. Experimental results of controlling the wheelchair in the indoor environment illustrated the effectiveness of the proposed approaches.

劳动力成本驱动物流业发展研究

选取2004—2020年长三角城市群26市的面板数据,采用熵值法测算各城市物流发展水平并描述其演变规律,利用普通面板模型、面板向量自回归模型及门槛模型探究劳动力成本对物流发展水平的影响。结果表明:各城市物流发展水平整体上有所进步,具有明显的俱乐部趋同现象和惯性发展趋势。劳动力成本是物流发展水平的驱动因素,劳动力成本的正向影响存在阶段性,当劳动力成本和产业结构跨越门槛值时,其促进作用呈先弱后强的非线性特征。

考虑随机变失效阈值的多相关退化可靠性建模

由于机械部件失效受多个退化过程及外界冲击等各种因素的耦合作用影响,而当前研究多考虑单一因素,并认为失效阈值为定值,未考虑到设备会因退化而导致其抗冲击能力降低的事实,因此同时考虑冲击对退化过程的影响,退化过程对冲击失效阈值的随机不确定性影响以及不同退化过程之间存在相关性影响,提出考虑随机变阈值的多相关退化可靠性模型。此外,通过Copula函数描述不同退化过程间的相关性,给出相关模型的参数估计方法。根据某型动车组的车轮实测磨耗数据进行实例计算,通过蒙特卡罗方法验证模型的准确性,讨论不同退化过程存在相关性对可靠度的影响,对随机变失效阈值进行灵敏度分析,进一步说明所提模型的实用性。

Does China's digital economy contribute to low-carbon transition?

The digital economy,as a new emerging economic form,has become an important power for realizing Chinese-style modernization and promoting green development in China.This paper measures the digital economy and low-carbon transition index based on the data of 30 provinces in China from 2013 to 2020 and analyzes the mechanism and path of the digital economy affecting low-carbon transition using the fixed effect panel data model and the threshold effect model.It is found that,(1)The digital economy and low-carbon transition in China are various in different regions,with characteristics of being unbalanced and insufficient.(2)The digital economy significantly promotes low-carbon transition,with the greatest influence in the Central region,followed by the Eastern region and the Western region.Under different dimensions,the development of informatization and digital transactions promote low-carbon transition,but the development of the internet plays an inhibiting role.(3)The higher the degree of urbanization and environmental regulation,the greater the influence of the digital economy on low-carbon transition.

融合小波阈值与多维自回归的时序预报模型研究

针对传统统计模型并不能完全涵盖位移影响分量信息以及真实影响分量信息易受到噪声干扰等问题,提出了一种融合小波阈值理论与多维自回归的混凝土坝位移时序预报模型。该方法主要是将小波阈值理论与时间序列算法结合起来创建混凝土坝位移时序预报模型,模型通过不同小波分解层数、小波基、阈值选取准则、阈值函数集成出一个MATLAB编码平台进行数据平滑处理,能高效挖掘大坝位移数据的影响分量信息,并选择自回归(autoregressive model, AR)时间序列模型作为预报模型。实例应用表明,新的融合模型预测性能较好,能有效监测大坝运行状态,且其分析结果对于其他数字工程的数据预测也具参考价值。

多轨道数字音频自适应变阶谱降噪模型构建

文中提出多轨道数字音频自适应变阶谱降噪模型构建,采用一阶高通数字滤波器预加重处理多轨道数字音频信号,以此为基础,通过最大熵谱估计算法估计数字音频信号频谱,搭建自适应变阶谱降噪模型,确定谱减阶数的自适应取值规则。将待处理的多轨道数字音频频谱估计结果输入至训练好的降噪模型中,输出结果经过逆变换即为降噪完成后的多轨道数字音频,从而实现了多轨道数字音频的自适应变阶谱降噪。实验数据显示:构建模型应用后,可以有效去除音频噪声信号,并不会缺失音频有效信息,降噪后多轨道数字音频信噪比最大值为91.25 dB,充分证实了构建模型降噪效果更佳。

Developing an Innovative High-precision Approach to Predict Medium-term and Long-term Satellite Clock Bias

A new prediction method based on the nonlinear autoregressive model is proposed to improve the accuracy of medium-term and long-term predictions of Satellite Clock Bias(SCB).Forecast experiments for three time periods were implemented based on the precision SCB published on the International GNSS Server(IGS)server.The results show that the medium-term and long-term prediction accuracy of the proposed approach is significantly better compared to other traditional models,with the training time being much shorter than the wavelet neural network model.

基于旋转算法的随机模糊均值-方差投资组合优化

Markowitz首先采用方差度量风险,并应用于投资组合优化中,大多数的均值方差模型仅对随机投资组合优化或模糊投资组合优化进行研究,然而,实际投资组合优化问题既包含随机信息也包含模糊信息。本文首先定义随机模糊变量的方差,并用其度量风险,提出了具有交易成本、借贷约束和阀值约束的均值-方差随机模糊投资组合优化模型。基于随机模糊理论,将上述模型转化为具有线性等式和线性不等式约束的凸二次规划问题,并得到其KKT条件。本文还提出改进的旋转算法求解上述模型,该算法消掉KKT条件中部分变量,减少计算量。最后,采用中国证券市场的实际数据进行样本内分析和样本外分析,验证了上述模型和算法的有效性。

Analyzing short time series data from periodically fluctuating rodent populations by thresholdmodels: A nearest block bootstrap approach

The study of the rodent fluctuations of the North was initiated in its modern form with Elton's pioneering work.Many scientific studies have been designed to collect yearly rodent abundance data,but the resulting time series are generally subject to at least two "problems":being short and non-linear.We explore the use of the continuous threshold autoregressive(TAR) models for analyzing such data.In the simplest case,the continuous TAR models are additive autoregressive models,being piecewise linear in one lag,and linear in all other lags.The location of the slope change is called the threshold parameter.The continuous TAR models for rodent abundance data can be derived from a general prey-predator model under some simplifying assumptions.The lag in which the threshold is located sheds important insights on the structure of the prey-predator system.We propose to assess the uncertainty on the location of the threshold via a new bootstrap called the nearest block bootstrap(NBB) which combines the methods of moving block bootstrap and the nearest neighbor bootstrap.The NBB assumes an underlying finite-order time-homogeneous Markov process.Essentially,the NBB bootstraps blocks of random block sizes,with each block being drawn from a non-parametric estimate of the future distribution given the realized past bootstrap series.We illustrate the methods by simulations and on a particular rodent abundance time series from Kilpisjrvi,Northern Finland.

MODELING AND PREDICTION CONCERNING TIME SERIES OF FLOOD/DROUGHT RUNS USING THE SELF-EXCITING THRESHOLD AUTOREGRESSIVE MODEL

When linear regressive models such as AR or ARMA model are used for fitting and predicting climatic time series,results are often not sufficiently good because nonlinear variations in the time series.In this paper, a nonlinear self-exciting threshold autoregressive(SETAR)model is applied to modeling and predicting the time series of flood/drought runs in Beijing,which were derived from the graded historical flood/drought records in the last 511 years(1470—1980).The results show that the modeling and predicting with the SETAR model are much better than that of the AR model.The latter can predict the flood/drought runs with a length only less than two years,while the formal can predict more than three-year length runs.This may be due to the fact that the SETAR model can renew the model according to the run-turning points in the process of predic- tion,though the time series is nonstationary.

股指期货基差的非线性特征和均值回复机制研究

只有当股指期货与现货之间的基差足够大到能够补偿交易成本时,指数套利者才会进入市场进行套利.利用三阶段门限自回归模型研究了我国股指期货市场的非线性特征及均值回复机制,并给出了有别于传统持有成本模型的无套利区间.实证结果表明:该模型刻画了股指期货市场的非线性均值回复特征;由模型识别出的门限值反映出我国反向套利成本过高的事实.

循环荷载作用下花岗岩疲劳力学性质及其本构模型

循环荷载作用下岩石力学性质研究对完善岩石力学基本理论和指导相关工程建设具有重要意义。通过花岗岩三轴循环荷载试验,系统研究花岗岩疲劳力学特性,提出花岗岩疲劳力学模型。研究结果表明:(1)岩石残余应变和变形模量与循环次数之间关系与岩石体积变形状态相关;(2)在应力–应变全空间内,花岗岩疲劳性质分为3个区域,不同区域内微观机制不同;(3)岩石疲劳破坏门槛值应为剪缩和剪胀区域分界点对应的峰值偏应力;(4)循环荷载作用下岩石疲劳势有别于单调加载时塑性势,循环荷载作用下岩石表现出比单调加载时更强的抵抗体积变形能力;(5)提出基于内变量理论的岩石疲劳本构模型,试验数据与模拟预测对比显示模型较好地反映出岩石疲劳力学性质。

汇改后人民币汇率波动的非线性特征研究——基于门限自回归TAR模型

文章对自2005年7月人民币汇率制度改革至2008年1月31日的人民币对美元名义汇率波动进行了计量研究,通过建立基于不同时间段汇率数据的门限自回归模型(TAR)可以看到,两年多来的人民币汇率波动存在门限的非线性特征,当升值幅度较大,即大于一定的门限值时,升值的冲击显示出更持久的延续性,体现出了升值预期的作用和升值不断加速的趋势。

微震预警冲击地压的时间序列方法

基于工作面微震事件释能规律的统计分析,研究了微震能量随时间推移而变化的趋势,认为高能量微震事件是冲击地压发生的必要条件。以大同忻州窑煤矿为例,采用时间序列模型中的ARIMA季节性模型和门限自回归模型分别对未来微震事件释放能量进行预测,比较了两种方法的优缺点及适用条件;构建了微震能量方差变化的特征函数,基于此特征函数提出了冲击危险模式的识别方法。研究表明:周期性较为明显的高能量微震事件,ARIMA季节性模型能有效地预测未来微震释能趋势,而门限自回归模型适用于预测高能微震周期性非显著的释能趋势;微震能量方差变化特征函数判别准则能够对冲击地压进行有效预警。

近半世纪宝鸡市干旱特征及模型预测研究

根据1952—2004年西北干旱区宝鸡市降水资料,对年际、年内降水序列进行干旱统计,通过趋势、频率分析法和突变检测方法分析了宝鸡市53a降水的变化规律及干旱变化特征;针对降水序列变化特征建立门限自回归模型,对干旱情灾害进行预测,利用实测资料进行检验研究等分析.结果表明,宝鸡市降水量自1984年后存在减少的趋势.降水序列存在阶段性震荡变化,90年代后旱情发生频率增加,2000年左右降水量序列发生突变,降水资源量略有增加,干旱情况有所缓解.

我国通货膨胀率非线性特征研究

本文应用带单位根的门限自回归模型,对我国1990年以来通货膨胀率的动态路径进行了模拟分析。通过估计和检验发现我国通货膨胀具有明显的非线性特征,模型较好地拟合了通货膨胀的动态调整过程。我国通货膨胀调整存在减速通货膨胀状态、适中通货膨胀状态和加速通货膨胀状态三个区制。适中通货膨胀状态是一个平稳的自回归过程,减速通货膨胀状态、加速通货膨胀状态则是具有单位根的自回归过程,具有自我加速的作用。在不同的区制下,通货膨胀率均有较高的持久性,但中间状态的持久性明显低于其他两种状态。

中国经济增长与通货膨胀间非对称关系的实证分析

利用1985—2012年中国主要宏观经济变量数据,采用门限自回归模型实证检验了通货膨胀对经济增长的非线性影响。研究结果表明,通货膨胀门限效应明显存在;在门限值以上,通货膨胀对经济增长起阻碍作用;在门限值以下,通货膨胀对经济增长起促进作用。在此基础上,利用平滑转换回归模型进一步检验了通货膨胀与经济增长之间的非线性关系,不同模型的实证结果都支持通货膨胀门限效应存在。