On hysteretic vector autoregressive model with applications

This paper proposes a new hysteretic vector autoregressive (HVAR) model in which the regime switching may be delayed when the hysteresis variable lies in a hysteresis zone. We integrate an adapted multivariate Student-t distribution from amending the scale mixtures of normal distributions. This HVAR model allows for a higher degree of flexibility in the degrees of freedom for each time series. We use the proposed model to test for a causal relationship between any two target time series. Using posterior odds ratios, we overcome the limitations of the classical approach to multiple testing. Both simulated and real examples herein help illustrate the suggested methods. We apply the proposed HVAR model to investigate the causal relationship between the quarterly growth rates of gross domestic product of United Kingdom and United States. Moreover, we check the pairwise lagged dependence of daily PM2.5 levels in three districts of Taipei.     

汇率货币模型的非线性协整关系检验——基于深度GRU神经网络

本文采用深度门控循环单元（GRU）神经网络探讨三种汇率货币模型（弹性价格、前瞻性和实际利率差模型）的非线性协整关系。GRU技术在深度学习中具有智能记忆、自主学习和强逼近能力等优点。为此，本文运用该技术对6组典型浮动汇率制国别数据进行了非线性Johansen协整检验。结果表明，汇率与宏观经济基本面之间存在非线性协整关系，从而说明了货币模型在非线性条件下的有效性，以及先进的深度学习工具在检验经济理论中的优势。     

Random effects regression mixtures for analyzing infant habituation

Random effects regression mixture models are a way to classify longitudinal data (or trajectories) having possibly varying lengths. The mixture structure of the traditional random effects regression mixture model arises through the distribution of the random regression coefficients, which is assumed to be a mixture of multivariate normals. An extension of this standard model is presented that accounts for various levels of heterogeneity among the trajectories, depending on their assumed error structure. A standard likelihood ratio test is presented for testing this error structure assumption. Full details of an expectation-conditional maximization algorithm for maximum likelihood estimation are also presented. This model is used to analyze data from an infant habituation experiment, where it is desirable to assess whether infants comprise different populations in terms of their habituation time.     

The relationship between lifetime health trajectories and socioeconomic attainment in middle age

A large literature demonstrates the direct and indirect influence of health on socioeconomic attainment, and reveals the ways in which health and socioeconomic background simultaneously and dynamically affect opportunities for attainment and mobility. Despite an increasing understanding of the effects of health on social processes, research to date remains limited in its conceptualization and measurement of the temporal dimensions of health, especially in the presence of socioeconomic circumstances that covary with health over time. Guided by life course theory, we use data from the British National Child Development Study, an ongoing panel study of a cohort born in 1958, to examine the association between lifetime health trajectories and socioeconomic attainment in middle age. We apply finite mixture modeling to identify distinct trajectories of health that simultaneously account for timing, duration and stability. Moreover, we employ propensity score weighting models to account for the presence of time-varying socioeconomic factors in estimating the impact of health trajectories. We find that, when poor health is limited to the childhood years, the disadvantage in socioeconomic attainment relative to being continuously healthy is either insignificant or largely explained by time-varying socioeconomic confounders. The socioeconomic impact of continuously deteriorating health over the life course is more persistent, however. Our results suggest that accounting for the timing, duration and stability of poor health throughout both childhood and adulthood is important for understanding how health works to produce social stratification. In addition, the findings highlight the importance of distinguishing between confounding and mediating effects of time-varying socioeconomic circumstances.     

A new generalized Weibull distribution in income economic inequality curves

Researchers have been developing various extensions and modified forms of the Weibull distribution to enhance its capability for modeling and fitting different data sets. In this note, we investigate the potential usefulness of the new modification to the standard Weibull distribution called odd Weibull distribution in income economic inequality studies. Some mathematical and statistical properties of this model are proposed. We obtain explicit expressions for the first incomplete moment, quantile function, Lorenz and Zenga curves and related inequality indices. In addition to the well-known stochastic order based on Lorenz curve, the stochastic order based on Zenga curve is considered. Since the new generalized Weibull distribution seems to be suitable to model wealth, financial, actuarial and especially income distributions, these findings are fundamental in the understanding of how parameter values are related to inequality. Also, the estimation of parameters by maximum likelihood and moment methods is discussed. Finally, this distribution has been fitted to United States and Austrian income data sets and has been found to fit remarkably well in compare with the other widely used income models.     

Relationship between the weighted distributions and some inequality measures

In this paper, we study the relationships between the weighted distributions and the parent distributions in the context of Lorenz curve, Lorenz ordering and inequality measures. These relationships depend on the nature of the weight functions and give rise to interesting connections. The properties of weighted distributions for general weight functions are also investigated. It is shown how to derive and to determine characterizations related to Lorenz curve and other inequality measures for the cases weight functions are increasing or decreasing. Some of the results are applied for special cases of the weighted distributions. We represent the reliability measures of weighted distributions by the inequality measures to obtain some results. Length-biased and equilibrium distributions have been discussed as weighted distributions in the reliability context by concentration curves. We also review and extend the problem of stochastic orderings and aging classes under weighting. Finally, the relationships between the weighted distribution and transformations are discussed.     

Robust Bayesian nonlinear mixed‐effects modeling of time to positivity in tuberculosis trials

Early phase 2 tuberculosis (TB) trials are conducted to characterize the early bactericidal activity (EBA) of anti‐TB drugs. The EBA of anti‐TB drugs has conventionally been calculated as the rate of decline in colony forming unit (CFU) count during the first 14 days of treatment. The measurement of CFU count, however, is expensive and prone to contamination. Alternatively to CFU count, time to positivity (TTP), which is a potential biomarker for long‐term efficacy of anti‐TB drugs, can be used to characterize EBA. The current Bayesian nonlinear mixed‐effects (NLME) regression model for TTP data, however, lacks robustness to gross outliers that often are present in the data. The conventional way of handling such outliers involves their identification by visual inspection and subsequent exclusion from the analysis. However, this process can be questioned because of its subjective nature. For this reason, we fitted robust versions of the Bayesian nonlinear mixed‐effects regression model to a wide range of TTP datasets. The performance of the explored models was assessed through model comparison statistics and a simulation study. We conclude that fitting a robust model to TTP data obviates the need for explicit identification and subsequent “deletion” of outliers but ensures that gross outliers exert no undue influence on model fits. We recommend that the current practice of fitting conventional normal theory models be abandoned in favor of fitting robust models to TTP data.     

Does biomass energy consumption reduce total energy CO2 emissions in the US?

This study investigates the causal relationship between total biomass energy consumption, total energy CO₂ emissions, and GDP in the United States for the period January 1973–December 2016 by employing a directed acyclic graph (DAG) techniques and the Autoregressive Distributed Lag (ARDL) model. Additionally, this paper examines the existence of the environmental Kuznets curve (EKC) hypothesis. The result of the DAG reveals that total biomass energy consumption and GDP have a unidirectional contemporaneous causal relationship with total energy CO₂ emissions. Based on the results of the ARDL, we find that a 1% increase in per capita total biomass energy consumption causes a 0.65% reduction in per capita total energy CO₂ emissions in the long-run. This finding implies that expanding the usage of biomass is one way to reduce and control greenhouse gases in the US. Moreover, we find that the inverted U-shaped EKC hypothesis is satisfied for the US case. Findings from this study suggest that energy policies should stimulate an increase of biomass production for reducing total energy CO₂.     

中国高耗能行业能源消费的贝叶斯非对称影响效应研究

构建双机制的非线性FAVAR模型,从贝叶斯角度出发,研究中国高耗能行业能源消费对宏观经济以及环境污染因素的影响效应。利用中国高耗能行业的能源消费以及废气排放数据,结合贝叶斯方法进行非线性FAVAR模型的参数估计,结果发现中国高耗能行业的能源消费增加和减少对国内生产总值、外商直接投资等宏观经济因素以及主要环境污染因素都具有非对称影响效应。但是,高耗能能源消费增加或减少的冲击一般都是短期效应,不具有长期影响效应。     

新型职业农民:成长轨迹、影响因素及培育策略——基于19个省市39位新型职业农民生命历程的分析

通过对全国19个省市39位优秀新型职业农民的生命历程进行分析,发现其成长轨迹呈现出折勾型、倒梯型、V字型、阶梯型以及N字型五种类型,并且不同来源、不同世代的新型职业农民成长轨迹具有一定的差异性。究其原因,新型职业农民终身发展的动力支撑显得不足;新型职业农民的个体主观能动性得到了充分的发挥;政府扶持与引领作用有待继续加强;新型职业农民培育时机并未得到恰当的把握;影响新型职业农民成长的相关利益主体分布比较广泛。针对以上情况,提出新型职业农民的培育策略:强化政府主导力量,增强新型职业农民职业吸引力;吸收年轻优秀群体,壮大新型职业农民的新生力量;重视人地实际需求,把握新型职业农民培育的时机。