Piecewise FARIMA models for long-memory time series

We consider the problem of modelling a long-memory time series using piecewise fractional autoregressive integrated moving average processes. The number as well as the locations of structural break points (BPs) and the parameters of each regime are assumed to be unknown. A four-step procedure is proposed to find out the BPs and to estimate the parameters of each regime. Its effectiveness is shown by Monte Carlo simulations and an application to real traffic data modelling is considered.     

A robust test for autocorrelation in the presence of a structural break in variance

It has been known that when there is a break in the variance (unconditional heteroskedasticity) of the error term in linear regression models, a routine application of the Lagrange multiplier (LM) test for autocorrelation can cause potentially significant size distortions. We propose a new test for autocorrelation that is robust in the presence of a break in variance. The proposed test is a modified LM test based on a generalized least squares regression. Monte Carlo simulations show that the new test performs well in finite samples and it is especially comparable to other existing heteroskedasticity-robust tests in terms of size, and much better in terms of power.     

Multilevel models for longitudinal data

Summary.  Repeated measures and repeated events data have a hierarchical structure which can be analysed by using multilevel models. A growth curve model is an example of a multilevel random-coefficients model, whereas a discrete time event history model for recurrent events can be fitted as a multilevel logistic regression model. The paper describes extensions to the basic growth curve model to handle auto-correlated residuals, multiple-indicator latent variables and correlated growth processes, and event history models for correlated event processes. The multilevel approach to the analysis of repeated measures data is contrasted with structural equation modelling. The methods are illustrated in analyses of children's growth, changes in social and political attitudes, and the interrelationship between partnership transitions and childbearing.     

Concept mapping as an approach for expert-guided model building: The example of health literacy

Concept mapping served as the starting point for the aim of capturing the comprehensive structure of the construct of ‘health literacy.’ Ideas about health literacy were generated by 99 experts and resulted in 105 statements that were subsequently organized by 27 experts in an unstructured card sorting. Multidimensional scaling was applied to the sorting data and a two and three-dimensional solution was computed. The three dimensional solution was used in subsequent cluster analysis and resulted in a concept map of nine “clusters”: (1) self-regulation, (2) self-perception, (3) proactive approach to health, (4) basic literacy and numeracy skills, (5) information appraisal, (6) information search, (7) health care system knowledge and acting, (8) communication and cooperation, and (9) beneficial personality traits. Subsequently, this concept map served as a starting point for developing a “qualitative” structural model of health literacy and a questionnaire for the measurement of health literacy. On the basis of questionnaire data, a “quantitative” structural model was created by first applying exploratory factor analyses (EFA) and then cross-validating the model with confirmatory factor analyses (CFA). Concept mapping proved to be a highly valuable tool for the process of model building up to translational research in the “real world”.     

Personal networks typologies: A structural approach

Building typologies allows to compare networks on multiple dimensions, and to approach a generalization grounded on empirical data. In this article, we present a typology of personal networks only based on indicators related to the structure of relations between alters. It is designed from very detailed data on young French people who were involved in a longitudinal study. Our typology mobilizes a small number of indicators to discriminate the types that compose it. In so doing, we intend to make it applicable to various surveys.     

Euro area inflation as a predictor of national inflation rates

The stability of inflation differentials is an important condition for the smooth working of a currency area, such as the European Economic and Monetary Union. In the presence of stability, changes in national inflation rates, while holding Euro area inflation fixed contemporaneously, should be only transitory. If this is the case, the rate of inflation of the whole area can also be interpreted as a predictor, at least in the long-run, of the different national inflation rates. However, in this paper we show that this condition is satisfied only for a small number of countries, including France and Italy. Better convergence results for inflation differentials are, instead, found for the USA. Some policy implications are drawn for the Eurozone.     

Reinforced structural holes

Holes in social structure are variably reinforced by the social organization around the hole. The more reinforced the hole, the greater the difficulty in bridging it, but the more likely a successful bridge will carry information novel, and so potentially valuable, to people on the other side. To study how reinforcement varies with access to structural holes, and the achievement associated with access, I propose a measure of access to reinforced structural holes (RSH), and present results predicting achievement in an integrated banker organization and a balkanized supply-chain organization. In both study populations, the people who have access to structural holes also have access to reinforced structural holes, and all measures of access have a statistically significant association with achievement. However, there is no consistent prediction advantage from incorporating reinforcement in measures of access to structural holes. The reinforced-holes measure predicts compensation better or as well as network constraint and betweenness, but is weaker or no better than a count of nonredundant contacts. I do not infer from the results a rank-order of alternative measures so much as substitutability. I expect achievement to be associated with access to structural holes, but I expect the association to vary across alternative measures depending on how achievement is achieved in a specific population.     

Social network and family business: Uncovering hybrid family firms

What does it mean for a private enterprise in China to be embedded in a family? Our purpose here is twofold: (1) use social network analysis to describe what it means for a firm to be embedded in a family, (2) reveal from the application a new kind of firm, not family, yet akin to family. Armed with data on a large probability sample of private enterprises — a third of which meet ownership and employment criteria of being family businesses — we uncover a category of “hybrid family firms” that look modern in the style of firms that exclude family, but operate socially in ways similar to family firms. Our conclusion from summary statistics on the sample is that there are no differences in average performance level or network advantage for the three categories of businesses: family firms, hybrid family firms, and family-excluded firms. The fact that CEOs of family firms and hybrid family firms more often turn to family as key business contacts is a fact about network composition that raises no question about network mechanisms. Whether the CEO turns to more or fewer family contacts, government help is more likely with stronger political connections, and business success and survival are more likely with a large, open network. That said, the look-modern, act-traditional hybrid family firms stand alone in prospering with a CEO embedded in a closed business network. Recognition of hybrid family firms adds to the literature’s illustrations of social network analysis used to distinguish types of businesses and business people, and extends the population of organizations within which governance and strategy are likely to be better understood when viewed through a family logic.     

中国城镇新增住房需求规模的测算与分析

本文围绕城镇家庭户自然增长、城镇家庭户机械增长和城镇存量住房拆除三项需求来源,设计了基于人口普查等公开统计数据定量测算城镇新增住房需求规模的系统性方法,对2001—2010年和2011—2015年间全国和各省份城镇新增住房需求规模进行测算与分析。在此期间新增住房供需关系经历了从基本均衡向供过于求的变化,同时存量住房拆除引致的被动需求取代城镇家庭户自然增长和机械增长引致的主动需求,成为新增住房需求的最主要来源。东、中、西部省份在新增需求规模、新增供需比、需求结构等方面表现出明显差异。本文还进一步对2021—2030年的发展趋 势进行了定量预测。由于三项需求来源均趋于下降,2021—2025年和2026—2030年间全国年均城镇新增住房需求规模预计将较2011—2015年间分别下降33%和53%。本文设计的城镇新增住房需求规模测算方法和预测思路能够为各级政府“十四五”住房发展规划编制、房地产企业战略制定等提供参考。     

基于局部社团结构平衡的双模符号网络链路预测研究

随着互联网产业的高速发展,双模符号网络已经成为一类常见的复杂网络,然而针对此 类网络的分析较少。本文在传统非符号网络局部社团理论和符号网络结构平衡理论的基础上,首次提出了双模符号网络下的局部社团理论。这一理论不仅考虑了符号网络中共同邻居的信息,还引入了共同邻居间存在的连接。进一步地,本文推导出符号网络中基于局部社团信息的加权平衡回路增益指数,该指标可以表示双模符号网络中用户节点和产品节点间的符号关系。为了将该指标更好地应用于双模 符号网络链路预测问题,本文提出了加权平衡回路增益分类器算法。实验结果表明,相比其他经典链路预测算法,新算法具有更好的预测能力。