Bayesian nonparametric modelling of the link function in the single-index model using a Bernstein–Dirichlet process prior

This paper proposes the use of the Bernstein–Dirichlet process prior for a new nonparametric approach to estimating the link function in the single-index model (SIM). The Bernstein–Dirichlet process prior has so far mainly been used for nonparametric density estimation. Here we modify this approach to allow for an approximation of the unknown link function. Instead of the usual Gaussian distribution, the error term is assumed to be asymmetric Laplace distributed which increases the flexibility and robustness of the SIM. To automatically identify truly active predictors, spike-and-slab priors are used for Bayesian variable selection. Posterior computations are performed via a Metropolis-Hastings-within-Gibbs sampler using a truncation-based algorithm for stick-breaking priors. We compare the efficiency of the proposed approach with well-established techniques in an extensive simulation study and illustrate its practical performance by an application to nonparametric modelling of the power consumption in a sewage treatment plant.     

Robust inference for estimating equations with nonignorably missing data based on SIR algorithm

Nonresponse is a very common phenomenon in survey sampling. Nonignorable nonresponse – that is, a response mechanism that depends on the values of the variable having nonresponse – is the most difficult type of nonresponse to handle. This article develops a robust estimation approach to estimating equations (EEs) by incorporating the modelling of nonignorably missing data, the generalized method of moments (GMM) method and the imputation of EEs via the observed data rather than the imputed missing values when some responses are subject to nonignorably missingness. Based on a particular semiparametric logistic model for nonignorable missing response, this paper proposes the modified EEs to calculate the conditional expectation under nonignorably missing data. We can apply the GMM to infer the parameters. The advantage of our method is that it replaces the non-parametric kernel-smoothing with a parametric sampling importance resampling (SIR) procedure to avoid nonparametric kernel-smoothing problems with high dimensional covariates. The proposed method is shown to be more robust than some current approaches by the simulations.     

Functional and emotional traits of corporate social media message strategies: Behavioral insights from S&P 500 Facebook data

Drawing from literature regarding public engagement, the Elaboration Likelihood Model (ELM), computer-mediated communication research, and emotion psychology, this study examines the effects of companies’ social media communication strategies on public engagement behaviors as indexed by post likes, shares, and comments. Specifically, it investigates how corporate Facebook posts’ functional traits (functional interactivity and vividness) and emotional traits (emotion presence, valence, and strength) impact public engagement online. Through data mining and computer-assisted sentiment analysis of 33,379 posts from 106 Standard & Poor 500 companies’ Facebook accounts, this study finds a negative effect of functional interactivity but a positive effect of vividness on engagement. It also shows that emotional traits overall yield stronger public engagement outcomes. Two-way interactions between emotional and functional features are also detected. Theoretical and practical implications are discussed.     

Closure property of consistently varying random variables based on precise large deviation principles

Let X = {X₁, X₂, …} be a sequence of independent but not necessarily identically distributed random variables, and let η be a counting random variable independent of X. Consider randomly stopped sum S_η = ∑^η_{k = 1}X_k and random maximum S_(η) ? max?{S₀, …, S_η}. Assuming that each X_k belongs to the class of consistently varying distributions, on the basis of the well-known precise large deviation principles, we prove that the distributions of S_η and S_(η) belong to the same class under some mild conditions. Our approach is new and the obtained results are further studies of Kizinevi?, Sprindys, and ?iaulys (2016) and Andrulyt?, Manstavi?ius, and ?iaulys (2017).     

Linear scalar-on-surface random effects regression models

Many research fields increasingly involve analyzing data of a complex structure. Models investigating the dependence of a response on a predictor have moved beyond the ordinary scalar-on-vector regression. We propose a regression model for a scalar response and a surface (or a bivariate function) predictor. The predictor has a random component and the regression model falls in the framework of linear random effects models. We estimate the model parameters via maximizing the log-likelihood with the ECME (Expectation/Conditional Maximization Either) algorithm. We use the approach to analyze a data set where the response is the neuroticism score and the predictor is the resting-state brain function image. In the simulations we tried, the approach has better performance than two other approaches, a functional principal component regression approach and a smooth scalar-on-image regression approach.     

基于高维波动率网络模型的股票市场风险特征研究

波动率是金融风险管理研究的重要内容之一。本文基于复杂网络理论和数据挖掘技术提出股票市场的高维波动率网络模型。首先运用互信息度量不同股票价格波动之间的相关关系,其次对股票市场不同周期下的波动情况建立度的中心势、平均距离、幂律分布等网络拓扑指标,再次根据这些指标利用Prim算法构建出高维波动率网络模型,最后运用Newman-Girvan算法对股票价格波动率的相关性进行分层研究。高维波动率网络模型突破了传统波动率模型关于变量维数的限制,能够在依赖少量假设的基础上,挖掘出多个金融市场主体间的相互关系,反映金融市场的风险特征及网络拓扑性质。实证结果发现：与常用的Pearson相关系数法相比,在互信息框架下,股价波动的非线性相关关系得到了更好的度量;股票市场的整体波动性与个股波动率相关性变化趋势相反,市场处在高波动时期资产组合分散化效果较好;网络中存在少量度数大的关键节点和中心节点,风险通过这些节点可以迅速传递到整个市场;股票市场的运行具有明显的行业聚集现象;网络分层研究进一步直观的展现了风险在层与层之间的传递规律和与之对应的行业特征。高维波动率网络模型为挖掘股票市场的风险特征与管理金融风险提供了一个新的工具。     

金融周期、金融波动与中国经济增长——基于省际面板门槛模型的研究

本文应用面板门槛模型,研究了2005-2017年间我国31个省（市、自治区）金融波动对经济增长的影响。研究发现,随着金融周期所处阶段的变化,金融波动对经济增长会产生显著的非对称性双重门槛效应,主要体现为如下两点：首先,金融周期处于膨胀期、平稳期和萧条期时金融波动对经济增长会产生负向影响,但从影响系数值的大小来看,处于膨胀期时最大,是后两者的2倍之多,处于平稳期时最小且并不显著;其次,分区域的稳健性检验表明,金融发展水平高的区域双重门槛值出现得早,且两个门槛值间的区间要比金融发展水平低的区域宽28%。这些结论说明,经济增长对于金融波动的容忍弹性会随着金融周期所处的阶段而变化,金融发展水平的提高会放大经济增长对金融波动的容忍区间,但也会加速金融周期处于膨胀期时爆发金融危机的可能性,这使得当前我国存在着进一步发展金融水平和严控金融风险的矛盾,对此本文也提出了相应的政策建议。     

Fast and approximate exhaustive variable selection for generalised linear models with APES

We present APproximated Exhaustive Search (APES), which enables fast and approximated exhaustive variable selection in Generalised Linear Models (GLMs). While exhaustive variable selection remains as the gold standard in many model selection contexts, traditional exhaustive variable selection suffers from computational feasibility issues. More precisely, there is often a high cost associated with computing maximum likelihood estimates (MLE) for all subsets of GLMs. Efficient algorithms for exhaustive searches exist for linear models, most notably the leaps‐and‐bound algorithm and, more recently, the mixed integer optimisation (MIO) algorithm. The APES method learns from observational weights in a generalised linear regression super‐model and reformulates the GLM problem as a linear regression problem. In this way, APES can approximate a true exhaustive search in the original GLM space. Where exhaustive variable selection is not computationally feasible, we propose a best‐subset search, which also closely approximates a true exhaustive search. APES is made available in both as a standalone R package as well as part of the already existing mplot package.     

The Social Inclusion and Inequality Nexus: EU versus non‐EU migrants

This study examines the relationship between social inclusions of migrant and income inequality. Both positive and negative impacts of social inclusion on income inequality have been observed in the previous literature. This study specifically considers two types of migration flow: migration flow from EU and migration flow from non‐EU. The aim of this article is twofold: 1) is there a strong association between social inclusion of migrants and income inequality, 2) is there any different impact of social inclusion of two types of migrants (EU vs non‐EU)? Using data from 33 mainly European countries over the period 2003‐2015 and controlling for savings rate, arable land rate and age‐dependency ratio, our results indicate that there is a significant negative relationship between social inclusion and income inequality. In particular, we find that social inclusion from non‐EU migrants significantly reduces income inequality compare with EU migrants.     

Policies for Recruiting Talented Professionals from the Diaspora: India and China Compared

Despite the continuous asymmetrical power relations between the Global North and South, the “global race for talent” is no longer a privilege enjoyed only by developed countries. Comparative analysis of policy content and effectiveness has attracted increasing interest. The examination of policy initiatives resulting in return migration of highly‐skilled migrants from the Global North to the Global South, however, remains inadequate. In this article, we provide a comparative analysis of policies targeting high‐level Chinese and Indian professionals including full‐time returnees. Two key differences in policy instruments and outcomes in the two countries are identified and they have implications for migration policies in other countries in the Global South and North.