On the mathematics of the Jeffreys–Lindley paradox

This paper is concerned with the well known Jeffreys–Lindley paradox. In a Bayesian set up, the so-called paradox arises when a point null hypothesis is tested and an objective prior is sought for the alternative hypothesis. In particular, the posterior for the null hypothesis tends to one when the uncertainty, i.e., the variance, for the parameter value goes to infinity. We argue that the appropriate way to deal with the paradox is to use simple mathematics, and that any philosophical argument is to be regarded as irrelevant.     

红色文化增进铸牢中华民族共同体意识的多维向度解析

红色文化是中国革命建设征程的历史凝结,补钙壮骨,通今溯源,与中华民族共同体意识有着严密的耦合逻辑。在政治上,红色文化有利于坚定中国共产党的领导,明晰铸牢中华民族共同体意识的政治前提;在经济上,红色文化彰显社会主义市场经济的民族互惠指向,夯实铸牢中华民族共同体意识的经济基础;在文化上,红色文化标注社会主义先进文化建设的中国底色,聚合中华民族共同体建设的文化动力;在社会上,红色文化促进各民族交往交流交融,提升铸牢中华民族共同体意识的社会认知;在生态上,红色文化有助于建立人与自然交响协奏的生命共同体,筑牢中华民族共同家园的生态底线。     

基于动态因子Copula模型的行业间系统性风险分析

国际金融市场间的相关关系以及系统性风险受到很多学者的重视，本文则以我国股市的行业指数作为研究对象进行实证研究。通过构建动态因子Copula模型，文章对行业的日收益率数据进行了动态相关性分析，并基于风险预期占比度量了我国行业之间系统性风险的溢出效应。本文分析了2006年1月4日至2016年7月1日的28个行业指数数据，基于GAS动态负荷因子的变化路径来刻画其相关关系，通过风险预期占比来研究行业间的风险溢出效应。研究表明，各个行业指数收益率之间存在较强的关联性。就单个行业来说，化工行业与其他行业关系最为不稳定。就金融与非金融行业而言，金融行业对非金融行业的影响较大且较为平稳。本文所得研究结果可以为投资者和风险管理者在进行决策时提供一定的指导。     

Fixed T dynamic panel data estimators with multifactor errors

This article analyzes a growing group of fixed T dynamic panel data estimators with a multifactor error structure. We use a unified notational approach to describe these estimators and discuss their properties in terms of deviations from an underlying set of basic assumptions. Furthermore, we consider the extendability of these estimators to practical situations that may frequently arise, such as their ability to accommodate unbalanced panels and common observed factors. Using a large-scale simulation exercise, we consider scenarios that remain largely unexplored in the literature, albeit being of great empirical relevance. In particular, we examine (i) the effect of the presence of weakly exogenous covariates, (ii) the effect of changing the magnitude of the correlation between the factor loadings of the dependent variable and those of the covariates, (iii) the impact of the number of moment conditions on bias and size for GMM estimators, and finally (iv) the effect of sample size. We apply each of these estimators to a crime application using a panel data set of local government authorities in New South Wales, Australia; we find that the results bear substantially different policy implications relative to those potentially derived from standard dynamic panel GMM estimators. Thus, our study may serve as a useful guide to practitioners who wish to allow for multiplicative sources of unobserved heterogeneity in their model.     

Mixtures of Gaussian copula factor analyzers for clustering high dimensional data

Mixtures of factor analyzers is a useful model-based clustering method which can avoid the curse of dimensionality in high-dimensional clustering. However, this approach is sensitive to both diverse non-normalities of marginal variables and outliers, which are commonly observed in multivariate experiments. We propose mixtures of Gaussian copula factor analyzers (MGCFA) for clustering high-dimensional clustering. This model has two advantages; (1) it allows different marginal distributions to facilitate fitting flexibility of the mixture model, (2) it can avoid the curse of dimensionality by embedding the factor-analytic structure in the component-correlation matrices of the mixture distribution.An EM algorithm is developed for the fitting of MGCFA. The proposed method is free of the curse of dimensionality and allows any parametric marginal distribution which fits best to the data. It is applied to both synthetic data and a microarray gene expression data for clustering and shows its better performance over several existing methods.     

Model averaging for treatment effect estimation in subgroups

In many clinical trials, biological, pharmacological, or clinical information is used to define candidate subgroups of patients that might have a differential treatment effect. Once the trial results are available, interest will focus on subgroups with an increased treatment effect. Estimating a treatment effect for these groups, together with an adequate uncertainty statement is challenging, owing to the resulting “random high” / selection bias. In this paper, we will investigate Bayesian model averaging to address this problem. The general motivation for the use of model averaging is to realize that subgroup selection can be viewed as model selection, so that methods to deal with model selection uncertainty, such as model averaging, can be used also in this setting. Simulations are used to evaluate the performance of the proposed approach. We illustrate it on an example early‐phase clinical trial.     

马克思主义视阈下的生产力解读

迄今,国内外对生产力的含义众说纷纭,有"劳动生产率与因素综合说";有"人类的能力说";有"人对自然的作用说";有"关系系统说". 正确地把握马克思主义生产力含义,对于我们坚持"解放生产力,发展生产力"的思想和科学发展观有重大的理论意义和实践意义.重新审视马克思的原著,应该把生产力定义为:劳动者通过具体劳动生产出满足人们需要的使用价值时所表征出来的力量.按照这一观点,决定生产力的因素应该包括实体性因素、附着性因素或渗透性因素和运行性因素.     

影响蓖麻种子萌发因子的研究

对蓖麻种子萌发进行温度、水分、播种深度、种皮等处理试验，结果表明，温度和播种深度显著影响蓖麻种子萌发，其中土壤温度低和覆土厚是导致蓖麻种子萌发缓慢的主要因子;种皮的存在有利于菌麻种子萌发;土壤水分含量在10％以上能满足蓖麻种子萌发。     

试论城市化进程中的民族关系--以对临夏市的调查为视点

临夏市作为西北民族地区的一个小城市,在城市化进程中,其民族关系也会受到影响.针对临夏市的实际情况及学术界的研究现状,我们选取临夏市城市化进程中变化较大的几个变量,即民族居住格局的变迁、人口流动的影响、民族通婚的增减以及宗教因素,作为研究重点来衡量城市化进程中的民族关系.在此基础上得出临夏市各民族之间的关系总体上是平等、团结、互助、合作的社会主义新型民族关系,和谐平等的民族关系占据了主导地位的结论,并归纳出城市化进程中临夏市的民族关系具有主从性、敏感性、易发性、历史性、隐蔽性、复杂性等特点,最后提出临夏市各族群众必须解决自身的定位问题,建立临夏市和谐民族关系的良性运转机制.     

Time‐Varying Risk Premium in Large Cross‐Sectional Equity Data Sets

We develop an econometric methodology to infer the path of risk premia from a large unbalanced panel of individual stock returns. We estimate the time‐varying risk premia implied by conditional linear asset pricing models where the conditioning includes both instruments common to all assets and asset‐specific instruments. The estimator uses simple weighted two‐pass cross‐sectional regressions, and we show its consistency and asymptotic normality under increasing cross‐sectional and time series dimensions. We address consistent estimation of the asymptotic variance by hard thresholding, and testing for asset pricing restrictions induced by the no‐arbitrage assumption. We derive the restrictions given by a continuum of assets in a multi‐period economy under an approximate factor structure robust to asset repackaging. The empirical analysis on returns for about ten thousand U.S. stocks from July 1964 to December 2009 shows that risk premia are large and volatile in crisis periods. They exhibit large positive and negative strays from time‐invariant estimates, follow the macroeconomic cycles, and do not match risk premia estimates on standard sets of portfolios. The asset pricing restrictions are rejected for a conditional four‐factor model capturing market, size, value, and momentum effects.