The effects of data input errors on subsequent statistical inference

Data input errors can potentially affect statistical inferences, but little research has been published to date on this topic. In the present paper, we report the effect of data input errors on the statistical inferences drawn about population parameters in an empirical study involving 280 students from two Polish universities, namely the Warsaw University of Life Sciences – SGGW and the University of Information Technology and Management in Rzeszow. We found that 28% of the students committed at least one data error. While some of these errors were small and did not have any real effect, a few of them had substantial effects on the statistical inferences drawn about the population parameters.     

Wavelet‐Based Testing for Serial Correlation of Unknown Form in Panel Models

Wavelet analysis is a new mathematical method developed as a unified field of science over the last decade or so. As a spatially adaptive analytic tool, wavelets are useful for capturing serial correlation where the spectrum has peaks or kinks, as can arise from persistent dependence, seasonality, and other kinds of periodicity. This paper proposes a new class of generally applicable wavelet‐based tests for serial correlation of unknown form in the estimated residuals of a panel regression model, where error components can be one‐way or two‐way, individual and time effects can be fixed or random, and regressors may contain lagged dependent variables or deterministic/stochastic trending variables. Our tests are applicable to unbalanced heterogenous panel data. They have a convenient null limit N(0,1) distribution. No formulation of an alternative model is required, and our tests are consistent against serial correlation of unknown form even in the presence of substantial inhomogeneity in serial correlation across individuals. This is in contrast to existing serial correlation tests for panel models, which ignore inhomogeneity in serial correlation across individuals by assuming a common alternative, and thus have no power against the alternatives where the average of serial correlations among individuals is close to zero. We propose and justify a data‐driven method to choose the smoothing parameter—the finest scale in wavelet spectral estimation, making the tests completely operational in practice. The data‐driven finest scale automatically converges to zero under the null hypothesis of no serial correlation and diverges to infinity as the sample size increases under the alternative, ensuring the consistency of our tests. Simulation shows that our tests perform well in small and finite samples relative to some existing tests.     

论经济学中数学方法的运用

随着经济学的发展,数学方法在经济学中的运用日益广泛.当今,数学方法已在经济学研究方法中占据重要地位,起到了十分重要的作用。我们要辩证地对待数学在经济学研究中所发挥的作用,既不能神化,也不能全盘否定。     

中国基础设施投资对经济增长波动的冲击效应分析

基础设施是经济发展的重要影响因素，而“基础设施投资促进经济发展”这一主流观点已得到广泛共识，但人们对经济增长与基础设施的探讨仅局限于总量的分析框架，并未对基础设施的类型加以区分，更没发现经济发展的不同阶段对基础设施的需求结构也是不同的。而以“基础设施投资促进经济发展”为出发点，将基础设施投资细分为生产性、生活性、社会性基础设施投资，并分别讨论其对经济的影响。通过邹检验发现：自1994年以来，基础设施投资冲击改变了中国经济增长的特征。其结论是生产性、生活性基础设施对经济增长的影响是明确的、同步的，但社会性基础设施对经济增长的作用是不确定的。因此，加强社会性基础设施投资是一个渐进地逐步推进的过程。     

基础建设与经济状况的相关性研究——以四川省为例

影响四川省经济发展的因素众多,基础设施建设就是其中之一。通过＂了解基础设施建设与经济发展状况的关系＂—＂分析四川省基础设施发展状况＂—＂研究四川省基础设施建设对其经济发展的具体影响＂—＂总结问题并提出相关建议＂这一思路,对四川省的基础设施建设以及经济发展状况逐步进行实证分析,得出两者明显相关的结论,并给出相关政策建议。     

ECX碳排放期货与欧美股市联动性研究——基于DCC-MVGARCH模型的实证分析

碳排放交易是人类利用市场力量解决全球气候变暖问题的尝试,分析碳期货市场与资本市场的动态相关性,有利碳排放交易策略的制定。本文利用DCC-MVGARCH模型,分析主要股票市场与EU ETS碳排放期货价格的联动关系。实证结果表明,主要的股票市场对EU ETS期货价格具有直接或间接的联动关系,且是单方面的引导关系,印证了股市反映实体经济,实体经济的波动影响碳排放期货市场的需求这样一个动态联动性。     

High dimensional asymptotics for the naive Hotelling T2 statistic in pattern recognition

Abstract

This paper examines the high dimensional asymptotics of the naive Hotelling T² statistic. Naive Bayes has been utilized in high dimensional pattern recognition as a method to avoid singularities in the estimated covariance matrix. The naive Hotelling T² statistic, which is equivalent to the estimator of the naive canonical correlation, is a statistically important quantity in naive Bayes and its high dimensional behavior has been studied under several conditions. In this paper, asymptotic normality of the naive Hotelling T² statistic under a high dimension low sample size setting is developed using the central limit theorem of a martingale difference sequence.     

小康社会独立指标评价体系及评价标准

党的十六大提出了全面建设小康社会的奋斗目标。基于此,我们以1995年国家制定的《全国小康生活水平基本标准》中的原有指标为基础,综合考察社会各方面因素,选取一些新的宏观指标,通过指标间的相关分析,找出不相关的指标或不相关指标组合,建立一套科学、系统和具有时代特征的指标评价体系。同时,以我国社会的发展实际为基础确定相应的标准,用于衡量"全面建设小康社会"的实现进程和进行预测。     

Regularization in dynamic random-intercepts models for analysis of longitudinal data

This paper addresses the problem of simultaneous variable selection and estimation in the random-intercepts model with the first-order lag response. This type of model is commonly used for analyzing longitudinal data obtained through repeated measurements on individuals over time. This model uses random effects to cover the intra-class correlation, and the first lagged response to address the serial correlation, which are two common sources of dependency in longitudinal data. We demonstrate that the conditional likelihood approach by ignoring correlation among random effects and initial responses can lead to biased regularized estimates. Furthermore, we demonstrate that joint modeling of initial responses and subsequent observations in the structure of dynamic random-intercepts models leads to both consistency and Oracle properties of regularized estimators. We present theoretical results in both low- and high-dimensional settings and evaluate regularized estimators' performances by conducting simulation studies and analyzing a real dataset. Supporting information is available online.     

Statistical considerations for design and analysis of stability,comparability and formulation tests

This article considers designed experiments for stability, comparability, and formulation testing that are analyzed with regression models in which the degradation rate is a fixed effect. In this setting, we investigate how the number of lots, the number of time points and their locations affect the precision of the entities of interest, leverages of the time points, detection of non-linearity and interim analyses. This investigation shows that modifying time point locations suggested by ICH for stability studies can significantly improve these objectives. In addition, we show that estimates of precision can be biased when a regression model that assumes independent measurements is used in the presence of within-assay session correlation. This bias can lead to longer shelf life estimates in stability studies and loss of power in comparability studies. Mixed-effect models that take into account within-assay session correlation are shown to reduce this bias. The findings in this article are obtained from well known statistical theory but provide valuable practical advice to scientists and statisticians designing and interpreting these types of experiments.