Horwitz's rule, transforming both sides and the design of experiments for mechanistic models

Summary. The paper develops methods for the design of experiments for mechanistic models when the response must be transformed to achieve symmetry and constant variance. The power transformation that is used is partially justified by a rule in analytical chemistry. Because of the nature of the relationship between the response and the mechanistic model, it is necessary to transform both sides of the model. Expressions are given for the parameter sensitivities in the transformed model and examples are given of optimum designs, not only for single-response models, but also for experiments in which multivariate responses are measured and for experiments in which the model is defined by a set of differential equations which cannot be solved analytically. The extension to designs for checking models is discussed.     

基于马尔可夫链的产品市场保有量预测模型

售后服务机构为了准确地准备库存,都需要预测产品的市场保有量。基于马尔可夫链建立的状态概率矩阵模型,与抽样调查、时间序列法、回归分析法等方法相比,更能准确预测市场产品保有量。与传统产品相比较,更新换代速度快的电子产品,要预测其市场保有量,更需要利用状态概率矩阵模型。     

美元、日元、英镑的中长期汇率预测方法研究

文章运用组合预测方法对中长期汇率走势进行分析 ,并根据贝特斯 (Bates)和格朗格 (Granger)提出的组合预测方法 ,对美元兑日元、美元兑英镑的中长期汇率进行了预测 ,提出组合预测为一种较好的方法。     

投资者信心和中国宏观经济波动——基于MIDAS混频模型

将投资者信心指数引入MIDAS混频模型之中,可以考察新冠疫情背景下投资者信心对于中国宏观经济波动的影响。基于投资者信心指数和“三驾马车”对我国季度GDP增长率进行预测,通过实证发现:引入投资者信心指数的MIDAS混频模型在预测精准度方面和基准模型进行比较,预测精准度高于未加入投资者信心的模型,均方根残差比值更小;在多元MIDAS混频模型之中,加入投资者信心指数的回归模型对我国GDP的实时预报和短期预测结果更加稳定,可为决策者提供更加精确的参考区间;宏观经济的波动对投资者信心指数变化反应,和投资、消费和进出口相比是最灵敏的。这为定量追踪宏观经济波动提供了一个新的视角。     

改革目标和企业风险度测定─—现代企业制度建设的关键

在建立现代企业制度的探索中,原有的“承包制”在实践中已暴露它的不足,与改革的深入不相适应。企业的生机在于竞争和风险。在商品经济条件下,企业经营实际上就是经营者运用创新手段,将受损风险转化为超额收获。而企业生产经营所存在的诸多不确定因素,可以通过概率论中的中心极限定理和经验,借鉴计划评审法（ＰＥＲＴ）中概率估算法对企业的经验风险度作出有价值的测定。     

中国FDI进入模式的影响因素

本文综合交易费用理论和谈判力量理论分析框架,探讨了FDI进入中国市场模式(合资／独资)的影响因素。应用两分Logit模型进行实证分析,结果显示区位因素、来源地因素、投资项目的经营期限以及投资规模都对FDI进入中国市场模式产生了重要的影响,而产业因素的影响则统计不显著。     

Multiscale Combined Forecast of Carbon PriceBased on Mixed Structure Data

Accurate forecast of the carbon trading price is of great significance in promoting the scientific and rational development of carbon trading market. Therefore, this paper proposes a multi-scale combined forecasting method for carbon price based on mixed structure data. First, the Google Index is used to extract the unstructured data related to the carbon price.The dimensions of unstructured data are reduced based on principal component analysis. Then, EMD is employedto the structured data,unstructured data and the carbon trading price to obtain different IMFs, which are reconstructed by the Fine-to-Coarse technique to get low, high frequency sequence and trend sequence. Furthermore, the three items are predicted respectively by using ARIMA, PLS and neural networks according to the features of each scale in time series. Finally, the forecasting results are summed to get the carbon price forecast sequence. The proposed method is used to forecast carbon price in EU. The empirical results show that the prediction accuracy of the model is higher than that of the single prediction method and the prediction method that time series aren’t decomposed by EMD, which is of great applicability.     

1906-1908年的郑孝胥与预备立宪公会

考虑到我国未来年度的OD分布预测中的路网构成变化、区域经济布局变动、区域经济增长速度差异等一些具有时变性和特殊性因素，采用在“四阶段法”运量预测基础上改进而成的“三阶段法”进行高速铁路短期客运量预测。首先采用组合预测模型进行趋势运量预测，然后采用多元LOGIT模型进行方式分担，最后采用弹性系数诱增模型进行诱增运量预测。以京沪高铁为例，采用“三阶段法”预测了2014年和2015年京沪高铁本线及跨线单向客流量。     

Small area estimation strategies for large population surveys: a comparison of design and model-based methods

Small area estimation (SAE) concerns with how to reliably estimate population quantities of interest when some areas or domains have very limited samples. This is an important issue in large population surveys, because the geographical areas or groups with only small samples or even no samples are often of interest to researchers and policy-makers. For example, large population health surveys, such as Behavioural Risk Factor Surveillance System and Ohio Mecaid Assessment Survey (OMAS), are regularly conducted for monitoring insurance coverage and healthcare utilization. Classic approaches usually provide accurate estimators at the state level or large geographical region level, but they fail to provide reliable estimators for many rural counties where the samples are sparse. Moreover, a systematic evaluation of the performances of the SAE methods in real-world setting is lacking in the literature. In this paper, we propose a Bayesian hierarchical model with constraints on the parameter space and show that it provides superior estimators for county-level adult uninsured rates in Ohio based on the 2012 OMAS data. Furthermore, we perform extensive simulation studies to compare our methods with a collection of common SAE strategies, including direct estimators, synthetic estimators, composite estimators, and Datta GS, Ghosh M, Steorts R, Maples J.'s [Bayesian benchmarking with applications to small area estimation. Test 2011;20(3):574–588] Bayesian hierarchical model-based estimators. To set a fair basis for comparison, we generate our simulation data with characteristics mimicking the real OMAS data, so that neither model-based nor design-based strategies use the true model specification. The estimators based on our proposed model are shown to outperform other estimators for small areas in both simulation study and real data analysis.     

证券分析师性别、盈余预测偏差与纠偏能力

证券分析师个人特征是其能力的重要表征.文章选取2007-2014年我国资本市场相关数据,研究了证券分析师的性别对盈余预测偏差的影响,以及盈余预测偏差的时间序列特征,实证结果显示:第一,整体上男性分析师的盈余预测偏差大于女性分析师;第二,男性分析师对于盈余预测偏差的纠正能力高于女性分析师;第三,随着对公司跟踪时间的增加,男性分析师的盈余预测偏差下降速度更快.研究结果说明,对于分析师跟踪时间较短的公司,女性分析师的盈余预测偏差较小,而对于受分析师长期跟踪的公司,男性分析师的盈余预测结果更准确.研究结论对投资者依据分析师性别判定其盈利预测准确性具有重要意义.