基于机器学习LSTM&US模型的消费者信心指数预测研究

消费者信心指数等宏观经济指标具有时间上的滞后效应和动态变化的多维性,不易精确预测。本文基于机器学习长短时间记忆(Long Short-Term Memory,LSTM)神经网络模型,结合大数据技术挖掘消费者信心指数相关网络搜索数据(User Search,US),进而构建一种LSTM&US预测模型,并将其应用于对我国消费者信心指数的长期、中期与短期的预测研究,同时引入多个基准预测模型进行了对比分析。结果发现:引入网络搜索数据能够提高LSTM神经网络模型的预测性能与预测精度;LSTM&US预测模型具有较好的泛化能力,对不同期限的预测效果均较稳定,其预测性能与预测精度均优于其他六种基准预测模型(LSTM、SVR&US、RFR&US、BP&US、XGB&US和LGB&US);预测结果显示本文提出的LSTM&US预测模型具有一定的实用价值,该预测方法为消费者信心指数的预测与预判提供了一种新的研究思路,丰富了机器学习方法在宏观经济指标预测领域中的理论研究。     

面板数据的可加分位回归模型研究与应用

本文在贝叶斯分析的框架下讨论了面板数据的可加模型分位回归建模方法。首先通过低秩薄板惩罚样条展开和个体效应虚拟变量的引进将非参数模型转换为参数模型,然后在假定随机误差项服从非对称Laplace分布的基础上建立了贝叶斯分层分位回归模型。通过对非对称Laplace分布的分解,论文给出了所有待估参数的条件后验分布,并构造了待估参数的 Gibbs抽样估计算法。计算机模拟仿真结果显示,新提出的方法相比于传统的可加模型均值回归方法在估计稳健性上明显占优。最后以消费支出面板数据为例研究了我国农村居民收入结构对消费支出的影响,发现对于农村居民来说,无论是高、中、低消费群体,工资性收入与经营净收入的增加对其消费支出的正向刺激作用更为明显。进一步,相比于高消费农村居民人群,低消费农村居民人群随着收入的增加消费支出上升速度较为缓慢。     

Comparing Two-Stage Segmentation Methods for Choice Data with a One-Stage Latent Class Choice Analysis

Market segmentation is a key concept in marketing research. Identification of consumer segments helps in setting up and improving a marketing strategy. Hence, the need is to improve existing methods and to develop new segmentation methods. We introduce two new consumer indicators that can be used as segmentation basis in two-stage methods, the forces and the dfbetas. Both bases express a subject’s effect on the aggregate estimates of the parameters in a conditional logit model. Further, individual-level estimates, obtained by either estimating a conditional logit model for each individual separately with maximum likelihood or by hierarchical Bayes (HB) estimation of a mixed logit choice model, and the respondents’ raw choices are also used as segmentation basis. In the second stage of the methods the bases are classified into segments with cluster analysis or latent class models. All methods are applied to choice data because of the increasing popularity of choice experiments to analyze choice behavior. To verify whether two-stage segmentation methods can compete with a one-stage approach, a latent class choice model is estimated as well. A simulation study reveals the superiority of the two-stage method that clusters the HB estimates and the one-stage latent class choice model. Additionally, very good results are obtained for two-stage latent class cluster analysis of the choices as well as for the two-stage methods clustering the forces, the dfbetas and the choices.     

外贸对经济增长贡献的计算方法初探

传统上外贸对经济增长的贡献是通过国民收入恒等式计算的,但是这种计算方法却没有把外贸对国内消费和投资的间接作用计算在内。由此在分析传统计算方法存在问题的基础上,就外贸对经济增长的贡献做出了新的界定,提出了一种计算外贸对中国经济增长贡献的新方法。根据这种方法,外贸对中国经济增长的贡献比传统方法的计算结果显著得多。     

Sequential Monitoring for Changes in Models with a Polynomial Trend

This article considers, for the first time, sequential monitoring procedures that detect possible parameter changes in the polynomial trend models. A new class of sequential monitoring procedures that based on generalized fluctuation testing principle is proposed. The asymptotic null distributions and the consistency of the proposed tests are derived and their asymptotic critical values are tabulated. The methods are illustrated and compared in a small simulation study. In particular, we apply the proposed tests to investigate the Chinese commodity retail sales and consumer price indexes. Simulations and applications support our method.     

Discrete distributions and purchasing models

Ihis paper reviews the use of different discrete distributions in the modelling of consumer purchasing behaviour.. A feature of the work is the extensive empirical validation of the models. Some interesting characterizations are briefly discussed.. Ihe paper concludes with some unresolved problems and suggested areas for future research     

将房价因素计入中国CPI的实证研究

近年来中国房价的快速上涨与下跌和相对稳定的CPI不配比问题,引起国人对CPI指标的质疑,已影响到政府统计公信力。运用中国2010年CPI的月度数据与各月CPI分类数据,在借鉴美国使用成本法和澳大利亚获取法的基础上,重新建立中国CPI在住房消费方面的两种核算方法。通过对两种方法核算的CPI数据比较发现,澳大利亚获取法核算符合中国社会经济发展现状,能更真实反映中国城乡居民消费价格水平。鉴于中国房价对CPI的显著影响,中国应该采用获取法核算CPI,将房价直接计入CPI核算中,进而提高CPI指标的公信力。     

线上商品评论反“L”型分布的偏差与纠正

 本文根据中国的商业环境,对淘宝网数据进行实证分析,发现线上商品评论呈近似反“L”型的非对称分布,且评论的均值不是商品质量的无偏估计量。这与现有研究认为线上商品评论呈正态分布并假设商品评价的均值是无偏估计量的结论不同,与国外的实证结果线上评论呈双峰分布亦不相同。为了深入研究线上商品评论存在偏差的原因,以及反“L”型分布的特征,本文构建了理论模型,该模型得出商品评价的均值作为无偏估计量的条件,并进一步揭示出中国电子商务网站“默认好评”机制和消费者主动评价偏差是线上商品评论存在偏差的重要原因。     

Scanner Data and the Treatment of Quality Change in Nonrevisable Price Indexes

The recently developed rolling year GEKS procedure makes maximum use of all matches in the data to construct nonrevisable price indexes that are approximately free from chain drift. A potential weakness is that unmatched items are ignored. In this article we use imputation Törnqvist price indexes as inputs into the rolling year GEKS procedure. These indexes account for quality changes by imputing the “missing prices” associated with new and disappearing items. Three imputation methods are discussed. The first method makes explicit imputations using a hedonic regression model which is estimated for each time period. The other two methods make implicit imputations; they are based on time dummy hedonic and time-product dummy regression models and are estimated on bilateral pooled data. We present empirical evidence for New Zealand from scanner data on eight consumer electronics products and find that accounting for quality change can make a substantial difference.     

Techniques for nonlinear least squares and robust regression

Recently, the authors and others have made considerable progress in developing algorithms for solving certain large-residual nonlinear least-squares problems where Gauss-Newton (GN) methods can be expected to perform poorly. These methods take account of the term in the Hessian ignored by the GN methods and use quasi-Newton procedures to update this term explicitly. This paper reviews these new approaches and discusses how they can be modified to give good performance on nonlinear models with robust loss functions where lack of scale invariance causes several new problems to arise.     

On spatiotemporal prediction for on-line monitoring data

Spatiotemporal prediction is of interest in many areas of applied statistics, especially in environmental monitoring with on-line data information. At first, this article reviews the approaches for spatiotemporal modeling in the context of stochastic processes and then introduces the new class of spatiotemporal dynamic linear models. Further, the methods for linear spatial data analysis, universal kriging and trend surface prediction, are related to the method of spatial linear Bayesian analysis. The Kalman filter is the preferred method for temporal linear Bayesian inferences. By combining the Kalman filter recursions with the trend surface predictor and universal kriging predictor, the prior and posterior spatiotemporal predictors for the observational process are derived, which form the main result of this article. The problem of spatiotemporal linear prediction in the case of unknown first and second order moments is treated as well.     

A nonsymbolic statistics course

A new statistics course Is being taught at the University of Kentucky which does not require students to learn the symbolic language uf statistics (the formulas) Students who will never take a traditional "methods"course in satistics learn to become better consumer of the satistics fed them daily by different news media.other students who realised that munch satistics await them in graduate see this course as a good startingpoint     

Statistical methods for clinical studies in the presence of surrogate end points

The use of surrogate end points has become increasingly common in medical and biological research. This is primarily because, in many studies, the primary end point of interest is too expensive or too difficult to obtain. There is now a large volume of statistical methods for analysing studies with surrogate end point data. However, to our knowledge, there has not been a comprehensive review of these methods to date. This paper reviews some existing methods and summarizes the strengths and weaknesses of each method. It also discusses the assumptions that are made by each method and critiques how likely these assumptions are met in practice.     

最优层界确定方法综述

回顾了分层抽样中层界最优解确定的解析方法和迭代方法,对比并评析了不同方法在计算繁杂程度、有效性等方面的优劣,针对偏态总体分层界限最优解的确定,对几何分层法提出了一种改进思路的变异系数法,并提出了进一步研究最优层界最优解确定方法的思路。     

国际上非基准年购买力平价推算方法评价

 全球国际比较项目大约每隔5年或6年调查一次，有关国际组织应用总量外推法、分类项目外推法、基期滚动法和简缩信息法等方法推算非基准年的PPP数据，以便取得完整的连续时间序列。本文对国际上常用的各种非基准年PPP推算方法和结果作详细分析、评价。     

人工智能方法在旅游预测中的应用及评析

对旅游需求预测的研究始于20世纪60年代，绝大多数研究成果出现于80年代以后。而对此类文献进行整理和研究的成果相对较少，且有明显的缺陷：一是研究深度不够，仅仅是对近期论文的简单罗列；二是由于受研究时间的局限性，未能涉及最新的研究方法。为此，系统地论述诸如粗糙集理论、遗传算法、时间序列方法等，并通过比较得出：人工智能方法在旅游预测方面的应用尽管取得了较好的效果，但也有其自身的缺陷，在以后的研究中应发挥各种分析方法的优点，根据实际分析对象的具体情况选择合适的分析方法，这样才能收到事半功倍的效果。     

中国农民工消费结构特征及市民化趋势分析——基于全国28省1249份有效样本数据检验

摸清农民工的消费结构和消费方式特征对扩大内需、寻求新的消费热点具有重要意义。首先利用2011年全国28省1 249份有效农民工非实验性问卷调查的数据,用SPSS17.0软件整理出农民工当前消费的基本情况,然后利用ELES法,深度分析农民工消费结构并与市民消费进行比较,结果发现:食物、服装、子女教育以及人情开支消费需求弹性比市民大;教育与娱乐消费份额偏低;医疗消费与住房消费特点与市民相似,消费倾向小,但住房支出的价格弹性大。最后,提出了优化农民工消费结构,扩大农民工消费需求,促进农民工深度市民化的政策建议。     

购买力平价（PPP）测算方法研究评述与展望

购买力平价（PPP）可为发行不同货币的国家间比较提供比汇率法更加可靠的数据基础,因此其在国际经济比较领域的应用日益广泛。本文对现有的PPP测算方法按照指数法和随机法两大板块进行了综述,包括了传统的EKS法、最小间隔树法、链式法、星形法、平均价格法、原始CPD法、CPRD法等基本方法,以及此后发展的加权EKS法、特征CPD法、空间CPD法和加权CPD法,简要评述了这些方法的基本思想及各自的优缺点。随后,本文详尽总结了两大类方法的发展脉络,深入探讨了这些方法的适用性问题,并作了研究展望。     

Bayesian Analysis of DSGE Models

This paper reviews Bayesian methods that have been developed in recent years to estimate and evaluate dynamic stochastic general equilibrium (DSGE) models. We consider the estimation of linearized DSGE models, the evaluation of models based on Bayesian model checking, posterior odds comparisons, and comparisons to vector autoregressions, as well as the non-linear estimation based on a second-order accurate model solution. These methods are applied to data generated from correctly specified and misspecified linearized DSGE models and a DSGE model that was solved with a second-order perturbation method.     

Census data quality--a user's view

"This paper presents the perspective of a major user of both decennial and economic [U.S.] census data. It illustrates how these data are used as a framework for commercial marketing research surveys that measure television audiences and sales of consumer goods through retail stores, drawing on Nielsen's own experience in data collection and evaluation. It reviews Nielsen's analyses of census data quality based, in part, on actual field evaluation of census results. Finally, it suggests ways that data quality might be evaluated and improved to enhance the usefulness of these census programs."