Temporal Disaggregation: Methods,Information Loss,and Diagnostics

This research provides a generalized framework to disaggregate lower-frequency time series and evaluate the disaggregation performance. The proposed framework combines two models in separate stages: a linear regression model to exploit related independent variables in the first stage and a state–space model to disaggregate the residual from the regression in the second stage. For the purpose of providing a set of practical criteria for assessing the disaggregation performance, we measure the information loss that occurs during temporal aggregation while examining what effects take place when aggregating data. To validate the proposed framework, we implement Monte Carlo simulations and provide two empirical studies. Supplementary materials for this article are available online.     

A latent Gaussian Markov random-field model for spatiotemporal rainfall disaggregation

Summary. Rainfall data are often collected at coarser spatial scales than required for input into hydrology and agricultural models. We therefore describe a spatiotemporal model which allows multiple imputation of rainfall at fine spatial resolutions, with a realistic dependence structure in both space and time and with the total rainfall at the coarse scale consistent with that observed. The method involves the transformation of the fine scale rainfall to a thresholded Gaussian process which we model as a Gaussian Markov random field. Gibbs sampling is then used to generate realizations of rainfall efficiently at the fine scale. Results compare favourably with previous, less elegant methods.     

Predicting bias homicide across victim groups: A county-level analysis

Outside several notable exceptions, few studies have examined variations in bias crime occurrences across American communities, and how community-level factors may differentially shape violent and non-violent bias crimes across victim groups. Drawing from ecological theories of crime, this study asks, (1) what are the structural predictors of the likelihood of bias homicide occurrences? and (2) how do structural predictors differ across bias victim groups? To answer these questions, data on bias homicide are derived from the United States Extremist Crime Database (ECDB) for the years 1990 through 2014 and paired with socio- structural variables from the United States Census Bureau. Results are discussed relative to the goals of understanding where fatal bias crimes are more likely to occur as a means of informing law enforcement and policymakers interested in preventing and responding to this specific form of crime.     

多维多层尺度下移动服务质量测度的实证研究

本文基于广泛的文献综述和定量研究,同时结合移动商务的独特性,提出了多维度多层次的移动服务质量模型,并根据服务质量研究的一般文献和移动服务方面的研究成果,提取了43个测度项用来度量模型中的结构变量.之后,从中国移动和中国联通两家公司选取了217份有效样本,通过使用部分分散技术(Partial Disaggregation Technique)对模型进行了三阶段检验.结果显示,模型达到了很好的拟合数据的要求,这使得本文提出的层次结构模型得到强有力的验证.并且.企业形象作为调节变量,对环境质量与服务质量之间和结果质量和服务质量之间的关系具有显著的调节作用,但对于交互质量和服务质量之间的调节作用不显著.     

Dynamic factor analysis with non-linear temporal aggregation constraints

Summary.  The paper estimates an index of coincident economic indicators for the US economy by using time series with different frequencies of observation (monthly and quarterly, possibly with missing values). The model that is considered is the dynamic factor model that was proposed by Stock and Watson, specified in the logarithms of the original variables and at the monthly frequency, which poses a problem of temporal aggregation with a non-linear observational constraint when quarterly time series are included. Our main methodological contribution is to provide an exact solution to this problem that hinges on conditional mode estimation by iteration of the extended Kalman filtering and smoothing equations. On the empirical side the contribution of the paper is to provide monthly estimates of quarterly indicators, among which is the gross domestic product, that are consistent with the quarterly totals. Two applications are considered: the first dealing with the construction of a coincident index for the US economy, whereas the second does the same with reference to the euro area.     

Measurement Problems of Inflation Disaggregation

Why do the three quarterly GNP inflation measures differ so much when they are constructed from the same underlying price data? Algebraically and in tables using data of the second quarter of 1984, it is shown that these differences occur because of quarterly shifts in the composition of the nation's product. Disaggregation of the inflation contributions of the GNP components also makes it clear why, for quarterly analyses, the GNP chain price index is superior to both the implicit GNP deflator and the fixed-weight GNP price index. In particular, the implicit GNP deflator can give severely distorted inflation signals.     

The Relative Deprivation Curve and Its Applications

This article introduces the relative deprivation curve to represent the size distribution of income and wealth. The curve has many useful applications in the measurement of poverty and inequality, which are explored. The methodology developed is then applied to the data obtained from the Australian Household Expenditure Survey, 1975–1976.     

从苏联解体的原因看加强党员意识形态教育的重要性

本文从中外学者对苏联解体原因的分析中探讨了苏共"精英集团"占主流的意识形态对苏联解体所起的重要作用.在现实生活中,人们往往忽略在上层建筑改变过程中起极其重要作用的意识形态力量.事实上,意识形态在社会生活中发挥着巨大的作用.社会革命和社会改革的发生,往往先在社会意识形态领域酝酿.意识形态的影响是长期、深远和隐蔽的.苏联解体带给我们的最大启示就是要切实加强执政党的建设和党员的意识形态教育,这是我们社会主义事业能否取得成功的保证.