Consistent noisy independent component analysis

We study linear factor models under the assumptions that factors are mutually independent and independent of errors, and errors can be correlated to some extent. Under the factor non-Gaussianity, second-to-fourth-order moments are shown to yield full identification of the matrix of factor loadings. We develop a simple algorithm to estimate the matrix of factor loadings from these moments. We run Monte Carlo simulations and apply our methodology to data on cognitive test scores, and financial data on stock returns.     

Epistemological reasons for preferring component analysis to factor analysis

Spearman's and Thomson's mathematical controversy over factor theory was forgotten when it became evident that empirical tetrad-differences bound away from zero (and when empirical evidence argued the need for extracting more factors from a matrix). In fact, both their models lead to zero tetraddifferences. Being more interested in the psychological than in the mathematical aspects of Spearman's model. Thompson remained indifferent to mathematical aspects of multiple factor analysis when Thurstone theorized it. Thus, he did not perceive that his counter-example negated the assumption Thrustone shared the rank of the matrix. The idea that components to be extracted must be equal to the rank of the matrix is not assumed in Hotelling's component model: as a result, this is the first epistemological reason for preferring component analysis to factor analysis. A second epistemological reason is the central theorem of Thurstone's multiple-factor model, which can be criticized because it is an assumption that, the rank of a complete matrix being n, it becomes k when commonalities are in the principal diagonal. This assumption goes against common sense, a fact demonstrated through comparison between residuals after k components have been extracted and after k principal factors have been extracted.     

Principal component analysis of yield curve movements

An important issue in interest rate modeling is the number and nature of the random factors driving the evolution of the yield curve. This paper uses principal component analysis to examine (1) the inherent dimension of historical yield curve changes indicated by the significance of eigenvalues of the covariance matrix, (2) the practical dimension determined by a variance threshold, (3) the shape of the yield curve change associated with the first principal component, and (4) the persistence of this shape over time. We find that although the first two components explain 93% of the sample variation within a 90% confidence interval, the remaining components make statistically significant contribution to the covariance matrix. Consequently, we can establish a practical limit on the dimension only if we are willing to designate a threshold error variance. Further, our results on the persistence of the shape of the yield curve shift associated with the first component depend upon this threshold. If all components are included, the hypothesis that the shape persists between two sample time periods is rejected. On the other hand, if all but the first six components are eliminated, the hypothesis is not rejected.     

积极风险回避与消极风险回避的比较分析

本文分析了风险回避策略作为风险处理方式的优点和缺点,并从感受器、加工器以及记性系统三个方面对积极风险回避策略和消极的风险回避策略进行了比较分析。     

Testing the assumptions behind importance sampling

Importance sampling is used in many areas of modern econometrics to approximate unsolvable integrals. Its reliable use requires the sampler to possess a variance, for this guarantees a square root speed of convergence and asymptotic normality of the estimator of the integral. However, this assumption is seldom checked. In this paper we use extreme value theory to empirically assess the appropriateness of this assumption. Our main application is the stochastic volatility model, where importance sampling is commonly used for maximum likelihood estimation of the parameters of the model.     

Customer satisfaction evaluation by common component and specific weight analysis using a mixed coding system

Quality & Quantity - The Servqual model (Parasuraman et al. in J Mark 49(4):41–50, 1985; J Retail 64:12–40, 1988) involves a set of five dimensions ranked as the most important for...     

A multivariate unobserved component analysis of US housing market

This study explores price dynamics and price relationships in the US housing market with a focus on four regions: Northeast, Midwest, South, and West. It applies a multivariate state-space model to identify the common trends and common cycles in US regional markets. The study finds that the principal source of secular price variability in the Northeast and West markets is due to two common stochastic trends, while a large share of transitional price variability in the Northeast, West and Midwest originates from three common stochastic cycles. The study estimates the relationships between the common unobserved components and economic variables and finds that unemployment, federal funds rate, corporate default risk, economic expansion, unanticipated inflation in the construction market are significant underlying economic phenomena that impact the evolution of the common movements in both the short run and the long run housing dynamics. Authorship is equally shared between the authors.     

Testing identifying assumptions in bivariate probit models

This paper considers the bivariate probit model's identifying assumptions: linear index specification, joint normality of errors, instrument exogeneity, and relevance. First, we develop sharp testable equalities that detect all possible observable violations of the assumptions. Second, we propose an easy-to-implement testing procedure for the model's validity using existing inference methods for intersection bounds. The test achieves correct empirical size and performs well in detecting violations of the conditions in simulations. Finally, we provide a road map on what to do when the bivariate probit model is rejected, including novel bounds for the average treatment effect that relax the normality assumption.     

Production technologies based on combined proportionality assumptions

The assumption of full proportionality is incorporated in the constant returns-to-scale (CRS) technology and allows for proportional scaling of inputs and outputs of production units. The assumption of selective proportionality was recently incorporated in the hybrid returns-to-scale (HRS) technology in which only a subset of outputs is proportional to a subset of inputs. In this paper we develop a production technology that exhibits both the full and selective proportionality at the same time. Real examples of such technology are pointed out. Subject to certain conditions, the DEA models based on this technology provide better discrimination than the CRS and HRS models.

基于主成分分析法的财务风险评价模型

本文在分析了现有财务风险研究理论的基础上引入了主成分分析法的基本原理,构建了主成分分析模型并进行了相关实证研究,验证了模型的科学性和合理性.     

集成式零件尺寸数据采集与分析装置研究

文章介绍了一个集成式尺寸数据采集与分析装置的设计原理和实现方法。该装置采用容栅式数显量具作为尺寸数据采集的前端,C51单片机作为数据采集和处理的中间环节,PC机作为数据分析处理的末端,能完成成批零件常规尺寸的采集、存储和误差统计分析。