Another Approach to Data Envelopment Analysis in Noisy Environments: DEA+

In this paper a DEA+ labeled approach for efficiency measurement in the stochastic case is presented along with a consistency proof and some preliminary evidence illustrating the small sample performance. DEA+ can basically handle multi-output technologies like standard DEA but allows to filter noise, that might have disturbed production and unlike a related approach does not require panel data. Consistency of DEA+ relies on the assumption of i.i.d. distributed and bounded noise and requires radial efficiency measurement. First Monte Carlo experiments show that a DEA+ based average inefficiency estimator performs well for samples of size n=100 in one-output, two-input settings compared to the corresponding Stochastic Frontier Estimator. Sensitivity of DEA+ performance with respect to parametrization of noise is weak, but higher noise contribution requires much larger sample size for satisfactory results.     

Testing the CAPM revisited

This paper re-examines the tests of the Sharpe–Lintner Capital Asset Pricing Model (CAPM). The null that the CAPM intercepts are zero is tested for ten size-based stock portfolios and for twenty five book-to-market sorted portfolios using five-year, ten-year and longer sub-periods during 1965–2004. The paper shows that the evidence for rejecting the CAPM on statistical grounds is weaker than the consensus view suggests, and highlights the pitfalls of testing multiple hypotheses with the conventional heteroskedasticity and autocorrelation robust (HAR) test with asymptotic P-values. The conventional test rejects the null for almost all sub-periods, which is consistent with the evidence in the literature. By contrast, the null is not rejected for most of the sub-periods by the new HAR tests developed by Kiefer et al. (2000), Kiefer and Vogelsang (2005), and Sun et al. (2008).     

浅析再创业中的企业资本运营

企业资本运营是一种战略行为,是以有效投资或有效理财为前提,以最佳成本和合适风险为基础,力求企业利润最大化,按照市场经济的一般规律来经营优化企业资本或生产要素的经济活动。其基本特征主要表现为：以资本导向为核心,采用价值形态管理模式,实行开放式经营,注重资本的流动性。它往往可以通过企业资产重组、买(借)”壳”上市、兼并与收购、吸收合并、债务重组和破产重组等多种多样基本模式和行为表现出来。     

平衡计分法在企业信用评级中的应用

基于平衡计分卡基本原理、现代信用理论和财务分析理论,从被评企业战略层面出发,对被评企业从财务、客户、内部运营和学习成长四个方面开展全方位的分析评判,设计了平衡计分法下信用评级指标体系和信用评分方法,并将其应用于一个实例分析。     

快速校正天波雷达电离层相位污染的改进方法

时变的电离层会对天波超视距雷达(OTHR)回波信号相位进行调制,产生相位污染,导致回波谱展宽。最大似然估计(MLE)法具有比相位梯度法更佳的污染校正效果,但计算量非常大。通过引入投影近似子空间跟踪法,提出了一种改进的MLE方法。改进方法采用递归手段估计最大特征值对应的特征向量,避免了特征值分解过程,能够显著降低计算量,污染校正效果与MLE法相当。理论分析与仿真对比表明改进方法普适性强,计算量只有MLE法的万分之一,更适合工程实现。     

The LIML estimator has finite moments!

The Limited Information Maximum Likelihood estimator of the vector of coefficients of a structural equation in a simultaneous equation model is the vector that defines the linear combination maximizing the effect variance relative to the error variance. If this “eigenvector” solution is normalized by setting a designated coefficient equal to 1, the second-order moment of the estimator may be unbounded. However, the second-order moment is finite if the normalization sets the sample error variance of the linear combination equal to 1.     

Pseudolikelihood estimation of the stochastic frontier model

Stochastic frontier analysis is a popular tool to assess firm performance. Almost universally it has been applied using maximum likelihood (ML) estimation. An alternative approach, pseudolikelihood (PL) estimation, which decouples estimation of the error component structure and the production frontier, has been adopted in both the non-parametric and panel data settings. To date, no formal comparison has yet to be conducted comparing these methods in a standard, parametric cross-sectional framework. We produce a comparison of these two competing methods using Monte Carlo simulations. Our results indicate that PL estimation enjoys almost identical performance to ML estimation across a range of scenarios and performance metrics, and for certain metrics, outperforms ML estimation when the distribution of inefficiency is incorrectly specified.     

Gravity Models of Interregional Migration in Indonesia

This article explores the determinants of interregional migration in Indonesia. Employing basic and modified (extended) gravity models, and using data from the 2000 and 2010 Population Censuses and the 2005 Intercensal Population Survey, we test Long’s (1985) hypothesis that in the early stages of population redistribution, economic development is positively related to a concentration of the population. Using per-capita GDP as a proxy for income and as an indicator of economic development, we find that migration in Indonesia is indeed directed towards more developed regions. This finding supports the notion that regional disparities in development are an important factor in interregional migration in Indonesia. In line with classical gravity models, our findings show that distance is negatively related to the size of migration flows. However, unlike previous studies of interprovincial migration in Indonesia, we find that the effect of distance has weakened over time.     

Regression analysis on serial dilution data from virus validation robustness studies

To ensure the safety of plasma-derived medicinal products, the Dutch Blood Supply Foundation (Sanquin) performs virus validation experiments. Data from these experiments are based on serial dilution assays. Regression analysis on assay data faces several problems: only a small number of data points are available, data contain censoring and are subject to sampling error. Furthermore, the process variability inherent to the experiments is not evident. In this paper we address these problems by introducing a regression model for serial dilution data and by analyzing how validation experiments and simulation techniques can help elucidate various sources of variability the experiments are subject to. These are then incorporated into the regression model.     

Estimating structural equation models with non-normal variables by using transformations

We discuss structural equation models for non-normal variables. In this situation the maximum likelihood and the generalized least-squares estimates of the model parameters can give incorrect estimates of the standard errors and the associated goodness-of-fit chi-squared statistics. If the sample size is not large, for instance smaller than about 1000, asymptotic distribution-free estimation methods are also not applicable. This paper assumes that the observed variables are transformed to normally distributed variables. The non-normally distributed variables are transformed with a Box–Cox function. Estimation of the model parameters and the transformation parameters is done by the maximum likelihood method. Furthermore, the test statistics (i.e. standard deviations) of these parameters are derived. This makes it possible to show the importance of the transformations. Finally, an empirical example is presented.