Bounding Analysis as an Inadequately Specified Methodology

The bounding analysis methodology described by Ha-Duong et al. (this issue) is logically incomplete and invites serious misuse and misinterpretation, as their own example and interpretation illustrate. A key issue is the extent to which these problems are inherent in their methodology, and resolvable by a logically complete assessment (such as Monte Carlo or Bayesian risk assessment), as opposed to being general problems in any risk-assessment methodology. I here attempt to apportion the problems between those inherent in the proposed bounding analysis and those that are more general, such as reliance on questionable expert elicitations. I conclude that the specific methodology of Ha-Duong et al. suffers from logical gaps in the definition and construction of inputs, and hence should not be used in the form proposed. Furthermore, the labor required to do a sound bounding analysis is great enough so that one may as well skip that analysis and carry out a more logically complete probabilistic analysis, one that will better inform the consumer of the appropriate level uncertainty. If analysts insist on carrying out a bounding analysis in place of more thorough assessments, extensive analyses of sensitivity to inputs and assumptions will be essential to display uncertainties, arguably more essential than it would be in full probabilistic analyses.     

Estimating the First- and Second-Order Parameters of a Heavy-Tailed Distribution

This paper suggests censored maximum likelihood estimators for the first‐ and second‐order parameters of a heavy‐tailed distribution by incorporating the second‐order regular variation into the censored likelihood function. This approach is different from the bias‐reduced maximum likelihood method proposed by Feuerverger and Hall in 1999. The paper derives the joint asymptotic limit for the first‐ and second‐order parameters under a weaker assumption. The paper also demonstrates through a simulation study that the suggested estimator for the first‐order parameter is better than the estimator proposed by Feuerverger and Hall although these two estimators have the same asymptotic variances.     

Estimation of the intercept parameter for linear regression model with uncertain non-sample prior information

This paper considers alternative estimators of the intercept parameter of the linear regression model with normal error when uncertain non-sample prior information about the value of the slope parameter is available. The maximum likelihood, restricted, preliminary test and shrinkage estimators are considered. Based on their quadratic biases and mean square errors the relative performances of the estimators are investigated. Both analytical and graphical comparisons are explored. None of the estimators is found to be uniformly dominating the others. However, if the non-sample prior information regarding the value of the slope is not too far from its true value, the shrinkage estimator of the intercept parameter dominates the rest of the estimators.     

Measuring the impact of prices on inequality: With applications to Thailand and Korea

This paper develops a new methodology to compute social cost of living indices. These indices indicate whether or not price changes have a favourable (or unfavourable) impact on the welfare of the poor. The indices are derived on the basis of two alternative classes of social welfare functions. The methodology developed in the paper is applied to compute social cost of living indices for Thailand and Korea. The empirical results show that changes in prices have generally affected the poor more adversely than the non-poor.     

Local model uncertainty and incomplete-data bias (with discussion)

Summary.  Problems of the analysis of data with incomplete observations are all too familiar in statistics. They are doubly difficult if we are also uncertain about the choice of model. We propose a general formulation for the discussion of such problems and develop approximations to the resulting bias of maximum likelihood estimates on the assumption that model departures are small. Loss of efficiency in parameter estimation due to incompleteness in the data has a dual interpretation: the increase in variance when an assumed model is correct; the bias in estimation when the model is incorrect. Examples include non-ignorable missing data, hidden confounders in observational studies and publication bias in meta-analysis. Doubling variances before calculating confidence intervals or test statistics is suggested as a crude way of addressing the possibility of undetectably small departures from the model. The problem of assessing the risk of lung cancer from passive smoking is used as a motivating example.     

Getting the offer: Sex discrimination in hiring

The hiring process is currently the least understood aspect of the employment relationship. It may be the most important for understanding the broad processes of stratification with allocation of demographic groups to jobs and firms. The lack of knowledge is due to difficulty of assembling data on the processes that occur at the point of hire. Against this background we analyze data on all applicants to positions in one of the largest Scandinavian banks in 1997–1998, providing what we believe to be the first study using applicant pool data and information about extended offers in a private-sector European firm, adding to the record of about half a dozen such U.S. studies. The hiring agents in the organization are fully conscious and concerned about the nonconscious biases and gender schemas they carry when making hiring decisions. Their effects on hiring are considered to be beyond dispute: women are at a clear disadvantage. For actual hiring practices we found that the opposite is true: women are not at a disadvantage and may even be at an advantage in getting offers. Two organizational practices may lead to female advantage. The hiring agents had been educated about the role of nonconscious biases, which perhaps mitigated their effects. But they had also been instructed to search actively for qualified females in the applicant pool. With no qualified females in the first pass, they go through the pool a second and third time hoping to find one. We discuss reasons why the interpretations and meanings the hiring agents attribute to the hiring process are at odds with what actually occurs.     

行为金融学理论述评

20世纪80年代以来,大量的金融研究与市场异象对传统资本市场理论提出了挑战。行为金融理论在整合多学科研究成果,特别是心理学研究的基础上,从投资者的实际决策行为出发,分析与解释金融市场。通过文献研究,文章回顾了有关行为金融学的基本理论与研究,包括前景理论及其他相关理论的发展。此外,对国内有关行为金融学研究特点进行总结分析,并对其今后研究发展提出了设想。     

International Business Cycle Asymmetry and Time Irreversible Nonlinearities

Using tests of time reversibility, this paper provides further statistical evidence on the long-standing conjecture in economics concerning the potentially asymmetric behaviour of output over the expansionary and contractionary phases of the business cycle. A particular advantage of this approach is that it provides a discriminating test that is instructive as to whether any asymmetries detected are due to asymmetric shocks to a linear model, or an underlying non-linear model with symmetric shocks, and in the latter case is informative as to the potential form of that nonlinear model. Using a long span of international per capita output growth data, the asymmetry detected is overwhelmingly consistent with the long standing perception that the output business cycle is characterized by steeper recessions and longer more gentle expansions, but the evidence for this form of business cycle asymmetry is weaker in the data adjusted for the influence of outliers associated with wars and other extreme events. Statistically significant time irreversibility is reported for the output growth rates of almost all of the countries considered in the full sample data, and there is evidence that this time irreversibility is of a form implying an underlying nonlinear model with symmetrically distributed innovations for 15 of the 22 countries considered. However, the time irreversibility test results for the outlier-trimmed full sample data reveal significant time irreversibility in output growth for around one half of the countries considered, predominantly in Northern Europe and North America, and of a form implying a nonlinear underlying model in only a further half of those cases.     

通用性指标偏好及其实验研究——平衡计分卡在多部门企业中的应用

文章分析了通用性指标偏好产生的原因、影响和解决途径,并通过实验研究,考察了中国实验者在平衡计分卡应用中是否也存在通用性指标偏好,以及集体决策方式对通用性偏好程度的影响。决策者组成的多元化,是集体决策发挥作用的重要因素,这一结论对于部门平衡计分卡在现实中的应用和企业绩效评价实践具有启示作用。     

Small-sample results for maximum-likelihood estimation in branching processes

In this article, small sample properties of the maximum-likelihood estimator (m.l.e.) for the offspring distribution (pk) and its mean m are considered in the context of the simple branching process. A representation theorem is given for the m.l.e. of (Pk) from which the m.l.e. of m is obtained. The case where p0 + p1 + p2 = 1 is studied in detail: numerical results are given for the exact bias of these estimators as a function of the age of the process; a curve fitting analysis expresses the bias of m? as a function of the mean and the variance of the offspring distribution and finally an “approximate m.l.e.” for (pk) is given.