电信主导运营商价格压榨的竞争效应

在纵向相关市场结构下,电信在位主导运营商的价格压榨行为是一种恢复纵向垄断市场势力和获取纵向一体最大化垄断利润的策略性行为,它会严重伤害下游市场的竞争,并降低社会总福利。在反垄断执法中,判定反竞争的价格压榨行为需要分析市场结构条件和进行竞争伤害检验,本文提出了基础的归因检验规则和具体的四种不同类型价格压榨检验方法。基于上述分析,对中国电信和中国联通网络接入价格歧视行为反垄断审查的结构条件和归因检验进行了分析,并认为不合理接入管制政策是价格压榨长期存在的重要原因。     

Compromised imputation in survey sampling

In this paper, a compromised imputation procedure has been suggested. The estimator of mean obtained from compromised imputation remains better than the estimators obtained from ratio method of imputation and mean method of imputation. An idea to form “Warm Deck Method” of imputation has also been suggested. Received: July 1998     

The value of dividends: Evidence from cum-dividend trading in the ex-dividend period

What is the market value of a dollar of fully franked dividends? We address this question by exploiting a new phenomenon in the Australian capital market—the trading of shares cum-dividend during the ex-dividend period. This allows a relatively clean measurement of the combined value of dividends and the associated tax effects net of transactions costs. Consistent with the theoretical model that we develop, the evidence from this sample is that one dollar of fully franked dividends, after tax effects and transaction costs, is worth significantly more than one dollar. We also show that, in contrast to our measure, the traditional measure of the ex-dividend price drop-off, based on close to close prices, has a lower average value and exhibits substantially more cross sectional variation.     

Book Reivew Section

Books reviewed:
Alan Davies, Myles McGregor-Lowndes and Robert Craig The Internet for Accountants
Wayne Lonergan The Valuation of Businesses, Shares and Other Equity , 3rd Edition
Frank K. Reilly and Keith C. Brown Investment Analysis and Portfolio Management , 5th Edition     

Relative Efficiency of the Fuzzy p-Value Approach to Hypothesis Testing

In missing data problems, it is often the case that there is a natural test statistic for testing a statistical hypothesis had all the data been observed. A fuzzy  p -value approach to hypothesis testing has recently been proposed which is implemented by imputing the missing values in the "complete data" test statistic by values simulated from the conditional null distribution given the observed data. We argue that imputing data in this way will inevitably lead to loss in power. For the case of scalar parameter, we show that the asymptotic efficiency of the score test based on the imputed "complete data" relative to the score test based on the observed data is given by the ratio of the observed data information to the complete data information. Three examples involving probit regression, normal random effects model, and unidentified paired data are used for illustration. For testing linkage disequilibrium based on pooled genotype data, simulation results show that the imputed Neyman Pearson and Fisher exact tests are less powerful than a Wald-type test based on the observed data maximum likelihood estimator. In conclusion, we caution against the routine use of the fuzzy  p -value approach in latent variable or missing data problems and suggest some viable alternatives.     

Proper and Improper Multiple Imputation

Multiple imputation has become viewed as a general solution to missing data problems in statistics. However, in order to lead to consistent asymptotically normal estimators, correct variance estimators and valid tests, the imputations must be proper . So far it seems that only Bayesian multiple imputation, i.e. using a Bayesian predictive distribution to generate the imputations, or approximately Bayesian multiple imputations has been shown to lead to proper imputations in some settings. In this paper, we shall see that Bayesian multiple imputation does not generally lead to proper multiple imputations. Furthermore, it will be argued that for general statistical use, Bayesian multiple imputation is inefficient even when it is proper.     

Stock Dividend Announcement Effects in an Imputation Tax Environment

A key question in asset pricing is the extent to which tax effects are passed through market prices or are capitalised in them. New Zealand stock dividends provide a useful window into this debate because of (1) the existence of both taxable and non-taxable stock dividends, and (2) the particular form of imputation tax system which allows the full pass through of corporate taxes to the investor on the proportion of profits which are distributed either as cash or taxable stock dividends. We present evidence that investors value future tax benefits associated with imputation tax credits.     

List augmentation with model based multiple imputation: a case study using a mixed-outcome factor model

This study concerns list augmentation in direct marketing. List augmentation is a special case of missing data imputation. We review previous work on the mixed outcome factor model and apply it for the purpose of list augmentation. The model deals with both discrete and continuous variables and allows us to augment the data for all subjects in a company's transaction database with soft data collected in a survey among a sample of those subjects. We propose a bootstrap-based imputation approach, which is appealing to use in combination with the factor model, since it allows one to include estimation uncertainty in the imputation procedure in a simple, yet adequate manner. We provide an empirical case study of the performance of the approach to a transaction data base of a bank.     

A toolkit in SAS for the evaluation of multiple imputation methods

This paper outlines a strategy to validate multiple imputation methods. Rubin's criteria for proper multiple imputation are the point of departure. We describe a simulation method that yields insight into various aspects of bias and efficiency of the imputation process. We propose a new method for creating incomplete data under a general Missing At Random (MAR) mechanism. Software implementing the validation strategy is available as a SAS/IML module. The method is applied to investigate the behavior of polytomous regression imputation for categorical data.