劳动收入份额的决定与波动机制研究

本文通过数理经济模型的理论分析和计量经济模型的实证分析发现,决定社会产品初次分配中劳动收入份额高低的因素,除了政府的生产税税率之外,主要是劳动者的实际工资水平和生产技术水平,且二者的作用方向完全相反,都取决于要素替代弹性的大小。当资本和劳动的替代弹性小于1时,劳动者的实际工资水平越高,劳动收入份额就越高,劳动者的生产技术水平越高,劳动收入份额就越低;而当资本和劳动的替代弹性大于1时,则劳动者的实际工资水平越高,劳动收入份额就越低;劳动者的生产技术水平越高,劳动收入份额就越高。理论和实证的分析还表明,劳动者货币工资水平和实际工资水平的增长随着经济周期的波动而波动,实际工资水平增长率的波动与经济周期的波动方向相反,导致劳动收入份额也随经济周期反向波动。     

Some practical advice on polynomial regression analysis from blocked response surface designs

It is often necessary to run response surface designs in blocks. In this paper the analysis of data from such experiments, using polynomial regression models, is discussed. The definition and estimation of pure error in blocked designs are considered. It is recommended that pure error is estimated by assuming additive block and treatment effects, as this is more consistent with designs without blocking. The recovery of inter-block information using REML analysis is discussed, although it is shown that it has very little impact if the design is nearly orthogonally blocked. Finally prediction from blocked designs is considered and it is shown that prediction of many quantities of interest is much simpler than prediction of the response itself.     

通货膨胀预期、企业成长性与企业投资

本文实证检验了通货膨胀预期对企业投资行为的影响及其内部机理来分析通货膨胀预期变化的经济后果。研究发现,宏观预期通货膨胀率的上升会促使微观企业增加当期资本支出规模,但是降低投资效率。进一步分析结果表明：第一,预期通货膨胀率越高,高成长性企业的投资水平显著提高,投资效率显著下降,这一结果在低成长性企业中并不显著;第二,宏观预期通货膨胀率升高,银行更愿意借款给企业。这一结果在高成长性企业中同样显著,在低成长性企业中并不显著。这说明通货膨胀预期通过外部融资促使公司投资,高成长性企业比低成长性企业能够获得更多的银行贷款,从而增加资本投资,而低成长性企业受通货膨胀预期的影响被弱化。     

Category Distinguishability and Observer Agreement

It is common in the medical, biological, and social sciences for the categories into which an object is classified not to have a fully objective definition. Theoretically speaking the categories are therefore not completely distinguishable. The practical extent of their distinguishability can be measured when two expert observers classify the same sample of objects. It is shown, under reasonable assumptions, that the matrix of joint classification probabilities is quasi-symmetric, and that the symmetric matrix component is non-negative definite. The degree of distinguishability between two categories is defined and is used to give a measure of overall category distinguishability. It is argued that the kappa measure of observer agreement is unsatisfactory as a measure of overall category distinguishability.     

对生产帕累托最优条件充分性的质疑与改进

现有的理论中,生产帕累托最优条件不充分,其最优状态不是唯一的,使得理论自身及随后的应用上产生了一些矛盾。确定唯一最优状态的充分条件应该是：双方都满意的交换利益分割和边际技术替代率相等。前者保证交换实现,后者保证交换产生的利益被完全穷尽。只要遵循新交换原则,交换必能实现,但不一定是帕累托最优。信息不充分时,如果信息不对称者对对方所判断的生产函数改变了其真实生产函数的技术性质,交换虽能实现,但生产并没有达到帕累托最优。     

How does the DerSimonian and Laird procedure for random effects meta-analysis compare with its more efficient but harder to compute counterparts?

The procedure suggested by DerSimonian and Laird is the simplest and most commonly used method for fitting the random effects model for meta-analysis. Here it is shown that, unless all studies are of similar size, this is inefficient when estimating the between-study variance, but is remarkably efficient when estimating the treatment effect. If formal inference is restricted to statements about the treatment effect, and the sample size is large, there is little point in implementing more sophisticated methodology. However, it is further demonstrated, for a simple special case, that use of the profile likelihood results in actual coverage probabilities for 95% confidence intervals that are closer to nominal levels for smaller sample sizes. Alternative methods for making inferences for the treatment effect may therefore be preferable if the sample size is small, but the DerSimonian and Laird procedure retains its usefulness for larger samples.     

On choosing between fixed and random block effects in some no-interaction models

The purpose of this article is to strengthen the understanding of the relationship between a fixed-blocks and random-blocks analysis in models that do not include interactions between treatments and blocks. Treating the block effects as random has been recommended in the literature for balanced incomplete block designs (BIBD) because it results in smaller variances of treatment contrasts. This reduction in variance is large if the block-to-block variation relative to the total variation is small. However, this analysis is also more complicated because it results in a subjective interpretation of results if the block variance component is non-positive. The probability of a non-positive variance component is large precisely in those situations where a random-blocks analysis is useful – that is, when the block-to-block variation, relative to the total variation, is small. In contrast, the analysis in which the block effects are fixed is computationally simpler and less subjective. The loss in power for some BIBD with a fixed effects analysis is trivial. In such cases, we recommend treating the block effects as fixed. For response surface experiments designed in blocks, however, an opposite recommendation is made. When block effects are fixed, the variance of the estimated response surface is not uniquely estimated, and in practice this variance is obtained by ignoring the block effect. It is argued that a more reasonable approach is to treat the block effects to be random than to ignore it.     

Disoominkfrcn between gaussian time series based on their spectral differences

Discrimination between two Gaussian time series is examined assuming that the important difference between the alternative processes is their covarianoe (spectral) structure. Using the likelihood ratio method in frequency domain a discriminant function is derived and its approximate distribution is obtained. It is demonstrated that, utilizing the Kullbadk-Leibler information measure, the frequencies or frequency bands which carry information for discrimination can be determined. Using this, it is shown that when mean functions are equal, discrimination based on the frequency with the largest discrimination information is equivalent to the classification procedure based on the best linear discriminant, Application to seismology is described by including a discussion concerning the spectral ratio discriminant for underground nuclear explosion and natural earthquake and is illustrated numerically using Rayleigh wave data from an underground and an atmospheric explosions.     

税收对中国收入分配差距的影响研究

研究中国税收与居民收入差距之间的定量关系,研究表明:目前中国税种少,个人所得税结构复杂,1994年以前个人所得税起征点高,2000年以后个人所得税起征点低;中等收入阶层是个人所得税主要来源,企业整体税收较重,大型(垄断)企业税收较轻,小型企业税收负担重;主要税种增值税流失严重,消费税比例高。以上原因致使中国税收调节居民收入差距效应微弱。根据研究结论,提出了政策建议。     

Surveillance of the interaction parameter of the ising model

Surveillance to detect changes of spatial patterns is of interest in many areas such as environmental control and regional analysis. Here the interaction parameter of the Ising model, is considered. A minimal sufficient statistic and its asymptotic distribution are used. It is demonstrated that the convergence to normal, distribution is rapid. The main result is that when the lattice is large, all approximations are better in several respects. It is shown that, for large lattice sizes, earlier results on surveillance of a normally distributed random variable can be used in cases of most interest. The expected delay of alarm at a fixed level of false alarm probability is examined for some examples.     

Estimating the intensity function of a power law process at the current time: time truncated case

The power law process, a nonhomogeneous Poisson process with intensity function µ(t) = (β/θ)(t/θ) , is frequently used to model the occurence of events in time. Often, an important quantity is the value of the intensity function at the current time, that is, the time when data collection is ceased. In this article, the problem of estimating this quantity is addressed when the data are time truncated, that is, when data collection is stopped at a predetermined time T. The class of multiples of the conditional MLE is suggested, and some members are analyzed. In addition, the class of estimators formed by first performing a preliminary test of significance on the parameter β is analyzed. Expressions for the bias and MSE of these estimators are derived and evaluated for several values of the parameters     

An efficient non-rejective implementation of the πps sampling designs

Poisson sampling is a method for unequal probabilities sampling with random sample size. There exist several implementations of the Poisson sampling design, with fixed sample size, which almost all are rejective methods, that is, the sample is not always accepted. Thus, the existing methods can be time-consuming or even infeasible in some situations. In this paper, a fast and non-rejective method, which is efficient even for large populations, is proposed and studied. The method is a new design for selecting a sample of fixed size with unequal inclusion probabilities. For the population of large size, the proposed design is very close to the strict πps sampling which is similar to the conditional Poisson (CP) sampling design, but the implementation of the design is much more efficient than the CP sampling. And the inclusion probabilities can be calculated recursively.     

Parameterization and inference for nonparametric regression problems

We consider local likelihood or local estimating equations, in which a multivariate function () is estimated but a derived function () of () is of interest. In many applications, when most naturally formulated the derived function is a non-linear function of (). In trying to understand whether the derived non-linear function is constant or linear, a problem arises with this approach: when the function is actually constant or linear, the expectation of the function estimate need not be constant or linear, at least to second order. In such circumstances, the simplest standard methods in nonparametric regression for testing whether a function is constant or linear cannot be applied. We develop a simple general solution which is applicable to nonparametric regression, varying-coefficient models, nonparametric generalized linear models, etc. We show that, in local linear kernel regression, inference about the derived function () is facilitated without a loss of power by reparameterization so that () is itself a component of (). Our approach is in contrast with the standard practice of choosing () for convenience and allowing ()> to be a non-linear function of (). The methods are applied to an important data set in nutritional epidemiology.     

不完全信息下纯交换帕累托最优的实现研究

现有的理论中，纯交换帕累托最优状况不是唯一的，且认为只要进行交换就必然能实现帕累托最优。其原因是纯交换帕累托最优条件不充分和交换原则存有缺陷。根据交换是一个讨价还价的过程，采用博弈分析方法，在不完全信息条件下，只要遵循理性人交换原则，交换必能实现，但不一定是帕累托最优。如果信息不对称方所判断的效用函数没有改变对方真实效用函数的偏好性质，交换必然能达到唯一的帕累托最优状况，但利益分割不公平，存在交换剥削；如果效用函数的偏好性质改变，帕累托最优不能实现，而是帕累托改进，但其消费状态也是唯一的。     

Bayesian approaches to the detection of outliers in poisson samples

This paper is concerned with the detection of upper outliers in a Poisson sample.The approach is Bayesian throughout. It is supposed that a small number of observations are contaminated, that is they are generated from a Poisson sample with mean inflated by a factor §.Bayes factors for the cases when (i) § is known, (ii) it is given a proper conjugate prior or (iii) it is completely unknown are discussed. It is suggested, in contrast to classical approaches, that transforming the data to normality does not simplify the problem.     

Improved nonparametric tolerance intervals based on interpolated and extrapolated order statistics

The standard approach to construct nonparametric tolerance intervals is to use the appropriate order statistics, provided a minimum sample size requirement is met. However, it is well-known that this traditional approach is conservative with respect to the nominal level. One way to improve the coverage probabilities is to use interpolation. However, the extension to the case of two-sided tolerance intervals, as well as for the case when the minimum sample size requirement is not met, have not been studied. In this paper, an approach using linear interpolation is proposed for improving coverage probabilities for the two-sided setting. In the case when the minimum sample size requirement is not met, coverage probabilities are shown to improve by using linear extrapolation. A discussion about the effect on coverage probabilities and expected lengths when transforming the data is also presented. The applicability of this approach is demonstrated using three real data sets.     

对Black-Litterman模型加入主观收益方法的改进

Black-Litterman模型是高盛公司提出的确定加入主观观念的资产组合模型,其显著特点是模型加入了投资者的主观观念。目前文献研究显示,模型加入投资人主观收益的方法依然是武断确定。这样加剧了主观投资组合模型使用的随意性,也必将影响其使用效果,会给投资造成巨大损失。由此本文提出使用BMA（贝叶斯移动平均法）模型预测投资者主观收益,文中利用中国上海A股市场数据,编写Matlab程序对主观资产组合模型进行了实证,实证表明预测效果得到了明显改善。该模型不但对Blackliterman模型有重要作用,而且对金融统计模型预测效果的提高也有很大启发。     

A statistical test for the hypothesis of Gaussian random function

A Gaussian random function is a functional version of the normal distribution. This paper proposes a statistical hypothesis test to test whether or not a random function is a Gaussian random function. A parameter that is equal to 0 under Gaussian random function is considered, and its unbiased estimator is given. The asymptotic distribution of the estimator is studied, which is used for constructing a test statistic and discussing its asymptotic power. The performance of the proposed test is investigated through several numerical simulations. An illustrative example is also presented.     

A multivariate logit model with marginal canonical association

In this work, a generalization of the Goodman Association Model to the case of q, q > 2, categorical variables which is based on the idea of marginal modelling discussed by Gloneck–McCullagh is introduced; the difference between the proposed generalization and two models, previously introduced by Becker and Colombi, is discussed. The Becker generalization is not a marginal model because it does not imply Logit Models for the marginal probabilities, and because it is based on the conditional approach of modelling the association. The Colombi model is only partially a marginal model because it uses simple logit models for the univariate marginal probabilities but is based on the conditional approach of modelling the association. It is also shown that the maximum likelihood estimation of the parameters of the new model is feasible and, to compute the maximum likelihood estimates, an algorithm is proposed, which is a numerically convenient compromise between the constrained optimization approach of Lang and the straightforward use of the Fisher Scoring Algorithm suggested by Glonek–McCullagh.Finally, the proposed model is used to analyze a data set concerning work accidents which occurred to workers at some Italian firms during the years 1994–1996.     

Non-minimaxity of linear combinations of restricted location estimators and related problems

The estimation of a linear combination of several restricted location parameters is addressed from a decision-theoretic point of view. Although the corresponding linear combination of the unbiased estimators is minimax under the restricted problem, it has a drawback of taking values outside the restricted parameter space. Thus, it is reasonable to use the linear combination of the restricted estimators such as maximum likelihood or truncated estimators. In this paper, a necessary and sufficient condition for such restricted estimators to be minimax is derived, and it is shown that the restricted estimators are not minimax when the number of the location parameters is large. The condition for minimaxity is examined for some specific distributions. Finally, similar problems of estimating the product and sum of the restricted scale parameters are studied, and it is shown that analogous non-dominance properties appear when the number of the scale parameters is large.