国际气候基金的资金筹集与资金分配的理论探索——兼顾全球帕累托最优与财政收支平衡分析

通过构建数理模型，文章分析了国际气候基金应该如何筹集和分配资金，才能同时实现全球气候治理的帕累托最优与自身的财政收支平衡。为此，首先，构建全球气候治理的帕累托最优模型与市场均衡模型，并分别求得帕累托最优实现条件和市场均衡条件；其次，比较二者，求得使市场均衡结果实现全球帕累托最优的唯一的价格条件；紧接着，在该价格条件的基础上，纳入财政收支平衡的考虑，进而推导出兼顾全球帕累托最优与财政收支平衡下的国际气候基金的资金筹集与资金分配所需要遵循的唯一规则；在此基础上，给出了兼顾全球帕累托最优与财政收支平衡下的国际气候基金的资金筹集与资金分配的全部可选方案；最后，提出了逐步推进国际气候基金建设的政策建议。文章的创新有两点：对Baumol和Oates构建的公共外部性模型的假设条件进行修正，从而使本文的结论更加适合于全球气候治理分析；推导得出同时实现全球帕累托最优和国际气候基金收支平衡的唯一价格条件。     

洛伦兹曲线的半参数估计

洛伦兹曲线与基尼系数是研究社会收入分配差异的重要工具.社会收入分配是一个复杂的过程,用尽可能精确的曲线给出洛伦兹曲线的估计进而给出基尼系数的估计,历来是统计学者和经济学者的工作目标.基于将参数方法与非参数方法相结合的思想给出洛伦兹曲线的半参数估计,进而导出基尼系数的估计,并据此进行了实证分析.     

Statistical regularity of firm size distribution: the Pareto IV and truncated Yule for Italian SCI manufacturing

In this paper we model the firm size distribution (FSD) of Italian manufacturing firms of SCI, the GDP survey of ISTAT, by a continuous and a discrete distribution: the Pareto IV distribution on total assets and the Yule distribution on Number of Employees. The Pareto IV distribution is characterized by four parameters and shows a better fit than both the Lognormal and Pareto I, which are the distributions more frequently applied to model firm size. The Pareto IV is inconsistent with Gibrat’s Law according to which the different segments of an Industry are characterized by proportionate growth and the distribution of size is Lognormal. A truncation of the Yule distribution has been necessary because the dataset is characterized by firms with at least 20 employees. The truncated Yule distribution shows a good fit for medium–large firms (firms with more than 50 employees). The partition of the dataset in innovative and non-innovative firms – both of which are well described by the Pareto IV – reveals a beneficial effect of scale on innovation. Finally, the good fit of both distributions holds not only for the composite industry, but for the single sectors too. The present work is part of a more general research project: “Industry evolution: innovation, profitability and firm’s growth”, conducted within the Department of Economic and Social Sciences of the Università Cattolica del Sacro Cuore (UCSC), Piacenza, coordinated by Professor Maurizio Baussola in cooperation with ISTAT (Italian Statistical Office, regional office for Lombardy). Part of this research was done when Lisa Crosato was a visiting research student at the LSE Statistics Department, during her Ph.D program in “Quantitative Models for Policy Analysis” at the UCSC of Piacenza.     

Choice experiments for estimating main effects and interactions

Choice-based conjoint experiments are used when choice alternatives can be described in terms of attributes. The objective is to infer the value that respondents attach to attribute levels. This method involves the design of profiles on the basis of attributes specified at certain levels. Respondents are presented sets of profiles and asked to select the one they consider best. However if choice sets have too many profiles, they may be difficult to implement. In this paper we provide strategies for reducing the number of profiles in choice sets. We consider situations where only a subset of interactions is of interest, and we obtain connected main effect plans with smaller choice sets that are capable of estimating subsets of two-factor and three-factor interactions in 2ⁿ and 3ⁿ plans. We also provide connected main effect plans for mixed level designs.     

Inference and prediction for pareto progressively censored data

Progressively censored data from a classical Pareto distribution are to be used to make inferences about its shape and precision parameters and the reliability function. An approximation form due to Tierney and Kadane (1986) is used for obtaining the Bayes estimates. Bayesian prediction of further observations from this distribution is also considered. When the Bayesian approach is concerned, conjugate priors for either the one or the two parameters cases are considered. To illustrate the given procedures, a numerical example and a simulation study are given.     

Asymptotic results in partially non-regular log-exponential distributions

We propose approximations to the moments, different possibilities for the limiting distributions and approximate confidence intervals for the maximum-likelihood estimator of a given parametric function when sampling from partially non-regular log-exponential models. Our results are applicable to the two-parameter exponential, power-function and Pareto distribution. Asymptotic confidence intervals for quartiles in several Pareto models have been simulated. These are compared to asymptotic intervals based on sample quartiles. Our intervals are superior since we get shorter intervals with similar coverage probability. This superiority is even assessed probabilistically. Applications to real data are included.     

Identifiability of income distributions in the context of damage and generating models

In the context of additive or multiplicative damage models, and under mild conditions, it is shown that the functional forms of suitably chosen regressions on a random variable X or/and its recorded part Y are characteristic of the distribution of X. The paper treats the cases where the recorded value is either an understatement or an overstatement of the true observation.     

RECURRENCE RELATIONS FOR SINGLE AND PRODUCT MOMENTS OF GENERALIZED ORDER STATISTICS FROM PARETO,GENERALIZED PARETO,AND BURR DISTRIBUTIONS

We give recurrence relations for single and product moments of generalized order statistics under the concept of Kamps from Pareto, generalized Pareto and Burr distributions. The results include as particular cases the above relations for moments of k–th record values.     

Inference based on progressively censored sample from Pareto population

Abstract

In this paper, we assume that the lifetimes have a two-parameter Pareto distribution and discuss some results of progressive Type-II censored sample. We obtain maximum likelihood estimators and Bayes estimators of the unknown parameters under squared error loss and a precautionary loss functions in progressively Type-II censored sample. Robust Bayes estimation of unknown parameters over three different classes of priors under progressively Type-II censored sample, squared error loss, and precautionary loss functions are obtained. We discuss estimation of unknown parameters on competing risks progressive Type-II censoring. Finally, we consider the problem of estimating the common scale parameter of two Pareto distributions when samples are progressively Type-II censored.