从全社会货运量估测我国地下经济规模

一、Ｇ·Ｍ估测方法的局限性印度学者盖本塔和米塔 (以下简称Ｇ·Ｍ)设计了以经济中电子消耗状况为基础的估算地下经济规模的方法 ,这一方法在考虑了产出矩阵和技术进步影响的前提下 ,假定在用电量与国民产品之间存在稳定关系基础上 ,考察官方计算的ＧＮＰ的增长是否能说明电力消费的增长。若不能说明 ,则可以从其间差异中估测出未申报的经济规模。如果剔除技术因素的影响 ,假设电力消费量与包括申报和未申报经济在内的经济总增加值之间存在线性关系 ,则ａ=电力投入量 申报与未申报的经济总增加值　　具体的ａ值可以通过假设在基期年份不存…     

包含制度因素的中国经济增长模型及实证分析

一、引言改革开放 2 0年来 ,中国经济以年均接近 1 0 %的速度增长。在这个过程中 ,有许多因素在起作用。为此许多学者对中国经济增长因素进行了实证分析 ,从分析的内容看 ,可以分成两类 :一类研究制度因素对经济增长的影响 (金玉国 ,2 0 0 1 ) ;另一类研究产业结构因素和投入要素对经济增长的影响 (梁昭 ,2 0 0 0 ,7)。但这两类分析都未能将制度因素、产业结构因素和投入要素综合起来对中国经济增长进行实证分析。本文拟从定量研究的角度出发 ,研究制度变迁、产业结构和投入要素共同作用下各因素对中国经济增长的影响。　　二、计量模型经济…     

地下经济与收入分配的关系探析

从理论上分析 ,最初的经济学是关于财富生产的经济学 ,这一点是毫无疑问的。而产品 (商品 )及其货币表现形式无论是在生产者之间划分 ,还是在生产要素之间划分都处于从属地位 ,也就是说分配是处于从属地位的。如果从短期及与整个社会相关角度进行研究 ,最重要的经济课题就是分配。因此 ,如果对分配下一个足够宽的定义 ,那么 ,分配就规定了整个经济过程。也正是因为上述缘由 ,李嘉图曾试图把经济学引向真正关注分配的问题上 ,并且强调 :决定和调节分配的法则是政治经济学最主要的问题。我国亦是如此 ,1978年以来 ,体制改革不断深化 ,社会经济…     

我国“地下经济”活动现状初探

目前,在我国的国民经济统计核算工作中,对“地下经济”活动的核算尚属空白。近几年我国社会总需求与社会总供给之间的差距日益扩大,故然主要是由于财政赤字预算与银行信贷膨胀所引发的货币超经济发行——“超国民收入分配”所致,但“地下经济”活动核算的缺漏,也是不容忽视的根源之一。因此,有必要重视和加强对“地下经济”活动的研究。本文拟对我国“地下经济”的现状进行初步的分析探讨。     

税收诱致性现金持有量模型因果性检验及对我国地下经济规模的估测

一、现金是地下经济运作的“血液”地下经济运转的主要特征之一是现金交易 ,因而 ,在国际上 ,地下经济通常又冠以“Casheconomy”名称。现金是地下经济的工具和在现行体制的体外循环的血液。采用现金支付方式 ,其交易结算在银行帐户上和买通各个关键环节上都不会留下痕迹 ,从而     

中国地下经济实证研究：1979-1997

一、地下经济 :一个不容忽视的领域　　地下经济在西方国家又称“隐形经济”、“黑色经济”或“缝隙经济”。地下经济作为政府干预经济的一种副产品早就在世界各国普遍存在。然而 ,直到本世纪中叶 ,西方经济学者才开始注意和研究地下经济问题。这是因为在六、七十年代 ,面对西方国家普遍存在的经济衰退、失业剧增与高通货膨胀交织共存的“滞胀”现实 ,西方主流学派的经济理论显得苍白无力、无所适从 ,陷入所谓“经济理论的第二次危机”。在这种情况下 ,一些经济学者试图从新的角度———地下经济的角度———来寻找解释上述经济现象的依据 ,…     

门限自回归模型在经济分析和预测中的应用

The paper presents the application of Threshold Auto-regression ( TAR ) model in dealing with the nonlinearity in economy. With SETAR model, forecasting of 31 China’s macro-economic series is performed and the accuracy of different preprocessing methods are compared. With TARSC model, China’s Adjacent National Income Index and Accumulation rate are used to analyze economic fluctuation, empirical in terpretation of the fluctuation and leading and lagging are also presented. The results of the application are satisfactory.     

非均衡经济计量模型及其估计

非均衡经济计量模型是在经济计量理论和非均衡市场大量统计观察的相互作用下发展起来的。它的开创者是美国经济计量学家费阿和杰斐。 一、基本模型 假设需求方程和供给方程如下。需求方程 Dt== a0X_t~D μ_t~D (t=1,2,…,T)(1)供给方程 St=β_0X_t~sX_t~s μ_t~s (t=1,2,…,T)(2)这里,Dt表示时期t的需求量,St表示时期t的供给量,X_t~D、X_t~s是各种外生变量的向量,a0,β0是待估参数,μ_t~D,μ_t~s是随机误差项,并假设μ_t~D,μ_t~s的均值为0方差为常数,无序列相关、     

应当加强“地下经济”活动的统计核算

一、“地下经济”活动的概念和内容“地下经济”活动蕴含在整个国民经济活动之中,它为国民经济统计核算提出了新的要求和新的核算内容。所谓“地下经济”活动是指国家各种经济社会核算以外的经济活动,具体地说,就是企业的会计核算、部门的业务核算和国民经济统计核算不能直接“覆盖”或无法直接核算的各种经济活动。“地下经济”活动包括各种非法活动和合法活动的“非法交易”(收入),以及各种没有核算的“自产自销”活动,我国通常所说的“灰收入”则属于合法活动的“非法交易”范围(包括各种非正常的增加收入行为)。其基本特征是非法性、无记录性。“地下经济”活动包括以下主要内容:     

Bivariate Birnbaum-Saunders accelerated lifetime model: estimation and diagnostic analysis

In this paper, we discuss the bivariate Birnbaum-Saunders accelerated lifetime model, in which we have modeled the dependence structure of bivariate survival data through the use of frailty models. Specifically, we propose the bivariate model Birnbaum-Saunders with the following frailty distributions: gamma, positive stable and logarithmic series. We present a study of inference and diagnostic analysis for the proposed model, more concisely, are proposed a diagnostic analysis based in local influence and residual analysis to assess the fit model, as well as, to detect influential observations. In this regard, we derived the normal curvatures of local influence under different perturbation schemes and we performed some simulation studies for assessing the potential of residuals to detect misspecification in the systematic component, the presence in the stochastic component of the model and to detect outliers. Finally, we apply the methodology studied to real data set from recurrence in times of infections of 38 kidney patients using a portable dialysis machine, we analyzed these data considering independence within the pairs and using the bivariate Birnbaum-Saunders accelerated lifetime model, so that we could make a comparison and verify the importance of modeling dependence within the times of infection associated with the same patient.     

Log-Weibull extended regression model: Estimation,sensitivity and residual analysis

The failure rate function commonly has a bathtub shape in practice. In this paper we discuss a regression model considering new Weibull extended distribution developed by Xie et al. (2002) that can be used to model this type of failure rate function. Assuming censored data, we discuss parameter estimation: maximum likelihood method and a Bayesian approach where Gibbs algorithms along with Metropolis steps are used to obtain the posterior summaries of interest. We derive the appropriate matrices for assessing the local influence on the parameter estimates under different perturbation schemes, and we also present some ways to perform global influence. Also, some discussions on case deletion influence diagnostics are developed for the joint posterior distribution based on the Kullback–Leibler divergence. Besides, for different parameter settings, sample sizes and censoring percentages, are performed various simulations and display and compare the empirical distribution of the Martingale-type residual with the standard normal distribution. These studies suggest that the residual analysis usually performed in normal linear regression models can be straightforwardly extended to the martingale-type residual in log-Weibull extended models with censored data. Finally, we analyze a real data set under a log-Weibull extended regression model. We perform diagnostic analysis and model check based on the martingale-type residual to select an appropriate model.     

Extreme value analysis of actuarial risks: estimation and model validation

We give an overview of several aspects arising in the statistical analysis of extreme risks with actuarial applications in view. In particular it is demonstrated that empirical process theory is a very powerful tool, both for the asymptotic analysis of extreme value estimators and to devise tools for the validation of the underlying model assumptions. While the focus of the paper is on univariate tail risk analysis, the basic ideas of the analysis of the extremal dependence between different risks are also outlined. Here we emphasize some of the limitations of classical multivariate extreme value theory and sketch how a different model proposed by Ledford and Tawn can help to avoid pitfalls. Finally, these theoretical results are used to analyze a data set of large claim sizes from health insurance.     

Nonparametric estimation of probabilistic sensitivity measures

Statistics and Computing - Computer experiments are becoming increasingly important in scientific investigations. In the presence of uncertainty, analysts employ probabilistic sensitivity methods...     

我国非正规经济规模的定量估测——现金比率法的修正及实证分析

所谓非正规经济就是逃避税收和政府管制 ,未向政府部门申报的经济行为。非正规经济的主要特征是反秩特征。非正规经济的规模与高税收、合法部门的低增长、高失业率、通货膨胀有正相关关系。一种社会契约限制了收入的增长 ,各种形式的价格管制又十分严格 ,如果非正规经济确实不受管制的影响 ,那么就可以期待在此期间未申报经济应当增长。概括起来 ,非正规经济的反秩特征有以下几点 :第一 ,短缺经济会诱发非正规经济。倘若社会某些商品出现短缺 ,则必然会导致该类商品在市场上供不应求 ,价格上涨 ,从而形成卖方市场的格局。按照人们的消费心理…     

Symmetrical design for symmetrical global sensitivity analysis of model output

Symmetrical global sensitivity analysis (SGSA) can help practitioners focusing on the symmetrical terms of inputs whose uncertainties have an impact on the model output, which allows reducing the complexity of the model. However, there remains the challenging problem of finding an efficient method to get symmetrical global sensitivity indices (SGSI) when the functional form of the symmetrical terms is unknown, including numerical and non-parametric situations. In this study, we propose a novel sampling plan, called symmetrical design, for SGSA. As a preliminary experiment for model feature extracting, such plan offers the virtue of run-size economy due to its closure respective to the given group. Using the design, we give estimation methods of SGSI as well as their asymptotic properties respectively for numerical model and non-parametrical model directly by the model outputs, and further propose a significance test for SGSI in non-parametric situation. A case study for a benchmark of GSA and a real data analysis show the effectiveness of the proposed design.     

Mean and sensitivity estimation in optional randomized response models

Optional randomized response models were introduced by Gupta et al. (2002). These models are based on the basic premise that a question may be sensitive for one respondent but may not be sensitive for another. In an optional RRT (randomized response technique) model, a respondent is asked to provide a scrambled response only if the respondent considers the question sensitive. Otherwise, the respondent provides a truthful response. The researcher does not know which type of response is provided. The proportion of respondents who provide a scrambled response is known as the sensitivity level of the question. In this paper, we estimate simultaneously the mean and the sensitivity level of a quantitative-response sensitive question using a two stage optional RRT model. The estimators are unbiased and asymptotically normally distributed. We discuss the advantages and disadvantages of using additive and multiplicative scrambling.     

A new log-location regression model: estimation,influence diagnostics and residual analysis

We introduce a log-linear regression model based on the odd log-logistic generalized half-normal distribution [7 G.M. Cordeiro, M. Alizadeh, R.R. Pescim, and E.M.M. Ortega, The odd log-logistic generalized half-normal lifetime distribution: Properties and applications, Comm. Statist. Theory Methods (2015), accepted for publication. [Google Scholar]]. Some of its structural properties including explicit expressions for the density function, quantile and generating functions and ordinary moments are derived. We estimate the model parameters by the maximum likelihood method. For different parameter settings, proportion of censoring and sample size, some simulations are performed to investigate the behavior of the estimators. We derive the appropriate matrices for assessing local influence diagnostics on the parameter estimates under different perturbation schemes. We also define the martingale and modified deviance residuals to detect outliers and evaluate the model assumptions. In addition, we demonstrate that the extended regression model can be very useful in the analysis of real data and provide more realistic fits than other special regression models. The potentiality of the new regression model is illustrated by means of a real data set.     

On estimation and diagnostics analysis in log-generalized gamma regression model for interval-censored data

The interval-censored survival data appear very frequently, where the event of interest is not observed exactly but it is only known to occur within some time interval. In this paper, we propose a location-scale regression model based on the log-generalized gamma distribution for modelling interval-censored data. We shall be concerned only with parametric forms. The proposed model for interval-censored data represents a parametric family of models that has, as special submodels, other regression models which are broadly used in lifetime data analysis. Assuming interval-censored data, we consider a frequentist analysis, a Jackknife estimator and a non-parametric bootstrap for the model parameters. We derive the appropriate matrices for assessing local influence on the parameter estimates under different perturbation schemes and present some techniques to perform global influence.