First-Order Integer-Valued Autoregressive (INAR (1)) Process: Distributional and Regression Properties

Some properties of a first-order integer-valued autoregressive process (INAR)) are investigated. The approach begins with discussing the self-decomposability and unimodality of the 1-dimensional marginals of the process {X_n} generated according to the scheme X_n=α° X _n-i +e_n, where α° X _n-1 denotes a sum of X_{n - 1}, independent 0 - 1 random variables Y^(n-1), independent of X _n-1 with Pr -( y ^{(n - 1)}= 1) = 1 - Pr ( y ^(n-i)= 0) =α. The distribution of the innovation process ( e _n) is obtained when the marginal distribution of the process ( X _n) is geometric. Regression behavior of the INAR(1) process shows that the linear regression property in the backward direction is true only for the Poisson INAR(1) process.     

山东省城乡居民边际消费倾向实证分析

文章利用凯恩斯主义消费函数建立一元线性回归模型,估算出山东省城乡居民消费函数的具体形式,分析了城乡居民边际消费倾向显著差异的存在性,对山东省促进居民消费增长提供了若干政策建议和对策。     

对河北省城镇居民消费的统计分析

消费是拉动经济增长的动力。市场经济条件下,研究居民的消费情况、消费结构,可以更深入的研究消费需求的变化趋势。本文侧重从定量分析的角度,对河北省城镇居民的人均可支配收入和消费结构进行初步分析,揭示城镇居民消费的总体状况和地区状况,提出符合实际的发展建议。     

Regression Sampling in Statistical Auditing: A Practical Survey and Evaluation

Several confidence intervals for the regression estimator are surveyed. A Monte Carlo experiment, based on the NETER and LOEBBECKE (1975) populations, gives estimated coverages and lengths of the different confidence intervals. One interval is exact under the assumption of multivariate normal distributions; it gives longer intervals (hence better coverages) than the interval based on a popular variance estimator. An interval due to ROBERTS (1970) is much too long. Jackknifing gives robust intervals. Rules of thumb for practitioners are given.     

Regression with imputed covariates: A generalized missing-indicator approach

A common problem in applied regression analysis is that covariate values may be missing for some observations but imputed values may be available. This situation generates a trade-off between bias and precision: the complete cases are often disarmingly few, but replacing the missing observations with the imputed values to gain precision may lead to bias. In this paper, we formalize this trade-off by showing that one can augment the regression model with a set of auxiliary variables so as to obtain, under weak assumptions about the imputations, the same unbiased estimator of the parameters of interest as complete-case analysis. Given this augmented model, the bias-precision trade-off may then be tackled by either model reduction procedures or model averaging methods. We illustrate our approach by considering the problem of estimating the relation between income and the body mass index (BMI) using survey data affected by item non-response, where the missing values on the main covariates are filled in by imputations.     

Semiparametric Regression Analysis of Panel Count Data: A Practical Review

Panel count data arise in many applications when the event history of a recurrent event process is only examined at a sequence of discrete time points. In spite of the recent methodological developments, the availability of their software implementations has been rather limited. Focusing on a practical setting where the effects of some time‐independent covariates on the recurrent events are of primary interest, we review semiparametric regression modelling approaches for panel count data that have been implemented in R package spef . The methods are grouped into two categories depending on whether the examination times are associated with the recurrent event process after conditioning on covariates. The reviewed methods are illustrated with a subset of the data from a skin cancer clinical trial.     

A Microeconomic Analysis of Homelessness: An Empirical Investigation Using Choice-Based Sampling

This study estimates a structural model of the probability of homelessness by drawing data from two sources. The observations on the housed come from the American Housing Survey and the data on the homeless come from a separate study dealing exclusively with the homeless. Since the survey on the homeless targeted a larger fraction of that population, the sample is choice-based. The results suggest that older males with low incomes and high levels of depression are more likely to be homeless. Improvement in the number and quality of homeless shelters is expected to increase substantially the number of homeless. The policy simulations provide support for cash transfers to the very poor to reduce their likelihood of becoming homeless and for a weakening of housing codes to increase the availability of inexpensive, low-quality rental housing.     

Meta‐Regression Analysis: A Quantitative Method of Literature Surveys

Abstract.  Pedagogically, literature reviews are instrumental. They summarize the large literature written on a particular topic, give coherence to the complex, often disparate, views expressed about an issue, and serve as a springboard for new ideas. However, literature surveys rarely establish anything approximating unanimous consensus. Ironically, this is just as true for the empirical economic literature. To harmonize this dissonance, we offer a quantitative methodology for reviewing the empirical economic literature. Meta‐regression analysis (MRA) is the regression analysis of regression analyses. MRA tends to objectify the review process. It studies the processes that produce empirical economic results as though they were any other social scientific phenomenon. MRA provides a framework for replication and offers a sensitivity analysis for model specification. In this brief essay, we propose a new method of reviewing economic literature, MRA, and discuss its potential.     

Application of Shrinkage Techniques in Logistic Regression Analysis: A Case Study

Logistic regression analysis may well be used to develop a predictive model for a dichotomous medical outcome, such as short-term mortality. When the data set is small compared to the number of covariables studied, shrinkage techniques may improve predictions. We compared the performance of three variants of shrinkage techniques: 1) a linear shrinkage factor, which shrinks all coefficients with the same factor; 2) penalized maximum likelihood (or ridge regression), where a penalty factor is added to the likelihood function such that coefficients are shrunk individually according to the variance of each covariable; 3) the Lasso, which shrinks some coefficients to zero by setting a constraint on the sum of the absolute values of the coefficients of standardized covariables.
Logistic regression models were constructed to predict 30-day mortality after acute myocardial infarction. Small data sets were created from a large randomized controlled trial, half of which provided independent validation data. We found that all three shrinkage techniques improved the calibration of predictions compared to the standard maximum likelihood estimates. This study illustrates that shrinkage is a valuable tool to overcome some of the problems of overfitting in medical data.     

Regional Unemployment in Transitional China: A Theoretical and Empirical Analysis

The purpose of this paper is to explain the pattern of regional unemployment in transitional China. A model is developed to explore how urban unemployment in the provinces is influenced by peasants' wages, formal sector wages, and the size of the formal sector. Evidence from panel data suggests that a significant indicator of high unemployment rates is greater Urban–Rural Income Inequality within the province. The hypothesis is that the urban–rural income gap produces migration, and more rural migrants substitute for urban workers, causing further urban unemployment.Since the economic reforms began in 1978, the non-state owned enterprises have been carrying an increasing weight in the economy, and they have contributed significantly to the rapid economic growth of China. Empirical evidence shows that economic reforms have reduced unemployment. The provinces that are still heavily dependent on the state sector are therefore more likely to experience higher unemployment.     

Absorptive Capacity and Productivity Spillovers from FDI: A Threshold Regression Analysis*

This paper explores whether the effect of foreign direct investment (FDI) on productivity growth is dependent on absorptive capacity using recently developed threshold regression techniques. In manufacturing sectors where technology‐exploiting multinationals are prevalent, the results point to the presence of nonlinear threshold effects: the productivity benefit from FDI increases with absorptive capacity until some threshold level beyond which it becomes less pronounced. But there is also a minimum absorptive capacity threshold level below which productivity spillovers from FDI are negligible or even negative. On the contrary, no evidence of productivity spillovers is found in sectors where FDI appears to be motivated by technology‐sourcing considerations.     

中国上市公司IPO后经营业绩变化情况的分析——来自A股公司的经验数据

本文以1999￣2000年在沪、深两市首发上市的216家A股公司为样本,以这些公司从首发前一年延伸至2003年的数据,研究了中国A股上市公司IPO前1年至后3年的收入、成本、费用及效益的变化。研究发现:与上市前相比,中国A股公司IPO后的主营业务收入、主营业务成本、期间费用等绝对数指标均显著上升,而主营业务收入营业利润率却显著下降;造成业绩下降因素很多,其中最直接的主要原因是公司上市后成本、费用的大幅度上升。     

中外个税比较分析

论文运用比较分析法,较为系统地研究了中外个税在税制模式、费用扣除和税收征管等方面的异同,探寻了各自的优缺点,为进一步完善个税改革奠定了理论基础。论文的新意在于提出以家庭为主的个税制。笔者认为,税制,特别是个税税制关系到分配公平和社会正义,合理的个税税制,可促进社会和谐和持续发展,研究的重要性也不辩自明。     

基于UIC406的铁路能力消耗与能力使用分析

以UIC406为基础,从能力消耗与能力使用两个层面展开论述,从时刻表规划的角度出发把握铁路能力的动态性与不确定性,进行能力的计算与评估,对于提高能力计算与评估结果的准确性有极为重要的意义。     

城乡统筹发展背景下的农民增收策略研究

"三农"问题的根本是农民问题,解决农民问题的关键就是解决农民的收入问题。文章论证了增加农民收入的重要意义,并提出了切实增加农民收入的具体解决方案。     

基于博弈的高速公路联网收费收入分配研究

综合考虑谈判破裂风险、贴现率、交通量、交通量分配和投资比重等因素,将高速公路联网收费收入分配看成一种讨价还价过程,结合投资人的投资额度和顺序,构建高速公路联网收费收入博弈分配模型,提出一种基于博弈的收费收入分配方法。实例表明:先投资、更乐观的投资者能获得更多的收入占有份额;政府对交通量分配实行明补或暗补措施,可以有效提高投资者的积极性。     

企业收入实现及“实现”概念探析——论企业收入的已实现、可实现和未实现

"实现"是财务会计的一个重要概念,是收入实现(因出售商品或劳务而形成的现金流入)的基本标准。在对企业收入实现计量及收入确认标准等问题进行分析探讨后发现,财务会计中存在确认收入的三个"实现"概念,即已实现、可实现和未实现。已实现是已获得的现金净流入,可实现是有望在确定期间内按确定金额获得的现金净流入,未实现是期望获得的未来的现金净流入。     

HAC估计的性质与修正及其在伪回归中的应用研究

本文通过频谱分析揭示了预白化HAC和参数化VARHAC比非参数HAC具有明显优势,并指出传统预白化HAC法由于使用了存在有限样本偏差的OLS法来估计自回归参数,导致其存在有限样本偏差,由此构造的t统计量具有过度拒绝原假设倾向。为了减少预白化HAC的偏差,将OLS估计量的线性修正（LBC）和非线性修正（NBC）嵌入到预白化HAC中,研究表明该法能大大减少长期方差的估计偏差,并通过蒙特卡洛模拟证实对预白化HAC的修正估计能有效减少平稳过程之间的伪回归概率,从而提高了回归模型统计推断的可靠性。     

The Distributional Effects of Tax‐benefit Policies under New Labour: A Decomposition Approach*

I revisit the distributional effects of tax‐benefit policy reforms under New Labour using counterfactual microsimulations embedded in a Shapley decomposition of time change in inequality and poverty indices. This makes it possible to quantify the relative effect of policy changes compared to all other changes, and to check the sensitivity of this policy effect to the use of (i) income vs. price indexation, and (ii) base vs. end period data. Inequality and poverty depth would have increased, and the sharp fall in child poverty would not have occurred, had the reforms of income support and tax credits not been implemented.     

Nonuniform DIF Detection using Discriminant Logistic Analysis and Multinomial Logistic Regression: A comparison for polytomous items

This study focused on the effectiveness in nonuniform polytomous item DIF detection using Discriminant Logistic Analysis (DLA) and Multinomial Logistic Regression (MLR). A computer simulation study was conducted to compare the effect of using DLA and MLR, applying either an iterative test purification procedure or non-iterative to detect nonuniform DIF. The conditions under study were: DIF effect size (0.5, 1.0 and 1.5), sample size (500 and 1000), percentage of DIF items in the test (0, 10 and 20%) and DIF type (nonuniform). The results suggest that DLA is more accurate than MLR in detecting DIF. However, the purification process only improved the correct detection rate when MLR was applied. The false positive rates for both procedures were similar. Moreover, when the test purification procedure was used, the proportion of non-DIF items that were detected as DIF decreased for both procedures, although the false positive rates were smaller for DLA than for MLR.