武汉城市圈城镇居民人均可支配收入差距检验

文章运用2000～2007年间统计数据.对武汉城市圈各城市城镇居民人均可支配收入差距,分别从绝对差距、相对差距、库兹涅茨"倒U"型假说、收敛性等角度,进行了详细的分析.结果表明:无论是绝对差距,还是相对差距,考察期间都是扩大的;"倒U"型假说通过检验;δ-收敛证明了可支配收入的离差是扩大的,绝对β-收敛证实了其存在收敛性;在加入反映城镇居民人均可支配收入差距的经济结构变量和产权结构变量后,三个模型检验都证明了其存在务件β-收敛.且二产占GDP比重和三产占GDP比重与各城市城镇居民人均可支配收入增长速度之间正相关,而国有单位职工占总人口比重值则与其增长速度存在负相关性.     

倒U型多拐点社会问题的测度

经济增长与收入分配不平等之间的倒U假说一直是学者们争论的焦点。基于重庆市率先提出将基尼系数纳入政绩考核这一举措,文章对倒U假说这一旧题采用重庆市数据进行验证。在讨论经济增长与基尼系数之间的关系时,文章采用较为前沿的非参数局部多项式估计法,对重庆市城镇居民基尼系数发展特点及趋势进行了很好地归纳与预测。     

全国基尼系数与经济增长关系研究—基于变参数模型

本文利用1986-2007年的时间序列数据,对全国基尼系数与人均GDP建立了变系数模型,以分析经济增长与基尼系数之间的关系。应用格兰杰因果检验与协整检验得到,人均GDP是全国基尼系数的单向格兰杰原因,并且两者具有协整关系。最后,估计变参数模型,结果表明：当人均GDP每增长1％,将引起基尼系数的增加值,是服从随机游走形式的。     

长三角地区城镇居民人均可支配收入的差距分析

文章对改革开放以来长三角地区16城市城镇居民人均可支配收入差距的演变,分别从绝对差距、相对差距、"倒U"型假说检验、收敛性等多角度进行了详细的分析。     

我国区域经济的不均衡、极化及演化研究

区域经济差异与极化是研究的热点问题,文章首先分析了我国经济发展的现状,结果发现全国尺度上地市间的差距在拉大,经济极化程度加剧;其次利用Gini系数来计算1994～2004年我国的不均衡程度,用核密度参数法、Wolfson指数来计算极化情况,分析了各省的经济不均衡和极化程度,发现西部省份的经济不均衡程度与极化程度并存,中部省份的不均衡和极化程度相对较弱,东部发达地区存在了两个极端;再次分析了Gini系数和Wolfson指数随时间变化趋势;最后在截面数据上研究了Gini系数和Wolfson指数同人均GDP的关系,发现各省Gini系数同人均GDP呈现近似三次多项式的分布.Wolfson指数同人均GDP无显著关系.     

工业化、出口、经济增长与环境压力的实证考量

文章以中国1983~2010年的人均工业废气排放量作为环境污染指标,人均工业能耗、人均出口量与人均实际GDP为解释变量构建了半参数库兹涅茨曲线模型。结果表明,中国的环境污染主要来自于工业化生产;出口加剧了环境的压力;人均实际GDP与环境压力二者之间不符合EKC假说;剔除工业化与出口因素影响后人均实际GDP与环境压力呈斜"U"型,其边际效应曲线呈"N"型,2007年后的人均实际GDP对环境压力为正的边际效应且迅速增加。     

生态足迹视角下经济增长与环境关系的实证检验

从生态足迹的角度,根据1978～2008年全国30个省(区、市)的面板数据,运用两阶段最小二乘法,探讨了我国经济与资源环境之间是否存在倒"U"型关系,结果发现:我国资源环境随着人均GDP的增长呈现倒"U"型,处于拐点时人均GDP为19672.6元,表明我国整体上已于2008年跨过拐点,进入经济与环境协调发展期。并通过动态面板的广义矩估计,详细分析了各类型生态足迹对我国区域经济增长的不同影响。     

环境库兹涅茨曲线研究——基于生活污染和空间计量方法

环境库兹涅茨曲线是环境经济学中的一个经典假说,大部分的研究假定污染排放不受相邻国家或地区的影响。基于中国1998—2007年省级生活污染排放数据,使用空间计量方法对EKC模型进行实证分析,结果显示：中国生活污染排放的空间依赖关系并不显著;同时,人均生活污水、人均生活污水中COD和人均生活SO2排放量与人均GDP之间为“倒U型”曲线关系,人均生活烟尘排放量与人均GDP之间为“倒N型”曲线关系,这说明就生活染污而言,中国的经济发展与环境改善是相互协调的。     

我国生态环境与经济协调发展的实证分析

文章利用我国省际面板数据,对改革开放30年来我国经济增长与环境污染关系进行实证检验,并分析环境污染对经济发展的动态影响.结果表明:我国工业二氧化硫排放量与人均GDP之间存在显著的倒U型关系,且绝大部分地区环境一收入关系位于环境曲线左半段;工业废水和工业固体废物排放量变动对经济增长均产生负面效应,且后者的负面影响相对较小.     

中国农村居民信贷与经济增长关系的实证研究

本文针对当前中国的热点问题——社会主义新农村建设,阐述了农村居民信贷与经济增长的主要关系,设定了农村经济增长与信贷之间的理论模型,并利用格兰杰因果关系检验对中国农村居民信贷与经济增长进行因果检验,以此为基础建立了中国农村经济增长与农村居民信贷的实证模型。     

Age,Trend, and Cohort Effects in a Macro Model of Canadian Expenditure Patterns

A quadratic almost ideal demand system allowing for age, cohort, and trend effects is developed at the macro level. The model is estimated by maximum likelihood, using a three-tier iterative/search method applied to pooled 1961–1992 time series for five regions of Canada and six categories of expenditure. Hypothesis tests indicate support for the model specification. Elasticities are compared with those reported in other studies, with special attention to food. Effects of demographic and trend variables on elasticities and expenditure shares are investigated. An overall conclusion is that such effects can be very important in a macro demand system.     

Test of two hypotheses explaining the size of populations in a system of cities

Two classical hypotheses are examined about the population growth in a system of cities: Hypothesis 1 pertains to Gibrat's and Zipf's theory which states that the city growth–decay process is size independent; Hypothesis 2 pertains to the so-called Yule process which states that the growth of populations in cities happens when (i) the distribution of the city population initial size obeys a log-normal function, (ii) the growth of the settlements follows a stochastic process. The basis for the test is some official data on Bulgarian cities at various times. This system was chosen because (i) Bulgaria is a country for which one does not expect biased theoretical conditions; (ii) the city populations were determined rather precisely. The present results show that: (i) the population size growth of the Bulgarian cities is size dependent, whence Hypothesis 1 is not confirmed for Bulgaria; (ii) the population size growth of Bulgarian cities can be described by a double Pareto log-normal distribution, whence Hypothesis 2 is valid for the Bulgarian city system. It is expected that this fine study brings some information and light on other usually considered to be more pertinent countries of city systems.     

Testing equality of two normal covariance matrices with monotone missing data

Abstract

The problem of testing equality of two multivariate normal covariance matrices is considered. Assuming that the incomplete data are of monotone pattern, a quantity similar to the Likelihood Ratio Test Statistic is proposed. A satisfactory approximation to the distribution of the quantity is derived. Hypothesis testing based on the approximate distribution is outlined. The merits of the test are investigated using Monte Carlo simulation. Monte Carlo studies indicate that the test is very satisfactory even for moderately small samples. The proposed methods are illustrated using an example.     

Solving the Stochastic Growth Model by Linear-Quadratic Approximation

I describe a method that can be used to approximate the solution of the stochastic growth model. The method relies on approximating the return and transition functions of the original problem by taking second-order and first-order Taylor expansions around the steady state of the system. The result is the optimal linear regulator problem.     

适应性市场假说:来自中国商品期货市场的证据

适应性市场假说认为交易策略随交易者行为适应环境变化而演化。研究10种技术交易策略在中国商品期货上的业绩表现,来检验是否符合适应性市场假说。实证结果显示简单知名的策略如MACD、RSI、BOLL、MA+MACD平均收益为负,复杂冷门的策略如MA组合、KD、SAR、ADX+KD、MA(5,20)、ATR平均收益显著为正,且交易策略绩效随价格趋势周期性波动。总之,技术交易规则超额收益在中国商品期货市场依然存在,结论和适应性市场假说一致。     

Optimal designs for non-competitive enzyme inhibition kinetic models

In this paper, we present a new method for determining optimal designs for enzyme inhibition kinetic models, which are used to model the influence of the concentration of a substrate and an inhibition on the velocity of a reaction. The approach uses a nonlinear transformation of the vector of predictors such that the model in the new coordinates is given by an incomplete response surface model. Although there exist no explicit solutions of the optimal design problem for incomplete response surface models so far, the corresponding design problem in the new coordinates is substantially more transparent, such that explicit or numerical solutions can be determined more easily. The designs for the original problem can finally be found by an inverse transformation of the optimal designs determined for the response surface model. We illustrate the method determining explicit solutions for the D-optimal design and for the optimal design problem for estimating the individual coefficients in a non-competitive enzyme inhibition kinetic model.     

A sequence of improved standard errors under heteroskedasticity of unknown form

The linear regression model is commonly used by practitioners to model the relationship between the variable of interest and a set of explanatory variables. The assumption that all error variances are the same (homoskedasticity) is oftentimes violated. Consistent regression standard errors can be computed using the heteroskedasticity-consistent covariance matrix estimator proposed by White (1980). Such standard errors, however, typically display nonnegligible systematic errors in finite samples, especially under leveraged data. Cribari-Neto et al. (2000) improved upon the White estimator by defining a sequence of bias-adjusted estimators with increasing accuracy. In this paper, we improve upon their main result by defining an alternative sequence of adjusted estimators whose biases vanish at a much faster rate. Hypothesis testing inference is also addressed. An empirical illustration is presented.     

Prior Density Selection as a Particular Case of Bayesian Model Selection: A Predictive Approach

A Bayesian model consists of two elements: a sampling model and a prior density. The problem of selecting a prior density is nothing but the problem of selecting a Bayesian model where the sampling model is fixed. A predictive approach is used through a decision problem where the loss function is the squared L ² distance between the sampling density and the posterior predictive density, because the aim of the method is to choose the prior that provides a posterior predictive density as good as possible. An algorithm is developed for solving the problem; this algorithm is based on Lavine's linearization technique.     

MULTIPLE COMPARISONS OF LOG-LIKELIHOODS AND COMBINING NONNESTED MODELS WITH APPLICATIONS TO PHYLOGENETIC TREE SELECTION

We consider multiple comparisons of log-likelihood's to take account of the multiplicity of testings in selection of nonnested models. A resampling version of the Gupta procedure for the selection problem is used to obtain a set of good models, which are not significantly worse than the maximum likelihood model; i.e., a confidence set of models. Our method is to test which model is better than the other, while the object of the classical testing methods is to find the correct model. Thus the null hypotheses behind these two approaches are very different. Our method and the other commonly used approaches, such as the approximate Bayesian posterior, the bootstrap selection probability, and the LR test against the full model, are applied to the selection of molecular phylogenetic tree of mammal species. Tree selection is a version of the model-based clustering, which is an example of nonnested model selection. It is shown that the structure of the tree selection problem is equivalent to that of the variable selection problem of the multiple regression with some constraints on the combinations of the variables. It turns out that the LR test rejects all the possible trees because of the misspecification of the models, whereas our method gives a reasonable confidence set. For a better understanding of the uncertainty in the selection, we combine the maximum likelihood estimates (MLE's) of the trees to obtain the full model that includes the trees as the submodels by using a linear approximation of the parametric models. The MLE of the phylogeny is then represented as a network of species rather than a tree. A geometrical interpretation of the problem is also discussed.     

Beta Regression for Modelling Rates and Proportions

This paper proposes a regression model where the response is beta distributed using a parameterization of the beta law that is indexed by mean and dispersion parameters. The proposed model is useful for situations where the variable of interest is continuous and restricted to the interval (0, 1) and is related to other variables through a regression structure. The regression parameters of the beta regression model are interpretable in terms of the mean of the response and, when the logit link is used, of an odds ratio, unlike the parameters of a linear regression that employs a transformed response. Estimation is performed by maximum likelihood. We provide closed-form expressions for the score function, for Fisher's information matrix and its inverse. Hypothesis testing is performed using approximations obtained from the asymptotic normality of the maximum likelihood estimator. Some diagnostic measures are introduced. Finally, practical applications that employ real data are presented and discussed.