面板数据双因素误差回归模型序列相关和随机效应的联合LM检验

对面板数据双因素误差回归模型构造了检验序列相关和随机效应的一种联合LM检验,发现该LM统计量也是检验联合假设H0：σμ^2=λ=0的Baltagi-Li LM统计量和检验假设H0：σv^2=λ=0的Breusch-Pagan-LM统计量之和。当面板数据的个体数N充分大时,该联合LM统计量的渐近分布是χ^2（3）分布;无论双因素误差面板数据回归模型的剩余误差项是AR（1）过程还是MA（1）过程,联合LM检验是相同的,即对随机效应和一阶序列相关的联合LM检验是独立于序列相关的形式。     

动态广义最小二乘法在面板协整中的应用研究

在进行非平稳面板数据的协整分析时,使用动态最小二乘法（DOLS）可以有效消除内生性问题,从而得到具有渐进正态分布的统计量。但在小样本条件下,由于可使用解释变量差分项的阶数有限,导致模型中均衡误差项的序列相关,使得DOLS统计量出现严重的检验水平畸变。为此,本文将单一时间序列的动态广义最小二乘法（DGLS）应用于非平稳的同质面板数据模型。在序贯极限分布的条件下,DGLS统计量仍具有正态的条件极限分布。而仿真实验表明,对于非平稳的同质面板数据模型,即使在均衡误差项存在高序列相关的条件下,DGLS统计量仍具有较好的小样本性质。     

面板数据模型的截面相关检验研究

 相关系数的绝对值形式可以很好的避免Pesaran（2004）的CD统计量中异向相关性相互抵消的情况,相应得到一个新的检验面板数据模型扰动项截面相关的统计量。蒙特卡洛模拟显示,无论是在因子模型下还是在空间移动平均模型下,新提出的统计量水平扭曲(size distortion)检验和功效(power)检验表现较好。通过模拟还发现当存在序列相关的扰动项时,先将扰动项进行去序列相关处理可以有效地避免序列相关导致的水平扭曲,并且不会降低统计量的功效。     

具有GARCH（1，1）-正态误差项的非对称单位根检验研究

摘　　要:理论研究表明许多经济变量呈现出非对称的门限自回归（TAR）或动态门限自回归（M-TAR）数据生成机制,因而非对称单位根检验就成为该领域的主要研究方向之一。本文对非对称单位根检验Enders-Granger方法在GARCH（1,1）-正态误差项下的检验水平与检验势作了系统的仿真研究。研究表明：GARCH（1,1）-正态误差项的TAR或M-TAR模型会对该方法的检验水平和检验势产生重要影响。     

基于二氧化碳排放量驱动因素分析

文章基于我国西部11个省份2000～2009年的面板数据构建了计量模型,分析了我国西部各省CO2排放量与GDP、人口、产业结构和能源强度的关系.结果表明:GDP和能源强度对西部11个省份的CO2排放量有显著影响;人口对内蒙的CO2排放量有显著影响;第二产业的比重对四川、贵州、甘肃、宁夏和内蒙的CO2排放量有显著影响,并根据分析结果对我国西部地区可持续发展提出了如下的对策建议:发展低碳经济;控制人口;优化产业结构;发展新能源等.     

中国CO2排放、煤炭消费及经济增长的协整检验

文章利用中国1960-2008年CO2排放、煤炭消费及经济增长的时间序列数据,建立了基于EKC曲线的协整模型,并利用ARDL的方法分析了三者之间的关系。研究结果表明中国CO2排放库兹涅茨曲线存在拐点,CO2排放与煤炭消费及经济增长之间存在着长期稳定的协整关系。煤炭消费及经济增长对CO2排放均具有正向冲击作用,其影响逐步加大,分别到3期和5期达到最大,然后衰减,经济增长对CO2排放具有更长期的影响。     

技术进步对中国二氧化碳排放的影响

 本文将内生增长理论与环境污染模型相结合,运用1997-2007年中国省市面板数据,对我国二氧化碳排放的影响因素进行实证分析,着重研究了技术进步（包括自主研发和技术引进）对CO2减排的贡献。本文得到如下结论:总体而言,我国CO2排放总量上升与经济总量的扩大、工业化水平的提高以及贸易自由化进程的加快等因素正相关;其次,自主研发、技术引进对我国的CO2减排具有显著的促进作用,但自主研发对引进技术的吸收能力较低, 在促进生产率提高和节能减排方面,与技术引进形成互补优势的能力尚待提高;再次,技术进步对我国CO2排放的影响表现出明显的地区差异。     

二氧化碳排放强度的实证研究

 二氧化碳(CO2)排放与减排问题已成为全球关注的焦点,而作为全球最大CO2排放量的中国,尤其值得研究。本文运用基于投入产出模型的结构因素分解法对中国1997-2007年CO2排放强度进行了因素分析。与已有研究相比,本文既测度了直接CO2排放,也测度了间接CO2排放;并考虑了各类不同种类能源的碳排放因子的差别,因此更具准确性和合理性。研究结果表明,生产模式的转变是CO2排放强度降低的主要原因,尤其是能源强度;而需求模式的作用较小。在此基础上,提出中国在未来发展中不仅要注重生产领域的减排,更应重视培养清洁需求的模式。     

鞅差序列误差下线性模型的经验似然推断

文章在线性模型误差项为鞅差序列情形下,应用经验似然方法得到了关于回归系数β的对数经验似然比统计量渐近服从菇分布,从而得到了关于β的置信域。     

我国能源相关二氧化碳排放的影响因素贡献度分析

文章基于完全分解分析模型和比例偏差模型建立一个混合模型,并应用该模型研究了我国二氧化碳排放相关影响因素的发展趋势。我们得出以下结论：（1）排放系数对我国碳排放的影响比较稳定;（2）虽然在1996～2005年间,能源强度是驱动我国二氧化碳减排的最主要因素,但是它对减排的影响贡献度呈降低趋势;（3）从长远来看,产业结构变化对碳减排的影响强度将持续增加;（4）经济活动水平对碳排放影响的增长强度是最大的。     

主权货币碳值评估及影响因素研究——以世界31个CO_2主要排放经济体为例

测算并比较了1999-2012年世界31个CO_2主要排放经济体的主权货币碳值。通过构建静态面板模型,实证研究显示,汇率水平、技术进步、人口数量、固定资本形成总额、城镇化水平、森林面积,以及外贸依存度、经济发展水平、股市市值、外汇储备与消费状况,均是影响主权货币碳值水平的重要因素。分经济集团实证估计结果则表明,不同因素对不同经济体的货币碳值影响作用并不一致,存在明显的区域异质性。     

A Factor-Adjusted Multiple Testing Procedure With Application to Mutual Fund Selection

In this article, we propose a factor-adjusted multiple testing (FAT) procedure based on factor-adjusted p-values in a linear factor model involving some observable and unobservable factors, for the purpose of selecting skilled funds in empirical finance. The factor-adjusted p-values were obtained after extracting the latent common factors by the principal component method. Under some mild conditions, the false discovery proportion can be consistently estimated even if the idiosyncratic errors are allowed to be weakly correlated across units. Furthermore, by appropriately setting a sequence of threshold values approaching zero, the proposed FAT procedure enjoys model selection consistency. Extensive simulation studies and a real data analysis for selecting skilled funds in the U.S. financial market are presented to illustrate the practical utility of the proposed method. Supplementary materials for this article are available online.     

Empirical likelihood for heteroscedastic partially linear errors-in-variables model with α-mixing errors

In this paper, we apply the empirical likelihood method to heteroscedastic partially linear errors-in-variables model. For the cases of known and unknown error variances, the two different empirical log-likelihood ratios for the parameter of interest are constructed. If the error variances are known, the empirical log-likelihood ratio is proved to be asymptotic chi-square distribution under the assumption that the errors are given by a sequence of stationary α-mixing random variables. Furthermore, if the error variances are unknown, we show that the proposed statistic is asymptotically standard chi-square distribution when the errors are independent. Simulations are carried out to assess the performance of the proposed method.     

Instrumental variable-based empirical likelihood inferences for varying-coefficient models with error-prone covariates

This paper presents the empirical likelihood inferences for a class of varying-coefficient models with error-prone covariates. We focus on the case that the covariance matrix of the measurement errors is unknown and neither repeated measurements nor validation data are available. We propose an instrumental variable-based empirical likelihood inference method and show that the proposed empirical log-likelihood ratio is asymptotically chi-squared. Then, the confidence intervals for the varying-coefficient functions are constructed. Some simulation studies and a real data application are used to assess the finite sample performance of the proposed empirical likelihood procedure.     

Empirical likelihood for partially linear varying-coefficient models with missing response variables and error-prone covariates

In this study, the empirical likelihood method is applied to the partially linear varying-coefficient model in which some covariates are measured with additive errors and the response variable is sometimes missing. Based on the correction-for-attenuation technique, we define an empirical likelihood-based statistic for the parametric component and show that its limiting distribution is chi-square distribution. The confidence regions of the parameters are constructed accordingly. Furthermore, a simulation study is conducted to evaluate the performance of the proposed method.     

Variance Estimation in Spatial Regression Using a Non-parametric Semivariogram Based on Residuals

Abstract.  The empirical semivariogram of residuals from a regression model with stationary errors may be used to estimate the covariance structure of the underlying process. For prediction (kriging) the bias of the semivariogram estimate induced by using residuals instead of errors has only a minor effect because the bias is small for small lags. However, for estimating the variance of estimated regression coefficients and of predictions, the bias due to using residuals can be quite substantial. Thus we propose a method for reducing this bias. The adjusted empirical semivariogram is then isotonized and made conditionally negative-definite and used to estimate the variance of estimated regression coefficients in a general estimating equations setup. Simulation results for least squares and robust regression show that the proposed method works well in linear models with stationary correlated errors.     

Empirical likelihood for high-dimensional partially linear model with martingale difference errors

In this paper, we focus on the empirical likelihood (EL) inference for high-dimensional partially linear model with martingale difference errors. An empirical log-likelihood ratio statistic of unknown parameter is constructed and is shown to have asymptotically normality distribution under some suitable conditions. This result is different from those derived before. Furthermore, an empirical log-likelihood ratio for a linear combination of unknown parameter is also proposed and its asymptotic distribution is chi-squared. Based on these results, the confidence regions both for unknown parameter and a linear combination of parameter can be obtained. A simulation study is carried out to show that our proposed approach performs better than normal approximation-based method.     

中国能源消费CO2排放关键部门的识别与减排对策

实现低碳经济发展目标不仅依赖于节能减排技术的提高,而且需要调整需求结构。使用基于投入产出分析法的最终需求拉动CO2排放的弹性系数指标,从前向关联和后向关联结合的视角,构建一个基于碳减排的关键部门识别模型。研究表明,根据总影响和分布影响两个指标将中国29个部门分为4类,即关键部门、本部门需求视角的相关部门、其他部门需求视角的相关部门和不相关部门,不同类别的部门最终需求对CO2排放的影响程度有显著差异且作用机制不同,必须区分行业特点有针对性地设计节能减排政策。     

Randomized quantile regression estimation for heteroskedastic non parametric model

In this paper, we propose robust randomized quantile regression estimators for the mean and (condition) variance functions of the popular heteroskedastic non parametric regression model. Unlike classical approaches which consider quantile as a fixed quantity, our method treats quantile as a uniformly distributed random variable. Our proposed method can be employed to estimate the error distribution, which could significantly improve prediction results. An automatic bandwidth selection scheme will be discussed. Asymptotic properties and relative efficiencies of the proposed estimators are investigated. Our empirical results show that the proposed estimators work well even for random errors with infinite variances. Various numerical simulations and two real data examples are used to demonstrate our methodologies.     

土地城市化推进的空间减排效应:内在机制与中国经验

本文基于土地利用方式转变影响产业结构的分析思路,将空间碳排放纳入动态最优化模型,理论剖析土地城市化推进的空间减排效应及其内在机制,进而将半参数与参数空间模型相结合实证考察不同类型土地城市化的空间减排效应,并重点检验产业结构的中介效应。研究发现:就全国而言,土地城市化与碳排放整体上存在倒U型关系,相较于以建成区面积表征的广度土地城市化,夜间卫星灯光数据表征的深度土地城市化与碳排放的倒U型关系更加典型且边际减排效应更为显著;分区域来看,仅东部地区土地城市化与碳排放保持稳健的倒U型关系,中部和西部地区均呈现正向单调关系;长期来看,广度土地城市化对周边地区碳排放具有抑制效应,而深度土地城市化则具有持续的促增效果;产业结构在土地城市化发挥空间减排效应的过程里中介作用显著,且这一中介效应具有非线性特征;土地城市化具有空间减排和产业结构调整的双重效应。本文据此提出,土地城市化推进应基于地方发展现状积极调整推进模式,强化区域联动发展和协同减排,同时把握产业结构转型升级的关键渠道,拓展土地城市化空间减排效应发挥的多元路径。