环境规制强度与经济增长——基于生产性资本和健康人力资本视角

近年来，环境保护与经济增长之间的"两难悖论"引起了广泛的关注，环境规制强度如何影响经济增长的问题亟待解决。本文基于代际交替模型（OLG），将环境规制、健康人力资本及生产性资本纳入分析框架，指出环境规制通过影响健康人力资本和生产性资本投资的方式对经济增长产生正负两方面影响。当环境规制强度较低时，环境规制强度与经济增长率正相关；当环境规制强度过高时，则与经济增长率负相关，存在一个最优环境规制强度。在此基础上，利用我国1997-2015年30个省级的面板数据，通过广义矩估计进行实证研究，验证了在我国存在这种显著的"倒U型"的非线性关系。     

GMM estimation of a realized stochastic volatility model: A Monte Carlo study

This article investigates alternative generalized method of moments (GMM) estimation procedures of a stochastic volatility model with realized volatility measures. The extended model can accommodate a more general correlation structure. General closed form moment conditions are derived to examine the model properties and to evaluate the performance of various GMM estimation procedures under Monte Carlo environment, including standard GMM, principal component GMM, robust GMM and regularized GMM. An application to five company stocks and one stock index is also provided for an empirical demonstration.     

利率期限结构模型估计结果影响因素经验研究

本文首先将利率期限结构模型分成了四类,并总结了国内外利率期限结构模型的估计方法。作者利用中美利率数据证明了利率市场的有效性,估计方法和数值优化算法都会对模型估计结果产生影响。实证结果表明,利用所有市场利率数据的新估计方法得到的参数会更加准确,可以消除利率市场的套利机会;遗传算法的估计结果不太稳定,单纯形法估计结果对初始值比较敏感,而矩形分割法的估计结果最为稳健。     

基于GMM的教育投资与经济增长分析

教育投资对经济增长具有促进作用已得到公认。以我国各个省市区作为横截面单元．利用各个横截面单元1998—2007年的样本组成的面板数据,建立动态面板数据模型采用广义矩估计方法来分析教育投资对经济增长的动态效应,发现教育投资对经济增长具有长期的促进作用,并且在逐年增强;分地区来看,略有差异。     

公共服务供给模式的多元化

以创新社会管理思想为指引,以政府部门购买公共服务为范例,以“石拱桥原理”作为分析工具,揭示公共服务提供模式创新的现实路径:人民群众监督政府,促使政府购买服务并监管社会力量,向人民群众提供优质服务,否则将承担相应的责任,以期从根本上提升当前公共服务供给绩效。     

A comparison of LS/ML and GMM estimation in a simple AR(1) model

This paper compares least squares (LS)/maximum likelihood (ML) and generalised method of moments (GMM) estimation in a simple. Gaussian autoregressive of order one (AR(1)) model. First, we show that the usual LS/ML estimator is a corner solution to a general minimisation problem that involves two moment conditions, while the new GMM we devise is not. Secondly, we examine asymptotic and finite sample properties of the new GMM estimator in comparison to the usual LS/ML estimator in a simple AR(1) model. For both stable and unstable (unit root) specifications, we show asymptotic equivalence of the distributions of the two estimators. However, in finite samples, the new GMM estimator performs better.     

Minimum Divergence,Generalized Empirical Likelihoods,and Higher Order Expansions

This article studies the minimum divergence (MD) class of estimators for econometric models specified through moment restrictions. We show that MD estimators can be obtained as solutions to a tractable lower dimensional optimization problem. This problem is similar to the one solved by the generalized empirical likelihood estimators of Newey and Smith (2004 Newey , W. K. , Smith , R. J. ( 2004 ). Higher order properties of GMM and Generalized Empirical Likelihood estimators . Econometrica 72 : 219 – 255 .[Crossref], [Web of Science ®] , [Google Scholar]), but it is equivalent to it only for a subclass of divergences. The MD framework provides a coherent testing theory: tests for overidentification and parametric restrictions in this framework can be interpreted as semiparametric versions of Pearson-type goodness of fit tests. The higher order properties of MD estimators are also studied and it is shown that MD estimators that have the same higher order bias as the empirical likelihood (EL) estimator also share the same higher order mean square error and are all higher order efficient. We identify members of the MD class that are not only higher order efficient, but also, unlike the EL estimator, well behaved when the moment restrictions are misspecified.     

广义矩估计的延伸——广义经验似然估计

从广义矩估计(GMM)到广义经验似然估计(GEL)的发展,是由于GMM估计量小样本性质的不足,促使人们寻求方法的改进和拓展。通过必要的证明和推导,详细解析GEL类估计量(包括EL,ET,CUE)的逻辑关系和数理结构,认识GEL的内在本质,并运用随机模拟方法证实了在小样本场合GEL类估计量比GMM估计量具有更小的估计偏差和均方误差,即GEL类估计改进了GMM估计的小样本性质。     

GMM inference in spatial autoregressive models

In this study, we investigate the finite sample properties of the optimal generalized method of moments estimator (OGMME) for a spatial econometric model with a first-order spatial autoregressive process in the dependent variable and the disturbance term (for short SARAR(1, 1)). We show that the estimated asymptotic standard errors for spatial autoregressive parameters can be substantially smaller than their empirical counterparts. Hence, we extend the finite sample variance correction methodology of Windmeijer (2005 Windmeijer, F. (2005). A finite sample correction for the variance of linear efficient two-step GMM estimators. Journal of Econometrics 126(1):25–51.[Crossref], [Web of Science ®] , [Google Scholar]) to the OGMME for the SARAR(1, 1) model. Results from simulation studies indicate that the correction method improves the variance estimates in small samples and leads to more accurate inference for the spatial autoregressive parameters. For the same model, we compare the finite sample properties of various test statistics for linear restrictions on autoregressive parameters. These tests include the standard asymptotic Wald test based on various GMMEs, a bootstrapped version of the Wald test, two versions of the C(α) test, the standard Lagrange multiplier (LM) test, the minimum chi-square test (MC), and two versions of the generalized method of moments (GMM) criterion test. Finally, we study the finite sample properties of effects estimators that show how changes in explanatory variables impact the dependent variable.