域产业转移效率研究及其对废气排放的影响

本文选取2005-2014年我国29个省(市)的面板数据,并将其分为东、中、西部三大区域,通过DEA-Malmquist模型对各个区域的产业转移效率从动、静两个方面进行了分析研究,同时,运用SYS-GMM模型探讨了产业转移对废气排放的影响.研究发现:东部地区在综合效率和纯技术效率方面都普遍高于中西部地区,而规模效率则是中部地区最高;三大区域的Malmquist生产率都呈不同程度的下降趋势;此外,产业转移和技术进步分别对废气排放也有显著的抑制作用,技术进步还能够提高产业转移过程中对废气治理的效率.     

分布未知情况下的空间滞后模型检验

 在扰动项分布未知的情况下,直接采用传统的空间模型检验方法是存在问题的。针对传统空间模型检验方法的不足,本文以Lee和Yu（2010）的研究为基础,采用Lee和Liu（2006）提出的最优矩条件,构造分布未知情况下空间滞后模型的稳健检验统计量。这种检验方法仅需参数的一致估计量,便于计算。蒙特卡罗结果表明,在小样本情况下,本文提出的检验有良好的性质,且明显优于Saavedra（2003）提出的检验。     

金融发展、技术进步与产业升级

 金融发展对我国的技术进步和产业升级均具有十分重要的作用。本文以我国2000-2008年28个省市数据为样本,运用“残差结构一阶自相关的固定效应面板数据方法”估算TFP和各个地区的金融发展水平,然后运用两步GMM系统估计方法检验了金融发展、技术进步与产业升级三者之间的关系,结果发现：在控制了相关变量后,金融发展对技术进步和产业升级都具有正向的促进作用。     

GMM Estimation of Autoregressive Roots Near Unity with Panel Data

This paper investigates a generalized method of moments (GMM) approach to the estimation of autoregressive roots near unity with panel data and incidental deterministic trends. Such models arise in empirical econometric studies of firm size and in dynamic panel data modeling with weak instruments. The two moment conditions in the GMM approach are obtained by constructing bias corrections to the score functions under OLS and GLS detrending, respectively. It is shown that the moment condition under GLS detrending corresponds to taking the projected score on the Bhattacharya basis, linking the approach to recent work on projected score methods for models with infinite numbers of nuisance parameters (Waterman and Lindsay (1998)). Assuming that the localizing parameter takes a nonpositive value, we establish consistency of the GMM estimator and find its limiting distribution. A notable new finding is that the GMM estimator has convergence rate , slower than , when the true localizing parameter is zero (i.e., when there is a panel unit root) and the deterministic trends in the panel are linear. These results, which rely on boundary point asymptotics, point to the continued difficulty of distinguishing unit roots from local alternatives, even when there is an infinity of additional data.     

GMM with Many Moment Conditions

This paper provides a first order asymptotic theory for generalized method of moments (GMM) estimators when the number of moment conditions is allowed to increase with the sample size and the moment conditions may be weak. Examples in which these asymptotics are relevant include instrumental variable (IV) estimation with many (possibly weak or uninformed) instruments and some panel data models that cover moderate time spans and have correspondingly large numbers of instruments. Under certain regularity conditions, the GMM estimators are shown to converge in probability but not necessarily to the true parameter, and conditions for consistent GMM estimation are given. A general framework for the GMM limit distribution theory is developed based on epiconvergence methods. Some illustrations are provided, including consistent GMM estimation of a panel model with time varying individual effects, consistent limited information maximum likelihood estimation as a continuously updated GMM estimator, and consistent IV structural estimation using large numbers of weak or irrelevant instruments. Some simulations are reported.     

Estimation of time-varying coefficient dynamic panel data models

In this paper, we consider dynamic panel data models where the autoregressive parameter changes over time. We propose the GMM and ML estimators for this model. We conduct Monte Carlo simulation to compare the performance of these two estimators. The simulation results show that the ML estimator outperforms the GMM estimator.     

动态面板空间随机前沿模型的参数估计及应用

生产效率一般会受到空间相关性和时间滞后效应的影响,不易准确测算。本文考虑时空双重滞后特征,提出一种动态面板数据空间随机前沿模型,针对模型的内生性问题,借鉴已有的估计方法,本文提出一种广义矩估计方法（Generalized Method of Moments,GMM）,并证明了参数估计的一致性。在应用分析中,利用本文所提出的理论模型实证分析了我国战略性新兴产业发展的效率,该理论模型能够客观、科学地测算技术效率,实证结论验证了理论模型的应用效果。     

Invariant tests based on M-estimators,estimating functions,and the generalized method of moments

We study the invariance properties of various test criteria which have been proposed for hypothesis testing in the context of incompletely specified models, such as models which are formulated in terms of estimating functions (Godambe, 1960) or moment conditions and are estimated by generalized method of moments (GMM) procedures (Hansen, 1982), and models estimated by pseudo-likelihood (Gouriéroux, Monfort, and Trognon, 1984b,c) and M-estimation methods. The invariance properties considered include invariance to (possibly nonlinear) hypothesis reformulations and reparameterizations. The test statistics examined include Wald-type, LR-type, LM-type, score-type, and C(α)?type criteria. Extending the approach used in Dagenais and Dufour (1991), we show first that all these test statistics except the Wald-type ones are invariant to equivalent hypothesis reformulations (under usual regularity conditions), but all five of them are not generally invariant to model reparameterizations, including measurement unit changes in nonlinear models. In other words, testing two equivalent hypotheses in the context of equivalent models may lead to completely different inferences. For example, this may occur after an apparently innocuous rescaling of some model variables. Then, in view of avoiding such undesirable properties, we study restrictions that can be imposed on the objective functions used for pseudo-likelihood (or M-estimation) as well as the structure of the test criteria used with estimating functions and generalized method of moments (GMM) procedures to obtain invariant tests. In particular, we show that using linear exponential pseudo-likelihood functions allows one to obtain invariant score-type and C(α)?type test criteria, while in the context of estimating function (or GMM) procedures it is possible to modify a LR-type statistic proposed by Newey and West (1987) to obtain a test statistic that is invariant to general reparameterizations. The invariance associated with linear exponential pseudo-likelihood functions is interpreted as a strong argument for using such pseudo-likelihood functions in empirical work.     

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。     

A Fast Iterated Bootstrap Procedure for Approximating the Small-Sample Bias

This article proposes a fast approximation for the small sample bias correction of the iterated bootstrap. The approximation adapts existing fast approximation techniques of the bootstrap p-value and quantile functions to the problem of estimating the bias function. We show an optimality result which holds under general conditions not requiring an asymptotic pivot. Monte Carlo evidence, from the linear instrumental variable model and the nonlinear GMM, suggest that in addition to its computational appeal and success in reducing the mean and median bias in identified models, the fast approximation provides scope for bias reduction in weakly identified configurations.