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.     

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.     

Sparse moving maxima models for tail dependence in multivariate financial time series

The multivariate maxima of moving maxima (M4) model has the potential to model both the cross-sectional and temporal tail-dependence for a rich class of multivariate time series. The main difficulty of applying M4 model to real data is due to the estimation of a large number of parameters in the model and the intractability of its joint likelihood. In this paper, we consider a sparse M4 random coefficient model (SM4R), which has a parsimonious number of parameters and it can potentially capture the major stylized facts exhibited by devolatized asset returns found in empirical studies. We study the probabilistic properties of the newly proposed model. Statistical inference can be made based on the Generalized Method of Moments (GMM) approach. We also demonstrate through real data analysis that the SM4R model can be effectively used to improve the estimates of the Value-at-Risk (VaR) for portfolios consisting of multivariate financial returns while ignoring either temporal or cross-sectional tail dependence could potentially result in serious underestimate of market risk.     

Diagnostics for the diagnostics

Abstract

This paper examines the tendency towards income convergence among China's main provinces during the two periods: the pre‐reform period 1953–1977 and the reform period 1978–1997 using the framework of the Solow growth model. The panel data method accounts for not only province‐specific initial technology level but also the heterogeneity of the technological progress rate between the fast‐growing coastal and interior provinces. Estimation problems of weak instruments and endogeneity are addressed by the use of a system generalized method of moments (GMM) estimator. The main empirical finding is that there is a system‐wide income divergence during the reform period because the coastal provinces do not share a common technology progress rate with the interior provinces.     

Consistent GMM Residuals-Based Tests of Functional Form

This paper presents a consistent Generalized Method of Moments (GMM) residuals-based test of functional form for time series models. By relating two moments we deliver a vector moment condition in which at least one element must be nonzero if the model is misspecified. The test will never fail to detect misspecification of any form for large samples, and is asymptotically chi-squared under the null, allowing for fast and simple inference. A simulation study reveals randomly selecting the nuisance parameter leads to more power than supremum-tests, and can obtain empirical power nearly equivalent to the most powerful test for even relatively small n.     

复杂环境下空间滞后模型的稳健LM检验

 针对误差项非正态分布和不同空间布局的复杂环境,本文提出一种新的空间滞后模型的稳健LM检验统计量及其检验方法,对比现有LM检验方法,它不仅消除了空间误差效应对检验的影响,而且克服了传统检验方法会受误差项非正态分布以及不同空间布局差异的影响。同时还具有在小样本情况下表现依然良好的优点。经过蒙特卡罗模拟的结果支持上述论断。     

基于GMM的缺失数据回归模型的半参数估计

响应变量存在数据缺失的情况广泛出现在社会经济研究中,对响应变量存在数据缺失的回归模型提出了一个在矩估计框架下的单一的半参数估计量,这种估计量保留了参数回归估计量与非参数匹配估计量的特性,从而使得该估计量既能在响应变量被观测的子样本中保持较好的拟合性,又能够降低响应变量未被观测的子样本的估计误差,并且证明了这种估计量是一致、渐进正态估计量。     

Unit root test for short panels with serially correlated errors

This paper proposes a unit root test for short panels with serially correlated errors. The proposed test is based on the instrumental variables (IVs) and the generalized method of moments (GMM) estimators. An advantage of the new test over other tests is that it allows for an ARMA-type serial correlation. A Monte Carlo simulation shows that the new test has good finite sample properties. Several methods to estimate the lag orders of the ARMA structure are briefly discussed.     

农村人口年龄结构变动对居民消费的影响研究--以西北五省为例

计划生育政策实施以来，我国人口年龄结构发生了剧烈变动，农村居民消费严重不足，扩大内需成为新常态下我国经济发展的战略方向。基于2001～2013年省际面板数据，运用一步系统广义矩估计法，实证研究了西北五省农村地区人口年龄结构变动对农村居民消费的影响，结果显示：西北五省农村少儿抚养比和农村老年抚养比均与居民消费呈同向变动关系，有效地支持了生命周期假说；城镇化水平与农村居民消费则呈负向关系。     

媒体效应对银行系统性风险的影响

基于中国上市银行2007—2015年的季度面板数据,运用主成分分析法构建新的银行投资者情绪指标,采用SGMM和DGMM等模型估计银行系统性风险的媒体效应。结果发现:以投资者情绪为中介,银行系统性风险有显著的媒体效应,即媒体报道数量越少,投资者情绪越乐观,银行发生系统性风险的可能性越大;媒体报道数量越多,投资者情绪越悲观,发生银行系统性风险的可能性越小。同时,前期的银行系统性风险越大,当期的银行系统性风险也越大;前期的投资者情绪越高涨,当期的投资者情绪也呈现高涨状态。