Inference for Income Distributions Using Grouped Data

We develop a general approach to estimation and inference for income distributions using grouped or aggregate data that are typically available in the form of population shares and class mean incomes, with unknown group bounds. We derive generic moment conditions and an optimal weight matrix that can be used for generalized method-of-moments (GMM) estimation of any parametric income distribution. Our derivation of the weight matrix and its inverse allows us to express the seemingly complex GMM objective function in a relatively simple form that facilitates estimation. We show that our proposed approach, which incorporates information on class means as well as population proportions, is more efficient than maximum likelihood estimation of the multinomial distribution, which uses only population proportions. In contrast to the earlier work of Chotikapanich, Griffiths, and Rao, and Chotikapanich, Griffiths, Rao, and Valencia, which did not specify a formal GMM framework, did not provide methodology for obtaining standard errors, and restricted the analysis to the beta-2 distribution, we provide standard errors for estimated parameters and relevant functions of them, such as inequality and poverty measures, and we provide methodology for all distributions. A test statistic for testing the adequacy of a distribution is proposed. Using eight countries/regions for the year 2005, we show how the methodology can be applied to estimate the parameters of the generalized beta distribution of the second kind (GB2), and its special-case distributions, the beta-2, Singh–Maddala, Dagum, generalized gamma, and lognormal distributions. We test the adequacy of each distribution and compare predicted and actual income shares, where the number of groups used for prediction can differ from the number used in estimation. Estimates and standard errors for inequality and poverty measures are provided. Supplementary materials for this article are available online.     

Efficient Penalized Estimation for Linear Regression Model

This paper develops new penalized estimation for linear regression model. We prove that the new method, which is referred to as efficient penalized estimation, is selection consistent, and more asymptotically efficient than the original one. Besides, we construct a new selector called efficient BIC Selector to tune the regularization parameter in the new estimation, which is shown to be consistent. Our simulation results suggest that the new method may bring significant improvement relative to the original penalized estimation. In addition, we employ a real data set to illustrate the application of the efficient penalized estimation.     

Estimation of Parameters of the Ornstein-Uhlenbeck Type Processes with Continuum of Moment Conditions

In this article, we develop a new method of parametric estimation for process of Ornstein-Uhlenbeck type. The proposed method is based on GMM with a continuum of moment conditions (CGMM), which is introduced in Carrasco and Florens (2002 Carrasco, M., Florens, J.P. (2002). Efficient GMM Estimation Using the Empirical Characteristic Function. Working paper, CREST. Paris. [Google Scholar]), and the estimator is called the CGMM estimator. We show that this CGMM estimator has consistency and asymptotic normality. Simulation studies evidence that the proposed method performs quite well in small-sample cases.     

Estimation Procedure for a Multiple Time Series Model

Given a multiple time series sharing common autoregressive patterns, we estimate an additive model. The autoregressive component and the individual random effects are estimated by integrating maximum likelihood estimation and best linear unbiased predictions in a backfitting algorithm. The simulation study illustrated that the estimation procedure provides an alternative to the Arellano–Bond generalized method of moments (GMM) estimator of the panel model when T > N and the Arellano–Bond generally diverges. The estimator has high predictive ability. In cases where T ≤ N, the backfitting estimator is at least comparable to Arellano–Bond estimator.     

Asymptotic variance approximations for invariant estimators in uncertain asset-pricing models

This article derives explicit expressions for the asymptotic variances of the maximum likelihood and continuously-updated GMM estimators in models that may not satisfy the fundamental asset-pricing restrictions in population. The proposed misspecification-robust variance estimators allow the researcher to conduct valid inference on the model parameters even when the model is rejected by the data. While the results for the maximum likelihood estimator are only applicable to linear asset-pricing models, the asymptotic distribution of the continuously-updated GMM estimator is derived for general, possibly nonlinear, models. The large corrections in the asymptotic variances, that arise from explicitly incorporating model misspecification in the analysis, are illustrated using simulations and an empirical application.     

CBPS-based estimation for linear models with responses missing at random

In this article, based on the covariate balancing propensity score (CBPS), estimators for the regression coefficients and the population mean are obtained, when the responses of linear models are missing at random. It is proved that the proposed estimators are asymptotically normal. In simulation studies and real example, the proposed estimators show improved performance relative to usual augmented inverse probability weighted estimators.     

交通基础设施建设对产业集聚的影响分析——以旅游产业为例

选取我国主要旅游城市2007—2016年的面板数据,以客运成本为纽带建立交通基础设施建设与产业集聚的计量经济模型,以此来检验空间经济学中运输成本和产业集聚之间的变动关系。研究结果表明:交通基础设施建设在产业集聚变动及空间溢出效应中起到了重要的作用,且存在"倒U型"非线性关系;在我国现阶段的交通基础设施建设中,以高铁建设为代表的质量因素对产业集聚的影响更加明显。基于此,建议以交通基础设施建设为着力点,促进我国产业结构均衡发展;加强高速铁路建设,进一步降低我国综合运输成本,减少产业集聚阻力;提升运输服务质量,加快我国交通现代化发展进程,推动产业集聚发展。     

A Monte Carlo comparison of GMM and QMLE estimators for short dynamic panel data models with spatial errors

We suggest a generalized spatial system GMM (SGMM) estimation for short dynamic panel data models with spatial errors and fixed effects when n is large and T is fixed (usually small). Monte Carlo studies are conducted to evaluate the finite sample properties with the quasi-maximum likelihood estimation (QMLE). The results show that, QMLE, with a proper approximation for initial observation, performs better than SGMM in general cases. However, it performs poorly when spatial dependence is large. QMLE and SGMM perform better for different parameters when there is unknown heteroscedasticity in the disturbances and the data are highly persistent. Both estimates are not sensitive to the treatment of initial values. Estimation of the spatial autoregressive parameter is generally biased when either the data are highly persistent or spatial dependence is large. Choices of spatial weights matrices and the sign of spatial dependence do affect the performance of the estimates, especially in the case of the heteroscedastic disturbance. We also give empirical guidelines for the model.     

晨星评级预测性功能研究

采用系统广义矩（System GMM）估计方法对晨星评级的预测性功能进行考察。发现无论是静态模型还是动态模型,估计结果都表明晨星评级对基金的未来业绩不具有预测功能。因此认为晨星评级不能预测基金未来,只能反映基金的过去业绩。