面板数据模型的应用现状与发展趋势

面板数据模型的应用研究是当前学术界的一个热点.文章针对国内学术界的研究现状,讨论了当前面板数据模型应用中的问题,指出了面板数据模型的改进取向.     

农村经济增长的面板数据模型分析

文章采用1992年～2005年期间的面板数据(Panel Data),利用变截距固定效应模型,分析该时间区间内影响辽宁省农村经济增长的因素以及各因素对各个县区农村经济增长的影响.分析结果表明,人力资本、农民纯收入、产业结构、城乡结构和财政投入均会对地区农村经济增长产生影响.     

固定效应部分线性变系数面板模型的快速有效估计

本文将最小二乘支持向量机(LSSVM) 和二次推断函数法(QIF) 相结合，为个体内具有相关结构的固定效应部分线性变系数面板模型提供了一种新的快速估计方法；在一定的正则条件下，论证了参数估计量的渐近正态性和非参数估计量的收敛速度；采用Monte Carlo模拟考察了估计方法在有限样本下的表现并将估计技术应用于现实数据分析。该方法不仅保证了估计的有效性和统计推断力，而且程序运行速度得到较大幅度提升。     

固定效应部分线性单指数面板模型的惩罚分位数回归

分位数回归是均值回归的有益补充,该方法毋须对分布函数的具体形式做出假设,且对具有异常值或厚尾分布的数据仍具有稳健性.当前,对部分线性单指数面板模型估计方法的研究主要集中于均值回归,基于此,本文考虑了固定效应部分线性单指数面板分位数回归模型,结合B-样条函数、SCAD惩罚函数和迭代加权最小二乘法,构建了模型的估计方法,证明了估计方法的一致性和渐近正态性,同时利用Monte Carlo模拟评价了所述方法在有限样本下的表现.最后,将估计方法应用于分析碳排放的影响因素.     

面板数据模型的设定、统计检验和新进展

在介绍面板数据及其优势与局限的基础上,首先,从异质性、时变性和相关性的观点对静态面板数据计量模型的设定、动态面板数据模型的估计方法和Granger因果检验进行系统的讨论。其次,按照假设检验的零假设进行分类,系统阐述面板单位根检验和协整检验理论。最后,介绍面板数据计量经济学的一些新进展。     

空间面板数据模型设定问题分析

空间面板数据模型将空间计量经济学和面板数据方法相结合,不仅同时考虑时空特征,而且将空间效应纳入研究体系,成为当前计量经济学的热点研究领域,但其模型设定、参数估计及模型检验也更为复杂,实证研究中往往出现模型设定偏误等问题。因此,基于空间面板数据模型的前沿理论,重点探讨模型设定中的常见问题,包括空间滞后模型与空间误差模型的选择、随机效应与固定效应的选择以及模型拟合优度的选择与比较,为模型的应用和新模型的扩展提供理论依据和参考。     

带线性误差的部分线性EV模型的经验似然推断

部分线性EV模型是经典的部分线性模型的推广,在此模型中,假定误差是线性过程.文章把经验似然方法推广到带线性误差的部分线性EV模型,提出了调整的经验对数似然比,并建立了非参数的Wilks定理.     

缺失数据下广义线性模型的经验似然推断

利用经验似然方法,讨论缺失数据下广义线性模型中参数的置信域问题,得到了对数经验似然比统计量的渐近分布为标准卡方分布;给出参数的一些估计量及其渐近分布,利用数据模拟解释了所提出的方法。     

面板数据的模型建立和检验分析

面板数据分析是数量经济学中一门重要的分支,在经济领域中有着广泛的应用。文章详细地介绍了面板数据的主要类型及模型的建立和检验。     

截面相关面板数据的部分异质结构的识别

在大维面板数据中,截面之间很可能呈现出部分异质的特征,即参数在截面间具有组群效应,同组参数相同而不同组参数相异。如果忽略部分异质性而采用完全异质或同质的方法,可能导致估计的不一致性以及统计推断无效性。鉴于已有的部分异质性的研究要么限定截面独立,要么局限于强因子情形,本文尝试在Reese和Westerlund（2015）[1]提出的允许强因子或非强因子存在的较一般的框架下探讨面板数据部分异质结构的识别问题。采用Pesaran（2006）[2] CCE （Common Correlated Effects）方法处理不同强弱的共同因子,并借鉴Su et al.,（2016）[3]的C-Lasso （Classifier- Least Absolute Shrinkage and Selection Operator）方法,对CCE变化后的方程构造带有加法-乘法惩罚项的惩罚最小二乘,优化后以同步地实现分组和参数的估计。理论分析表明,在强因子或半强因子情形中,本文所提方法在分组方面具有渐近一致性,即所有个体被正确分组的概率随着 而趋于1。同时,参数的Lasso估计和事后Lasso估计均具有渐近正态性。另外分析结果也表明,因子的强弱不会影响分组的一致性但会影响以上两种估计量的渐近正态性,因子越强,两种估计量收敛得越快。模拟结果则表明有限样本下,本文所提的方法在分组、参数估计和分组数确定方面均具有良好的表现。具体的,在强因子和不同的半强因子情形中,随着N,T的增加,分组和分组数正确率很快地上升到100%,而两种参数估计的均方根误差和偏差则明显地降低。最后,利用本文所提的方法,研究了人力资本对经济增长影响的部分异质性。     

Statistical Inference for Single‐index Panel Data Models

We study estimation and hypothesis testing in single‐index panel data models with individual effects. Through regressing the individual effects on the covariates linearly, we convert the estimation problem in single‐index panel data models to that in partially linear single‐index models. The conversion is valid regardless of the individual effects being random or fixed. We propose an estimating equation approach, which has a desirable double robustness property. We show that our method is applicable in single‐index panel data models with heterogeneous link functions. We further design a chi‐squared test to evaluate whether the individual effects are random or fixed. We conduct simulations to demonstrate the finite sample performance of the method and conduct a data analysis to illustrate its usefulness.     

Inference of the Trend in a Partially Linear Model with Locally Stationary Regressors

In this article, we construct the uniform confidence band (UCB) of nonparametric trend in a partially linear model with locally stationary regressors. A two-stage semiparametric regression is employed to estimate the trend function. Based on this estimate, we develop an invariance principle to construct the UCB of the trend function. The proposed methodology is used to estimate the Non-Accelerating Inflation Rate of Unemployment (NAIRU) in the Phillips Curve and to perform inference of the parameter based on its UCB. The empirical results strongly suggest that the U.S. NAIRU is time-varying.     

Inference for Local Autocorrelations in Locally Stationary Models

For nonstationary processes, the time-varying correlation structure provides useful insights into the underlying model dynamics. We study estimation and inferences for local autocorrelation process in locally stationary time series. Our constructed simultaneous confidence band can be used to address important hypothesis testing problems, such as whether the local autocorrelation process is indeed time-varying and whether the local autocorrelation is zero. In particular, our result provides an important generalization of the R function acf() to locally stationary Gaussian processes. Simulation studies and two empirical applications are developed. For the global temperature series, we find that the local autocorrelations are time-varying and have a “V” shape during 1910–1960. For the S&P 500 index, we conclude that the returns satisfy the efficient-market hypothesis whereas the magnitudes of returns show significant local autocorrelations.     

Empirical Likelihood Inferences for Semiparametric Varying-Coefficient Partially Linear Models with Longitudinal Data

In this article, empirical likelihood inferences for semiparametric varying-coefficient partially linear models with longitudinal data are investigated. We propose a groupwise empirical likelihood procedure to handle the inter-series dependence of the longitudinal data. By using residual-adjustment, an empirical likelihood ratio function for the nonparametric component is constructed, and a nonparametric version Wilks' phenomenons is proved. Compared with methods based on normal approximations, the empirical likelihood does not require consistent estimators for the asymptotic variance and bias. A simulation study is undertaken to assess the finite sample performance of the proposed confidence regions.     

Statistical Inference in Partially Linear Varying-Coefficient Models with Missing Responses at Random

This article considers statistical inference for partially linear varying-coefficient models when the responses are missing at random. We propose a profile least-squares estimator for the parametric component with complete-case data and show that the resulting estimator is asymptotically normal. To avoid to estimate the asymptotic covariance in establishing confidence region of the parametric component with the normal-approximation method, we define an empirical likelihood based statistic and show that its limiting distribution is chi-squared distribution. Then, the confidence regions of the parametric component with asymptotically correct coverage probabilities can be constructed by the result. To check the validity of the linear constraints on the parametric component, we construct a modified generalized likelihood ratio test statistic and demonstrate that it follows asymptotically chi-squared distribution under the null hypothesis. Then, we extend the generalized likelihood ratio technique to the context of missing data. Finally, some simulations are conducted to illustrate the proposed methods.     

Series Estimation in Partially Linear In‐Slide Regression Models

Abstract. The partially linear in‐slide model (PLIM) is a useful tool to make econometric analyses and to normalize microarray data. In this article, by using series approximations and a least squares procedure, we propose a semiparametric least squares estimator (SLSE) for the parametric component and a series estimator for the non‐parametric component. Under weaker conditions than those imposed in the literature, we show that the SLSE is asymptotically normal and that the series estimator attains the optimal convergence rate of non‐parametric regression. We also investigate the estimating problem of the error variance. In addition, we propose a wild block bootstrap‐based test for the form of the non‐parametric component. Some simulation studies are conducted to illustrate the finite sample performance of the proposed procedure. An example of application on a set of economical data is also illustrated.     

Statistical Inference on Partially Linear Additive Models with Missing Response Variables and Error-prone Covariates

This paper considers statistical inference for the partially linear additive models, which are useful extensions of additive models and partially linear models. We focus on the case where some covariates are measured with additive errors, and the response variable is sometimes missing. We propose a profile least-squares estimator for the parametric component and show that the resulting estimator is asymptotically normal. To construct a confidence region for the parametric component, we also propose an empirical-likelihood-based statistic, which is shown to have a chi-squared distribution asymptotically. Furthermore, a simulation study is conducted to illustrate the performance of the proposed methods.     

Local Influence Analysis for Penalized Gaussian Likelihood Estimators in Partially Linear Models

Abstract. Partially linear models are extensions of linear models to include a non-parametric function of some covariate. They have been found to be useful in both cross-sectional and longitudinal studies. This paper provides a convenient means to extend Cook's local influence analysis to the penalized Gaussian likelihood estimator that uses a smoothing spline as a solution to its non-parametric component. Insight is also provided into the interplay of the influence or leverage measures between the linear and the non-parametric components in the model. The diagnostics are applied to a mouthwash data set and a longitudinal hormone study with informative results.     

Efficient Estimation in Marginal Partially Linear Models for Longitudinal/Clustered Data Using Splines

Abstract.  We consider marginal semiparametric partially linear models for longitudinal/clustered data and propose an estimation procedure based on a spline approximation of the non-parametric part of the model and an extension of the parametric marginal generalized estimating equations (GEE). Our estimates of both parametric part and non-parametric part of the model have properties parallel to those of parametric GEE, that is, the estimates are efficient if the covariance structure is correctly specified and they are still consistent and asymptotically normal even if the covariance structure is misspecified. By showing that our estimate achieves the semiparametric information bound, we actually establish the efficiency of estimating the parametric part of the model in a stronger sense than what is typically considered for GEE. The semiparametric efficiency of our estimate is obtained by assuming only conditional moment restrictions instead of the strict multivariate Gaussian error assumption.     

Asymptotic Normality for the Partially Linear EV Models with Longitudinal Data

In this article, we consider a partially linear EV regression model under longitudinal data. By using a weighted kernel method and modified least-squared method, the estimators of unknown parameter, the unknown function are constructed and the asymptotic normality of the estimators are derived. Simulation studies are conducted to illustrate the finite-sample performance of the proposed method.