Sieve Estimation of Time-Varying Panel Data Models With Latent Structures

We propose a heterogeneous time-varying panel data model with a latent group structure that allows the coefficients to vary over both individuals and time. We assume that the coefficients change smoothly over time and form different unobserved groups. When treated as smooth functions of time, the individual functional coefficients are heterogeneous across groups but homogeneous within a group. We propose a penalized-sieve-estimation-based classifier-Lasso (C-Lasso) procedure to identify the individuals’ membership and to estimate the group-specific functional coefficients in a single step. The classification exhibits the desirable property of uniform consistency. The C-Lasso estimators and their post-Lasso versions achieve the oracle property so that the group-specific functional coefficients can be estimated as well as if the individuals’ membership were known. Several extensions are discussed. Simulations demonstrate excellent finite sample performance of the approach in both classification and estimation. We apply our method to study the heterogeneous trending behavior of GDP per capita across 91 countries for the period 1960–2012 and find four latent groups.     

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。     

Sample Attrition and Labor Supply Response in Experimental Panel Data

Sample attrition is a potentially serious problem for analysis of panel data, particularly experimental panel data. In this article, a variety of estimation procedures are used to assess the importance of attrition bias in labor supply response to the Seattle and Denver Income Maintenance Experiments (SIME/DIME). Data from Social Security Administration earnings records and the SIME/DIME public use file are used to test various hypotheses concerning attrition bias. The study differs from previous research in that data on both attriters and nonattriters are used to estimate the experimental labor supply response. Although not conclusive, the analysis suggests that attrition bias is probably not a serious enough problem in the SIME/DIME data to warrant extensive correction procedures. The methodology used in this study could be applied to other panel data sets.     

A Joint Test for Conditional Heteroscedasticity in Dynamic Panel Data Models

This article proposes a joint test for conditional heteroscedasticity in dynamic panel data models. The test is constructed by checking the joint significance of estimates of second to pth-order serial correlation in the squares sequence of the first differenced errors. To avoid any distribution assumptions of the errors and the effects, we adopt the GMM estimation for the parameter coefficient and higher order moment estimation for the errors. Based on the estimations, a joint test is constructed for conditional heteroscedasticity in the error. The resulted test is asymptotically chi-squared under the null hypothesis and easy to implement. The small sample properties of the test are investigated by means of Monte Carlo experiments. The evidence shows that the test performs well in dynamic panel data with large number n of individuals and short periods T of time. A real data is analyzed for illustration.     

地方政府支出与农村居民消费需求：基于1998-2007年省级 面板数据的经验分析

 政府支出对居民消费需求产生挤出抑或挤入效应是理论界最近关注的焦点之一。本文在理论诠释地方政府支出对农村居民消费需求的传导机制以及模型建立的理论框架下,通过建立地方政府支出与农村居民消费支出之间的个体固定效应变截距模型,运用我国31个省份1998-2007年的经验数据进行实证检验,结果表明：地方政府财政支农支出对农村居民消费具有显著的挤入效应,而转移性支出与农村居民消费的相关程度并不明显。基于理论分析和实证检验结果,本文最后针对地方政府扩大农村居民消费提出相关建议。     

Modeling Nonignorable Nonresponse in Categorical Panel Data With an Example in Estimating Gross Labor-Force Flows

Many large-scale sample surveys use panel designs under which sampled individuals are interviewed several times before being dropped from the sample. The longitudinal data bases available from such surveys could be used to provide estimates of gross change over time. One problem in using these data to estimate gross change is how to handle the period-to-period nonresponse. This nonresponse is typically nonrandom and, furthermore, may be nonignorable in that it cannot be accounted for by other observed quantities in the data. Under the models proposed in this article, which are appropriate for the analysis of categorical data, the probability of nonresponse may be taken to be a function of the missing variable of interest. The proposed models are fit using maximum likelihood estimation. As an example, the method is applied to the problem of estimating gross flows in labor-force participation using data from the Current Population Survey and the Canadian Labour Force Survey.     

Inference in Approximately Sparse Correlated Random Effects Probit Models With Panel Data

Abstract

We propose a simple procedure based on an existing “debiased” l₁-regularized method for inference of the average partial effects (APEs) in approximately sparse probit and fractional probit models with panel data, where the number of time periods is fixed and small relative to the number of cross-sectional observations. Our method is computationally simple and does not suffer from the incidental parameters problems that come from attempting to estimate as a parameter the unobserved heterogeneity for each cross-sectional unit. Furthermore, it is robust to arbitrary serial dependence in underlying idiosyncratic errors. Our theoretical results illustrate that inference concerning APEs is more challenging than inference about fixed and low-dimensional parameters, as the former concerns deriving the asymptotic normality for sample averages of linear functions of a potentially large set of components in our estimator when a series approximation for the conditional mean of the unobserved heterogeneity is considered. Insights on the applicability and implications of other existing Lasso-based inference procedures for our problem are provided. We apply the debiasing method to estimate the effects of spending on test pass rates. Our results show that spending has a positive and statistically significant average partial effect; moreover, the effect is comparable to found using standard parametric methods.     

Selecting the Correct Number of Factors in Approximate Factor Models: The Large Panel Case With Group Bridge Estimators

This article proposes a group bridge estimator to select the correct number of factors in approximate factor models. It contributes to the literature on shrinkage estimation and factor models by extending the conventional bridge estimator from a single equation to a large panel context. The proposed estimator can consistently estimate the factor loadings of relevant factors and shrink the loadings of irrelevant factors to zero with a probability approaching one. Hence, it provides a consistent estimate for the number of factors. We also propose an algorithm for the new estimator; Monte Carlo experiments show that our algorithm converges reasonably fast and that our estimator has very good performance in small samples. An empirical example is also presented based on a commonly used U.S. macroeconomic dataset.     

Provincial Conditional Income Convergence in China, 1953-1997: A Panel Data Approach

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.     

中国工业经济研发活动及其影响因素——基于2004-2008年面板数据的分析

应用2004-2008年中国大中型工业企业行业面板数据,从投入和产出两个层面探讨中国工业经济研发活动的影响因素,特别地分析了产权结构对创新活动的影响。结果表明：企业规模和研发投入之间存在较为稳定的正相关关系,“倒U型”假说成立;企业规模与研发产出的关系比较模糊,特别是如果使用专利数量衡量创新,企业规模和专利数量之间没有必然的联系,表现在资本密集型行业和劳动密集型行业的差异;国有资产比重增加无论对研发投入还是研发产出都有抑制作用,技术机会系数对于研发活动有显著的正影响。     

中国区域环境库兹涅茨曲线的稳定性检验——基于省际面板数据

在运用F和Hausman统计检验识别环境库兹涅茨面板模型形式的基础上,选择合理的面板模型估计、检验中国30个省份环境库兹涅茨曲线的稳定性.研究结果表明:环境库兹涅茨曲线的稳定性与经济发展水平较高的地区相关性较强,与经济发展水平较低的地区相关性较弱.工业粉尘、烟尘、二氧化硫排放量估计的环境库兹涅茨曲线稳定性相对较好,工业废水、固体废物排放量估计的环境库兹涅茨曲线稳定性相对较差.在环境保护政策的约束下,除北京、上海以外,其他地区环境库兹涅茨曲线的拐点时间具有刚性特征.     

外资进入与国内工资差异：基于工业行业面板数据的联立估计

 本文通过内生化熟练劳动力的相对工资与相对供给,建立两方程联立估计模型,利用1999-2006年我国36个工业行业数据实证考察了外资对我国工资不平等的影响。以科技人员相对工资作为熟练劳动力的代理指标,研究结果表明,外资进入一方面会通过技术外溢渠道促进我国内资企业偏向熟练劳动力的技术进步、从而扩大我国的工资不平等,另一方面其较高的劳动报酬会吸引更多的熟练劳动力流向外资企业、从而减少我国内资企业熟练劳动力的相对供给,进而进一步扩大我国的工资不平等。文章最后对估计结果的稳健性进行了较为细致的检验,检验结果表明本文的主要估计结果是稳健的。     

Testing for a Change of the Innovation Distribution in Nonparametric Autoregression: The Sequential Empirical Process Approach

We consider a nonparametric autoregression model under conditional heteroscedasticity with the aim to test whether the innovation distribution changes in time. To this end, we develop an asymptotic expansion for the sequential empirical process of nonparametrically estimated innovations (residuals). We suggest a Kolmogorov–Smirnov statistic based on the difference of the estimated innovation distributions built from the first ?ns?and the last n ? ?ns? residuals, respectively (0 ≤ s ≤ 1). Weak convergence of the underlying stochastic process to a Gaussian process is proved under the null hypothesis of no change point. The result implies that the test is asymptotically distribution‐free. Consistency against fixed alternatives is shown. The small sample performance of the proposed test is investigated in a simulation study and the test is applied to a data example.     

中国生猪生产布局影响因素实证研究——基于省级面板数据

合理的生猪生产布局对中国生猪产业可持续发展具有十分重要的意义,影响生猪生产区域变动的因素不能仅停留在感性认识上。基于Nerlove模型及经济学相关理论,结合各省级单位的面板数据建立动态自回归模型,对中国生猪生产布局变动的规律、特点、趋势及其效果作出系统的测度和分析。研究表明：中国生猪生产区域布局经历了五次比较大的变动,而粮食产量、原有饲养规模、消费市场的潜力、交通运输的通达性、气候条件、畜牧业与种植业的比较优势等都是影响生猪生产区域格局的因素;从影响程度看,原有饲养规模和消费市场购买力水平影响最为突出,地区粮食产量对生猪生产布局影响不显著。此结论对优化中国生猪生产布局具有指导意义。     

基于面板数据模型的生态足迹与经济增长的关系研究——以中部六省为例

生态足迹与经济增长之间的演化关系分析是度量区域经济可持续发展能力的重要方法,因此运用生态足迹模型对中部六省1989—2011年的生态足迹进行了测算,并运用面板数据模型深入分析并比较了中部六省生态足迹与经济增长之间的关系。研究结果表明:中部六省的生态足迹较高,1989—2011年中部六省全部表现为生态赤字,并且生态赤字的数值不断增大;中部六省的生态环境利用效率较低,属于粗放型经济增长方式;中部六省的生态足迹与经济增长的关系存在较大差异,不同省份的生态足迹对经济增长的影响系数差异较大,并且对于不同的生态生产性土地,不同省份的利用效率也有很大不同。     

家庭人口结构变动对居民消费的影响研究——基于微观家庭面板数据的分析

居民消费与家庭人口结构密切相关,本文基于2018年和2019年国家统计局住户调查数据,构建基于微观家庭的平衡面板数据随机效应和固定效应模型,结合我国人口未来变动趋势,从家庭人口年龄结构、城镇化属性、受教育水平三个维度着手,就家庭人口结构变动对家庭平均消费率和消费收入弹性的影响进行实证分析。结果表明,城镇化率提高、城镇化发展质量提升以及居民受教育水平提高有助于提高家庭平均消费率和消费收入弹性,人口老龄化对家庭平均消费率具有负面效应,更为积极的生育政策能够促进居民消费。本文建议持续推进以人为核心的新型城镇化,坚持教育优先发展,持续优化生育政策,积极应对人口老龄化,挖掘老年人口消费潜力,推动建设高水平国内消费市场。     

政府消费与经济增长： 基于1985－2007年中国省际面板数据的实证分析

 在理论分析基础上,基于中国31个省(市、区)1985-2007年地区生产总值、政府消费、固定资产投资总额和年末在岗职工人数数据,运用面板数据的变截据和变系数相关模型对政府消费的增长效应进行计量检验。检验结果表明：在全国层面上政府消费的系数为4.656;在省(市、区)层面上,各地方政府消费的系数均大于零,但区域间存在显著差异。财政支出方式应逐步实现从政府投资向政府消费转变,并调整政府消费的内部结构、城乡结构和地区结构。     

中国农业对外直接投资是否存在生产率悖论——基于2005—2014年省级面板数据的实证分析

基于2005—2014年中国省级层面农业对外直接投资的面板数据,分别运用普通最小二乘法、弱工具变量更不敏感的有限信息最大释然法(LIML)及差分矩估计方法(GMM),对影响农业对外直接投资的因素进行实证研究,结果表明:生产率是农业对外直接投资的关键影响要素,这一结论与当前的异质性贸易理论关于生产率是企业开展对外直接投资的决定因素的结论是一致的,中国农业领域的对外合作不存在生产率悖论;农业对外直接投资尚未产生滞后影响,前期的对外直接投资对当期的对外直接投资没有产生带动作用;产业规模优势、政府的参与程度和政策支持是影响农业企业开展对外直接投资的重要因素,企业产业集聚对农业企业对外直接投资没有产生积极作用。据此,从企业层面和政府视角提出了具体应对政策与建议。     

Decomposition of Seasonality and Long‐term Trend in Seismological Data: A Bayesian Modelling of Earthquake Detection Capability

This study demonstrates the decomposition of seasonality and long‐term trend in seismological data observed at irregular time intervals. The decomposition was applied to the estimation of earthquake detection capability using cubic B‐splines and a Bayesian approach, which is similar to the seasonal adjustment model frequently used to analyse economic time‐series data. We employed numerical simulation to verify the method and then applied it to real earthquake datasets obtained in and around the northern Honshu island, Japan. With this approach, we obtained the seasonality of the detection capability related to the annual variation of wind speed and the long‐term trend corresponding to the recent improvement of the seismic network in the studied region.     

我国服务业发展与经济增长的因果关系研究——基于东、中、西部面板数据的实证研究

 摘　　要:本文运用面板单位根检验、协整检验以及误差修正模型等现代计量经济学方法，对我国东部地区、中部地区和西部地区的服务业与经济增长的关系进行实证研究 。