Panel data models with cross-sectional dependence: a selective review

In this review, we highlight some recent methodological and theoretical develop- ments in estimation and testing of large panel data models with cross-sectional dependence. The paper begins with a discussion of issues of cross-sectional dependence, and introduces the concepts of weak and strong cross-sectional dependence. Then, the main attention is primarily paid to spatial and factor approaches for modeling cross-sectional dependence for both linear and nonlinear （nonparametric and semiparametric） panel data models. Finally, we conclude with some speculations on future research directions.

Small Sample Estimation in Dynamic Panel Data Models: A Simulation Study

We used simulated data to investigate both the small and large sample properties of the within-groups (WG) estimator and the first difference generalized method of moments (FD-GMM) estimator of a dynamic panel data (DPD) model. The magnitude of WG and FD-GMM estimates are almost the same for square panels. WG estimator performs best for long panels such as those with time dimension as large as 50. The advantage of FD-GMM estimator however, is observed on panels that are long and wide, say with time dimension at least 25 and cross-section dimension size of at least 30. For small-sized panels, the two methods failed since their optimality was established in the context of asymptotic theory. We developed parametric bootstrap versions of WG and FD-GMM estimators. Simulation study indicates the advantages of the bootstrap methods under small sample cases on the assumption that variances of the individual effects and the disturbances are of similar magnitude. The boostrapped WG and FD-GMM estimators are optimal for small samples.

Comparison of Uniform and Kernel Gaussian Weight Matrix in Generalized Spatial Panel Data Model

Panel data combine cross-section data and time series data. If the cross-section is locations, there is a need to check the correlation among locations. ρ and λ are parameters in generalized spatial model to cover effect of correlation between locations. Value of ρ or λ will influence the goodness of fit model, so it is important to make parameter estimation. The effect of another location is covered by making contiguity matrix until it gets spatial weighted matrix (W). There are some types of W—uniform W, binary W, kernel Gaussian W and some W from real case of economics condition or transportation condition from locations. This study is aimed to compare uniform W and kernel Gaussian W in spatial panel data model using RMSE value. The result of analysis showed that uniform weight had RMSE value less than kernel Gaussian model. Uniform W had stabil value for all the combinations.

Some Additional Moment Conditions for a Dynamic Count Panel Data Model with Predetermined Explanatory Variables

This paper proposes some additional moment conditions for the linear feedback model with explanatory variables being predetermined, which is proposed by [1] for the purpose of dealing with count panel data. The newly proposed moment conditions include those associated with the equidispersion, the Negbin I-type model and the stationarity. The GMM estimators are constructed incorporating the additional moment conditions. Some Monte Carlo experiments indicate that the GMM estimators incorporating the additional moment conditions perform well, compared to that using only the conventional moment conditions proposed by [2,3].

Semi-Varying Coefficient Panel Data Model with Technical Indicators Predicts Stock Returns in Financial Market

Accurately predicting stock returns is a conundrum in financial market.Solving this conundrum can bring huge economic benefits for investors and also attract the attention of all circles of people.In this paper the authors combine semi-varying coefficient model with technical analysis and statistical learning,and propose semi-varying coefficient panel data model with individual effects to explore the dynamic relations between the stock returns from five companies:CVX,DFS,EMN,LYB,and MET and five technical indicators:CCI,EMV,MOM,ln ATR,ln RSI as well as closing price(ln CP),combine semi-parametric fixed effects estimator,semi-parametric random effects estimator with the testing procedure to distinguish fixed effects(FE) from random effects(RE),and finally apply the estimated dynamic relations and the testing set to predict stock returns in December 2020 for the five companies.The proposed method can accommodate the varying relationship and the interactive relationship between the different technical indicators,and further enhance the prediction accuracy to stock returns.

A Fluctuation Test for Structural Change Detection in Heterogeneous Panel Data Models

Structural change in panel data is a widespread phenomena. This paper proposes a fluctuation test to detect a structural change at an unknown date in heterogeneous panel data models with or without common correlated effects. The asymptotic properties of the fluctuation statistics in two cases are developed under the null and local alternative hypothesis. Furthermore, the consistency of the change point estimator is proven. Monte Carlo simulation shows that the fluctuation test can control the probability of type I error in most cases, and the empirical power is high in case of small and moderate sample sizes. An application of the procedure to a real data is presented.

A panel data model to predict airline passenger volume

Airline passenger volume is an important reference for the implementation of aviation capacity and route adjustment plans.This paper explores the determinants of airline passenger volume and proposes a comprehensive panel data model for predicting volume.First,potential factors influencing airline passenger volume are analyzed from Geo-economic and service-related aspects.Second,the principal component analysis(PCA)is applied to identify key factors that impact the airline passenger volume of city pairs.Then the panel data model is estimated using 120 sets of data,which are a collection of observations for multiple subjects at multiple instances.Finally,the airline data from Chongqing to Shanghai,from 2003 to 2012,was used as a test case to verify the validity of the prediction model.Results show that railway and highway transportation assumed a certain proportion of passenger volumes,and total retail sales of consumer goods in the departure and arrival cities are significantly associated with airline passenger volume.According to the validity test results,the prediction accuracies of the model for 10 sets of data are all greater than 90%.The model performs better than a multivariate regression model,thus assisting airport operators decide which routes to adjust and which new routes to introduce.

Health diagnosis of ultrahigh arch dam performance using heterogeneous spatial panel vector model

Currently,more than ten ultrahigh arch dams have been constructed or are being constructed in China.Safety control is essential to long-term operation of these dams.This study employed the flexibility coefficient and plastic complementary energy norm to assess the structural safety of arch dams.A comprehensive analysis was conducted,focusing on differences among conventional methods in characterizing the structural behavior of the Xiaowan arch dam in China.Subsequently,the spatiotemporal characteristics of the measured performance of the Xiaowan dam were explored,including periodicity,convergence,and time-effect characteristics.These findings revealed the governing mechanism of main factors.Furthermore,a heterogeneous spatial panel vector model was developed,considering both common factors and specific factors affecting the safety and performance of arch dams.This model aims to comprehensively illustrate spatial heterogeneity between the entire structure and local regions,introducing a specific effect quantity to characterize local deformation differences.Ultimately,the proposed model was applied to the Xiaowan arch dam,accurately quantifying the spatiotemporal heterogeneity of dam performance.Additionally,the spatiotemporal distri-bution characteristics of environmental load effects on different parts of the dam were reasonably interpreted.Validation of the model prediction enhances its credibility,leading to the formulation of health diagnosis criteria for future long-term operation of the Xiaowan dam.The findings not only enhance the predictive ability and timely control of ultrahigh arch dams'performance but also provide a crucial basis for assessing the effectiveness of engineering treatment measures.

Ecological Footprint and Endogenous Economic Growth in the Poyang Lake Area in China Based on Empirical Analysis of Panel Data Model

In this paper, ecological footprint methods were used to calculate the ecological footprint of six cities （Nanchang, Jingdezhen, Jiujiang, Xinyu, Yingtan and Fuzhou） in the Poyang Lake Area, Jiangxi, China from 1991 to 2010. Ecological footprint was the input factor for ecological resources and the contribution of this and other factors such as labor and capital to economic growth were analyzed. The results showed that, from 1991 to 2010, ecological footprints in the six cities increased year by year. The amount of land for fossil energy, under cultivation and grassland influenced total ecological footprint in each city. The contribution of ecological resources, labor factors and capital factors to economic growth showed regional differences. Nanchang, Jiujiang, Xinyu, and Yingtan are capital-orientated and capital factor had a great influence on the economic growth rates, whereas, Jingdezhen and Fuzhou were labor-orientated. The contribution of ecological resources to economic growth in the six cities was the lowest of all three factors, meaning that efficiency of ecological resource utilization is low. Total productivity plays a key role in economic development; however, the overall level of total factor productivity for the six cities was low and indicates that the technological content of Poyang Lake Area’s economic growth is low and the utilization of input factors extensive. In summary, we suggest changing the mode of economic growth and developing tertiary industry in the region.

An analysis of Chinese urban residents＇ consumer demand employing a dynamic semi-parametric panel data model

Using data for China for the years 1991 to 2005 by province and employing the semi- parametric panel data model estimation method developed by Horowitz （2004） and Henderson et al. （2006） and Hubler＇s non-parametric generalized method of moments （GMM） estimation （2005）, this article constructs a dynamic semi-parametric panel data model and describes the dynamic changing trajectory of the effect on consumption of income disparity among urban residents. Our findings show that there is a significant ＂ratchet effect＂ in the consumption of urban residents; that income disparity among urban residents has a clear negative influence on consumption; and that the trajectory of this influence shows a roughly bimodal curve.

Bootstrap LM Tests for Spatial Dependence in Panel Data Models with Fixed Effects

This paper applies bootstrap methods to LM tests(including LM-lag test and LM-error test) for spatial dependence in panel data models with fixed effects, and removes fixed effects based on orthogonal transformation method proposed by Lee and Yu(2010). The consistencies of LM tests and their bootstrap versions are proved, and then some asymptotic refinements of bootstrap LM tests are obtained. It shows that the convergence rate of bootstrap LM tests is O((N T)-2) and that of fast double bootstrap LM tests is O((NT)-5/2). Extensive Monte Carlo experiments suggest that,compared to aysmptotic LM tests, the size of bootstrap LM tests gets closer to the nominal level of signifiance, and the power of bootstrap LM tests is higher, especially in the cases with small spatial correlation. Moreover, when the error is not normal or with heteroskedastic, asymptotic LM tests suffer from severe size distortion, but the size of bootstrap LM tests is close to the nominal significance level.Bootstrap LM tests are superior to aysmptotic LM tests in terms of size and power.

教育支出的产出效应研究——基于空间Panel Data与菲德模型的数量分析

考虑到我国省际经济增长的空间溢出性和空间相关性,将空间Panel Data模型和菲德模型结合起来研究教育支出的产出效应,这种方法能在考虑空间相关性的基础上研究教育支出的部门溢出,从而对教育支出的产出效应得到更准确的估计。研究结果显示省际经济增长之间存在显著的空间相关性,而空间Panel Data模型的实证结果也表明,如果不考虑空间相关性会夸大教育支出的部门溢出。

基于Panel Data模型的中国土地市场发育区域差异及其对房价的影响

研究目的:分析中国土地市场发育的区域差异,以及这种差异对房价的影响。研究方法:面板数据模型。研究结果:(1)中国土地市场发育水平存在区域差异,区域内部的发育水平也不尽相同,东、中、西部的差异呈现先缩小后加大的趋势;(2)土地市场发育对房价的影响同样存在区域差异,东部、中部地区具有负向影响,且东部较中部显著,西部地区具有正向影响。研究结论:(1)提高土地市场发育水平可以促使房价达到合理水平;(2)在提高土地市场化水平的基础上不断完善市场经济环境,有利于这种作用的实现。

基于Panel-data的区际产业转移粘性分析

利用面板数据模型,以江苏省为例,对28个制造业的科技进步、产业集群、区域人力资本积累、沉没成本和资产专用性、劳动力跨区域流动、制度创新、政府阻力等因素对产业转移粘性的关系进行了实证研究,并依据各个因素的贡献率得出了跨区域产业转移的初步结论。

房价影响因素的空间非一致性与差异化调控手段——基于Panel Data模型的实证研究

房地产价格受多种宏观经济因素的综合影响,不同城市的房价决定因素可能存在差异。文章将全国城市划分为四种级别,并选择17个一、二、三线样本城市,以货币供应量、CPI、GDP、城镇居民家庭人均可支配收入和社会固定资产投资额为解释变量,选取2002-2012年的季度数据,构建Panel Data模型,研究房价影响因素的空间非一致性,研究结果证明了空间非一致性的存在。基于此,对一、二、三线城市分别提出了差异性调控手段建议。

基于PanelData的金砖国家对美高技术产品出口竞争力及影响因素分析

以巴西、俄罗斯、印度、中国、南非组成的金砖国家(BRICS),作为新兴经济代表,纷纷重视高技术产品的出口竞争。2010年金砖国家高技术产品出口总值达5008亿美元,占世界高技术产品出口的22.76%,而金砖国家在美国市场高技术产品的出口竞争成为新的焦点。本文旨在比较金砖国家对美高技术产品出口竞争力影响因素,挖掘金砖国家高技术产品出口的竞争优势,并借鉴金砖国家的成功经验,为中国提出对策建议,以增强中国对美高技术产品出口竞争力。

基于Panel Data模型的新疆区域产业结构调整对环境污染的影响分析

文章选取2000年以来新疆区域经济与环境数据,建立产业结构调整与环境污染指标的计量模型,重点估算出产业结构调整对环境污染的区域特征。实证结果发现:第一,在工业废水排放量方面,固定效应系数最高为克拉玛依市,最低为和田地区。第二,在工业废气排放量方面,固定效应系数最高为克拉玛依市,数值最低的为克州。第三,新疆产业结构调整对工业固体废物排放量的固定效应系数最高的是乌鲁木齐市,数值最低的是塔城地区;第四,就新疆三大区域而言,北疆尤其天山北坡经济带的固定效应系数普遍高于东疆和南疆地区,区域分布差异非常显著。从实证分析结果来看,推动经济社会与资源环境协调发展的基础是抓好重点区域和领域的环保工作,要加强污染防治,积极推行排污许可和总量控制制度,落实重点流域、区域、城市、产业污染治理任务。

基于Panel Data模型的土地供应量对房价的影响研究

研究目的:定量反映土地供应量对房价的影响及其作用机制。研究方法:计量经济学方法。研究结果:一年前的土地供应量对房屋供应量的影响是正的,对房价的影响是负的,两者在统计上都是显著的,但土地供应量增加对降低房价的影响力非常小。两年前的土地供应量虽然影响房屋供应量,但却不影响房价。研究结论:影响中国当前房价的因素非常复杂,增减土地供应量对调控房价有一定作用,但必须同时有其他工具的辅助,否则其效果很可能不够显著。

对外开放、金融发展与海洋产业集聚——基于Panel Data模型的省际差异分析

在着力发展海洋产业的时代背景下,梳理出对外开放、金融发展与海洋产业集聚之间的作用机理,基于1997—2012年中国沿海11个省份的面板数据,运用Panel Data模型,实证了对外开放条件下,我国金融发展对海洋产业集聚作用的省际差异性。检验结果显示,金融发展水平与海洋产业集聚程度之间并非正相关,对外开放作为至关重要的条件因素,直接制约着金融要素作用的发挥。说明各省必须依照实际情况,三者协调发展,才能有效促进海洋产业的集聚。

基于Panel Data模型的天津郊区城镇化对农民收入的影响研究

通过Panel Data模型,分析了天津郊区城镇化水平对农民收入的影响,并比较了郊区地区因素和时间因素对农民收入的影响。研究发现,天津郊区城镇化发展与农民收入水平之间存在长期均衡关系,但影响较小;地区和时间差异因素发挥了较为显著的作用。