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.

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.

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.

Tea Production Response to Seasonal Rainfall Variability: Evidence from Rwanda

Tea is a very important cash crop in Rwanda, as it provides crucial income and employment for farmers in poor rural areas. From 2017 to 2020, this study was intended to determine the impact of seasonal rainfall on tea output in Rwanda while still considering temperature, plot size (land), and fertiliser for tea plantations in three of Rwanda’s western, southern, and northern provinces, western province with “Gisovu” and “Nyabihu”, southern with “Kitabi”, and northern with “Mulindi” tea company. The study tested the level of statistical significance of all considered variables in different formulation of panel data models to assess individual behaviour of independent variables that would affect tea production. According to this study, a positive change in rainfall of 1 mm will increase tea production by 0.215 percentage points of tons of fresh leaves. Rainfall is a statistically significant variable among all variables with a positive impact on tea output Qitin Rwanda’s Western, Southern, and Northern provinces. Rainfall availability favourably affects tea output and supports our claim. Therefore, there is a need for collaboration efforts towards developing sustainable adaptation and mitigation options against climate change, targeting tea farming and the government to ensure that tea policy reforms are targeted towards raising the competitiveness of Rwandan tea at local and global market.

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.

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.

Factors Driving the Expansion of Construction Land:A Panel Data Study of Districts and Counties in Ningbo City,China

This paper analyzes panel data from 2003–2012 to identify the factors driving the expansion of construction land in Ningbo city;it uses panel data,regional-level,and year-by-year regression models.The results indicate the following：（1） For each 1% increase in the size of the economy,urban population,and industrial structure adjustment coefficient,the amount of construction land increased by 0.35%,0.52% and –1%,respectively.（2） The factors driving the expansion of urban construction land differed across regions.In more highly developed areas such as Yuyao,Cixi,Fenghua and the downtown area,population growth was the most obvious driving factor with coefficients of 4.880,1.383,3.036 and 0.583,respectively,in those areas.Here,the impact of industrial structure adjustment was lower than that of population growth（with coefficients of 1.235,0.307,0.145 and –0.242）,while economic development was an increasingly insignificant factor（with coefficients of –0.302,0.071,0.037 and 0.297）.On the other hand,economic development was the most important factor for the expansion of construction land in relatively less developed areas such as Xiangshan and Ninghai counties with coefficients of 0.413 and 0.195,respectively.Here,population growth（with coefficients of –0.538 and 0.132） and industrial structure adjustment（with coefficients of –0.097 and 0.067） were comparatively weaker driving factors.（3） The results of the year-by-year regression indicate the increased impact of economic development as a driving factor（from –1.531 in 2005 to 1.459 in 2012）.The influence of the population growth factor slowly declined（from 1.249 in 2005 to 0.044 in 2012） and from 2009 on was less influential than the economic development factor.The industrial structure coefficient remained negative and its influence diminished from year to year（from –5.312 in 2004 to –0.589 in 2012）.

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.

ESTIMATION IN A SEMI-VARYING COEFFICIENT MODEL FOR PANEL DATA WITH FIXED EFFECTS

This paper considers a semi-varying coefficient model for panel data with fixed effects,proposes the profile-likelihood-based estimators for the parametric and nonparametric components,and establishes convergence rates and asymptotic normality properties for both estimators.Simulation results show that the proposed estimators behave well in finite sample cases.

Research on the Impact of Industrial Agglomeration and Structural Upgrade on County Economic Growth under the New Normal Condition-Based on the Empirical Study of 101 County Counties in Jiangsu and Zhejiang Provinces

In this paper, the method of generalized moments of data and systems for 101 counties in Jiangsu and Zhejiang Provinces is used to analyze the impact of industrial agglomeration and industrial structure upgrading on county economic growth. The real diagnosis result indicated that, the industry gathers with the industrial structure promotes has the forward function to the county territory economic growth rate, in the sample time that had not discovered the industry gathers to the economical growth existence non-linearity in？ uence. On the other hand, the industrial structure promotes to the county territory economy long-term growth power is stronger, later period should break through the present stage in the manufacturing industry mature county territory through the industrial structure promotion strategy the industry to glide down.

Decomposing the drivers of residential space cooling energy consumption in EU-28 countries using a panel data approach

While space cooling currently represents less than 1%of final energy use in the residential sector of the Euro-pean Union(EU-28),it was the fastest growing end-use during the 2000-15 period with a mean annual growth rate of 6%per year.Currently,little is known about factors which have driven regional air-conditioning(AC)energy consumption over time,since the literature is limited to cross-sectional studies that lack differentiation between climatic and non-climatic influences.Future projections for the EU’s electricity sector may therefore neglect the potential implications of rapidly growing AC demand.We develop a novel decomposition framework,which breaks down residential space cooling energy consumption in EU-28 countries into the effect of different components from 2000 to 2015.Decomposition is extended to panel data models identifying specific drivers of space cooling’s climate-sensitive components.Finally,we explore scenarios of residential AC energy consumption up to 2050 and evaluate their impact on summer time peak loads.AC diffusion was found to be the key driver of space cooling energy consumption,but this effect was partly counterbalanced by efficiency gains.While weather influences AC equipment ownership rate in EU-28 households,personal income has a larger marginal effect.In baseline scenarios,AC diffusion saturates by 2050,while modestly increasing sectoral final energy use.Still,our range of scenario values for space cooling energy consumption in 2050 exceed the majority of those originat-ing from recently published projections.In a future renewables-driven electricity system,energy security risks may emerge from a scenario of fast AC up-take in new and renovated buildings,especially for colder European countries where modelled peak cooling electricity demand is shown to outgrow the projected expansion of solar capacity.These findings have important implications for the EU’s strategy to decarbonise energy supply.

The Positive Research of Human Capital Structure and the Disparity Problem of Area in Our Country

The factors that influence the economic growth are various and complicated.This paper has especially probed into calculating and impact on regional economic growth of the human capital structure. First, on the basis of considering human capital quality, we use Gini coefficient law to calculate human capital structure coefficient of our country＇s each province （municipal or district）; Second, according to the result of calculating of human capital structure coefficient, considering input of material capital, average education level and so on at the same time, we set up regional economic growth model and use the panel data to examine the model. The result indicates the human capital structure coefficient of each province （municipal or district）in inverse proportion to economic growth （- 0. 108）. The last is the conclusion of this text.

Spatial transmission and meteorological determinants of tuberculosis incidence in Qinghai Province,China:a spatial clustering panel analysis

Background:Tuberculosis(TB)is the notifiable infectious disease with the second highest incidence in the Qinghai province,a province with poor primary health care infrastructure.Understanding the spatial distribution of TB and related environmental factors is necessary for developing effective strategies to control and further eliminate TB.Methods:Our TB incidence data and meteorological data were extracted from the China Information System of Disease Control and Prevention and statistical yearbooks,respectively.We calculated the global and local Moran’s I by using spatial autocorrelation analysis to detect the spatial clustering of TB incidence each year.A spatial panel data model was applied to examine the associations of meteorological factors with TB incidence after adjustment of spatial individual effects and spatial autocorrelation.Results:The Local Moran’s I method detected 11 counties with a significantly high-high spatial clustering(average annual incidence:294/100000)and 17 counties with a significantly low-low spatial clustering(average annual incidence:68/100000)of TB annual incidence within the examined five-year period;the global Moran’s I values ranged from 0.40 to 0.58(all P-values<0.05).The TB incidence was positively associated with the temperature,precipitation,and wind speed(all P-values<0.05),which were confirmed by the spatial panel data model.Each 10°C,2 cm,and 1 m/s increase in temperature,precipitation,and wind speed associated with 9%and 3%decrements and a 7%increment in the TB incidence,respectively.Conclusions:High TB incidence areas were mainly concentrated in south-western Qinghai,while low TB incidence areas clustered in eastern and north-western Qinghai.Areas with low temperature and precipitation and with strong wind speeds tended to have higher TB incidences.

Direct energy rebound effect for road transportation in China

The enhancement of energy efficiency stands as the principal avenue for attaining energy conservation and emissions reduction objectives within the realm of road transportation.Nevertheless,it is imperative to acknowledge that these objectives may,in part or in entirety,be offset by the phenomenon known as the energy rebound effect(ERE).To quantify the long-term EREs and short-term EREs specific to China’s road transportation,this study employed panel cointegration and panel error correction models,accounting for asymmetric price effects.The findings reveal the following:The long-term EREs observed in road passenger transportation and road freight transportation range from 13%to 25%and 14%to 48%,respectively;in contrast,the short-term EREs in road passenger transportation and road freight transportation span from 36%to 41%and 3.9%to 32%,respectively.It is noteworthy that the EREs associated with road passenger transportation and road freight transportation represent a partial rebound effect,falling short of reaching the magnitude of a counterproductive backfire effect.This leads to the inference that the upsurge in energy consumption within the road transportation sector cannot be solely attributed to advancements in energy efficiency.Instead,various factors,including income levels,the scale of commodity trade,and industrial structure,exert more substantial facilitating influences.Furthermore,the escalation of fuel prices fails to dampen the demand for energy services,whether in the domain of road passenger transportation or road freight transportation.In light of these conclusions,recommendations are proffered for the formulation of energy efficiency policies pertinent to road transportation.

Uncovering the Relationship between FDI,Human Capital and Technological Progress in Chinese High-technology Industries

Using a panel dataset for 28 sub-industries from 5 Chinese industries from 1995 to 2006, this paper examines the impact of human capital, R＆D expenditure and FD1 spillover on the productivity improvement of Chinese high-technology industries. The whole industry sample results suggest that human capital promotes total factor productivity, technical change and technical efficiency change, but that FDI lowers all of these factors in Chinese high-technology industry. When we distinguish between types of ownership structure in the industries, we find that human capital improves technical change but lowers technical efficiency change, whereas FDI only improves technical efficiency change in state-owned and state-controlled enterprises but reduces technical change in state-owned and state-controlled enterprises and joint ventures.

How does Bank Capital Affect Bank Profitability and Risk？ Evidence from China＇s WTO Accession

This paper examines how bank capital affects bank profitability and risk in China, and how its impact differed before and after the nation entered the WTO. Our study uses the dynamic generalized method of moments approach with a panel database containing 171 Chinese commercial banks. We ftnd that bank capital has significant influence on bank profitability and risk, but its impact has declined since China joined the WTO in 2001. For different sized groups, the impact of capital on profitability exhibits a distinct trend. The effects of capital on bank risk are different for large and small banks depending on the risk variables used for the Chinese banking industry.

Regional inequality, spatial spillover effects, and the factors influencing city-level energy-related carbon emissions in China

Data show that carbon emissions are increasing due to human energy consumption associated with economic development. As a result, a great deal of attention has been focused on efforts to reduce this growth in carbon emissions as well as to formulate policies to address and mitigate climate change. Although the majority of previous studies have explored the driving forces underlying Chinese carbon emissions, few have been carried out at the city-level because of the limited availability of relevant energy consumption statistics. Here, we utilize spatial autocorrelation, Markov-chain transitional matrices, a dynamic panel model, and system generalized distance estimation(Sys-GMM) to empirically evaluate the key determinants of carbon emissions at the city-level based on Chinese remote sensing data collected between 1992 and 2013. We also use these data to discuss observed spatial spillover effects taking into account spatiotemporal lag and a range of different geographical and economic weighting matrices. The results of this study suggest that regional discrepancies in city-level carbon emissions have decreased over time, which are consistent with a marked spatial spillover effect, and a ‘club' agglomeration of high-emissions. The evolution of these patterns also shows obvious path dependence, while the results of panel data analysis reveal the presence of a significant U-shaped relationship between carbon emissions and per capita GDP. Data also show that per capita carbon emissions have increased in concert with economic growth in most cities, and that a high-proportion of secondary industry and extensive investment growth have also exerted significant positive effects on city-level carbon emissions across China. In contrast, rapid population agglomeration, improvements in technology, increasing trade openness, and the accessibility and density of roads have all played a role in inhibiting carbon emissions. Thus, in order to reduce emissions, the Chinese government should legislate to inhibit the effects of factors that promote the release of carbon while at the same time acting to encourage those that mitigate this process. On the basis of the analysis presented in this study, we argue that optimizing industrial structures, streamlining extensive investment, increasing the level of technology, and improving road accessibility are all effective approaches to increase energy savings and reduce carbon emissions across China.

Inter-industry Technology Spillover Effects in China:Evidence from 35 Industrial Sectors

The present paper calculates 35 industrial sectors similarity matrices for the period of 1997-2008 using China s input-output tables for 1997, 2002 and 2007, and uses these to measure inter-industry technology spillover to analyze the spillover effects on industrial sectors lahor productivity. The empirical analysis shows that inter-industry technology spillover has a significant positive effect on the labor productivity of each industry. The elasticity of productivity effects of inter-industry technology spillover is not only larger than that of direct R＆D input, but also increases over time. We group the industries into four major categories and find that the inter-industry technology spillover effect within the categories is, on average, greater than that between the four categories, indicating that technology spillover occurs more easily between similar industries. This research shows that the interindustry technology spillover effect in China has begun to increase, and the government should take advantage of this effect.

Chinese decentralization and income inequality between urban and rural residents--An empirical study based on the dual dimensions of internal and external budget

The paper firstly explains the mechanism of decentralization on income inequality from two theoretical dimensions of internal and external budget.Based on the re-measurement of income Gini coefficient and Chinese decentralization level,this paper establishes panel data model from national and regional perspective to empirically investigate the impact of Chinese decentralization on income inequality between urban and rural residents.Firstly,the promotion of budgetary income decentralization is conductive to reduce income inequality of urban and rural residents whether it is from the national or sub regional.Second,although the promotion of budgetary expenditure decentralization exacerbates the urban-rural income inequality in the national level,it could obviously reduce the urban-rural income inequality in the midwest with relatively low level of budgetary expenditure decentralization.Thirdly,the impact of extra-budgetary decentralization on income inequality is consistent,no matter in the whole country,in eastern or in western regions.This means that the extra-budgetary income decentralization makes the income inequality between urban and rural residents worse while the extra-budgetary expenditure decentralization reduces the income inequality between urban and rural residents with different degrees.Fourthly,the transfer payment,years of schooling and urban unemployment rate are conductive to narrow the income inequality between urban and rural residents while the impacts of economic development,urbanization and opening degree are obviously different in different regions.The above conclusions can not only be used for reference to further perfecting and reshaping the fair efficiency mechanism of Chinese decentralization,but also indicates the direction of the new fiscal and tax system reform.