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Sponsor: Shaanxi Provincial Department of Education
Sponsored by：Xi'an University of Finance and Economics
Higher Education Branch of China Statistical Education Society
Director: ZHAO MINJUAN
Vice Director: LI JIAORUI ZHAO YANYUN
Publisher: Editorial Department of Journal of Statistics and Information
Address:No. 64, XiaozhaiEast Road, Yanta District, Xi’an, China
Post Code：710061
E-mail: tjyxxlt@126.com
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Relative Risk Confidence Interval Construction Based on Saddle Point Approximation under Poisson Distribution
HOU Jian;LIU Shuo;TIAN Maozai;Relative risk(RR) is a commonly used indicator in epidemiological randomized controlled trials to compare morbidity or mortality between two groups.Interval estimation of RR is an effective statistical inference method that reflects uncertainty and confidence.However, traditional interval estimation methods, such as Wald's interval, Wilson interval, Fisher interval, etc.,have certain limitations: large error, low coverage, long interval length, and unreasonable interval.In order to overcome these shortcomings, an interval estimation method based on saddle point approximation is proposed: in the context of Poisson distribution, the approximation formula of RR is derived by using Taylor expansion and Laplace integral, and the corresponding confidence interval construction method is given.Through Monte Carlo simulation and empirical analysis, the saddle point approximation method is compared with some existing interval estimation methods, and the results show that the saddle point approximation method can obtain the shortest confidence interval in the case of small or large samples, and the interval coverage is closer to the nominal level, which provides a superior interval estimation method for RR.Finally, the advantages and limitations of the saddle point approximation method, as well as its application value and prospect in epidemiological research are discussed.
Synergistic Effects of Carbon Mitigation and Economic Growth in China: Measurement and Regional Disparities
LI Boying;Guided by the “Dual Carbon” strategy, identifying pathways for the coordinated development of carbon reduction and economic growth-towards the goal of common prosperity-has emerged as a critical challenge in China's modernization process.Using national panel data from 2000 to 2022 and provincial panel data for 30 provinces from 2000 to 2021,this study employs the Tapio decoupling model and the Logarithmic Mean Divisia Index(LMDI) decomposition method from the perspective of environmental equity.It systematically assesses the decoupling relationship between carbon emissions and economic growth at both the national and provincial levels, and further explores the key drivers and underlying factors contributing to regional disparities in decoupling status.The findings indicate, firstly, a “weak decoupling” relationship between carbon emissions and economic growth nationwide, characterized by significant regional variations.Specifically, less developed provinces exhibit higher decoupling elasticity, generally experiencing faster growth in carbon emissions compared to economically developed regions.Secondly, factors such as energy structure, energy consumption intensity, per capita GDP,and population size significantly influence carbon decoupling elasticities, with these variables showing distinctly different impacts across various regions.Lastly, from a spatial distribution perspective, there exist notable imbalances in carbon governance efficiency and environmental equity in contemporary China.Less developed regions face dual pressures of “economic development and emission reduction” during the low-carbon transition process, potentially exacerbating regional imbalances and leading to further polarization.This study expands the theoretical analysis of carbon decoupling by integrating perspectives on environmental equity and policy coordination mechanisms, thereby providing theoretical foundations and policy recommendations for formulating region-specific carbon reduction policies and achieving the coordinated advancement of economic development and environmental protection.
Estimation of Scale and Analysis of Economic Effects of Industry Asset Replacement
XU Yingmei;CHEN Hongmin;Digital assets represent newer technology and higher levels of intelligence in the context of global digitalization and intelligentization.With technology advancing and market demand changing asset replacement has become an indispensable step in the process of digital transformation.Asset replacement focuses on upgrading assets through technological progress to improve efficiency.It is different from fixed-asset investment which focuses more on expanding asset scale.The two types of investments are now presented in a composite form.Companies not only digitally upgrade their existing assets in reconstruction and technological transformation projects, but also extensively incorporate digital assets in new construction and expansion projects.The National Development and Reform Commission has announced actions to speed up the large-scale renewal of equipment and the trade-in of consumer goods, aiming to promoting high-quality development.Based on the actions, replaceable assets are defined as assets in the stock that should be renewed but remain un-renewed.To study the potential of asset replacement and its economic effects, the productive capital stock of digital hardware, digital software and non-digital assets of China's industries from 2001 to 2020 is measured.Industries are categorized based on the Statistical Classification of the Digital Economy and its Core Industries(2021) released by the National Bureau of Statistics.Replaceable assets are classified into three categories: buildings exceeding international standard of service life, non-building assets exceeding average service life, and digital assets accelerating replacement due to technological innovation supporting requirement.The scale of replaceable assets and its contribution to economic growth are estimated both in industial and national level.The differences in the contribution patterns of digital and non-digital assets to the economy are compared under production function model.The results show that: first, China's productive capital stock accounts for about 97% of non-digital asset types such as machinery, equipment, and buildings and the digital asset investment continues to increase.The proportion of capital stock exceeding average service lives of digital hardware are higher than that of other asset types.The time lag in the penetration of digital technology indicates great potential for asset replacement.Second, the scale of replaceable assets during the five-year period from 2020 to 2024 amounts to approximately 10.47 trillion to 16.87 trillion CNY,of which digital assets contribute more than 70%.Current asset depreciation methods cannot reflect the high-speed iteration of digital technology accurately.Third, the impact of digital capital on the output of industries is significant, with marginal output elasticity being about 20%.Digital assets are growing rapidly in scale and contributing more to economic growth than nondigital assets and labor.The finding reveals the structural transformation of factor returns, indicating that digital assets have become a significant driving force for economic growth.Large-scale asset replacement will lead to a totaled 17.94 trillion to 19.04 trillion CNY increase in comparable value-added during 2020 to 2024,of which digital assets contributed more than 96%.Based on the large-scale equipment renewals policies, China should build a structured docking mechanism for enterprise asset renewal, advance the digital integration and retrofitting of non-digital assets, accelerate the upgrade of digital assets, and further improve the tax support policies for asset digital transformation, to better promote high-quality development and the digital transformation of industries.
Study on the Relationship between CEO Equity-based Incentives and Default Risk in the Banking Industry
YAO Kai;DING Ruosha;Bank default risk is a key indicator for measuring the vulnerability of banks.Existing research has pointed out that CEO compensation incentives are related to a bank's risk-taking, but the research on the two components of CEO equity incentives, CEO delta and CEO vega, is relatively lacking.By constructing the default distance indicator to measure bank default risk, this paper specifically explores the roles of CEO delta and CEO vega in default risk.CEOs equity-based compensations have two distinguished incentives: CEO delta and CEO vega.The empirical results show that CEO vega is positively associated with default risk.However, there is no significant association between default risk and CEO delta.After controlling for the effects of other CEO equity-based components, the empirical results remain robust, this indicates that CEO equity-based incentives significantly influence a bank's default risk.In the period after the passage of the GLB Act, CEO vega has a significant impact on default risk.The empirical analysis further explores the relationship between the impact of CEO vega on default risk and the risk-shifting hypothesis.By dividing banks into high-risk and low-risk groups, it is found that the impact of CEO vega on default risk is weaker for high-risk banks.Moreover, the effect of CEO vega is weaker in high-risk banks.The main innovations are focused on three aspects.First, the impacts of the two different incentive effects, CEO delta and CEO vega, is examined within equity-based incentives on default risk.Existing research focus more on how to use equity incentives to mitigate agency conflicts.However, for the banking industry, while equity incentives can alleviate principal-agent problems, at the cost of increased default risk.Therefore, when designing equity-based incentive policies, banks need to consider both the different incentive effects and default risk.This paper also studies whether the effects of equity-based incentives are influenced by the bank's own risk condition, and finds that when the default risk is higher, the marginal impact of CEO vega is weaker.The empirical findings have implications for corporate governance and risk management in the banking industry.First, given the unique characteristics of the banking industry, when designing equity-based incentives for bank executives, it is necessary to differentiate the different effects of equity-based incentives on default risk and incorporate into the bank's overall risk management considerations.Second, when equity-based incentives are used to reduce agency problems and encourage bank executives to explore innovations, it is necessary to take default risks into consideration.
Vertical Ecological Protection Compensation and Industrial Structure Upgrading
WANG Yeqiang;LUO Yangfan;The upgrading of industrial structure is conducive to economic growth and environmental protection in ecological protection areas.Vertical ecological protection compensation has a more direct and rapid impact on the industrial structure upgrading in ecological protection areas.It is of great practical significance to examine the impact of vertical ecological protection compensation on industrial structure upgrading.Based on the county-level panel data from 2010 to 2022,the quasi-natural experiment of the negative list system for industrial access in key national ecological function zones is applied to identify the impact of vertical ecological protection compensation under the background of industrial regulation on industrial structure upgrading by using the difference-in-difference model.The benchmark results confirm that vertical ecological protection compensation under the background of industrial regulation can promotes the upgrading of industrial structure, but the “running-in” of transfer payment of key national ecological function zones and negative list system for industrial access makes the policy effect lag by two years.The mechanism analysis shows that, vertical ecological protection compensation under the background of industrial regulation promotes the upgrading of industrial structure by inhibiting the industrial development of ecological protection areas and promoting the development of service industries in ecological protection areas.Through the heterogeneity analysis, it is found that the policy effect performs better in counties with low-level economic conditions and high financial pressure, and the policy effect of resource-based counties is better than that of non-resource-based counties.Therefore, it is necessary to improve the synergy between the transfer payment of key national ecological function areas and the negative list system for industrial access, to strengthen the policy effect of vertical ecological protection compensation.Optimize the system for the use of vertical ecological protection compensation, and promotes the tilt of vertical ecological protection compensation towards the service industry.Increase vertical ecological protection compensation in developing areas, and supports the industrial structure upgrading of low-level economic development, high financial pressure and resource-based regions.
Research on the Mechanism and Effect of Data Elements Driving Corporate Green Transformation
SUN Panfeng;ZHUO Ronghai;TIAN Maozai;Green development refers to an economic growth and social development that aims at efficiency,harmony and sustainability,and its core lies in realizing a harmonious symbiosis between environmental protection and economic development,striving for a win-win situation.Currently Chinese enterprises still face two major challenges in their green transformation proess:insuffieient transformation momentum and inadequate capabilities.As a core element in the era of digital economy,can data elements drive the green transformation of enterprises? What are the paths of enterprise green transformation? Is there any heterogeneity?Answering the above questions is not only crucial for enterprises to innovate their production and operation methods and promote their green,high-quality and sustainable development,but also of great significance for promoting China ' s economic transition to green development,reaching the goal of "dual-carbon",and realizing high-quality and sustainable development of the economy.Therefore,this paper focuses on the above issues to carry out theoretical analysis and empirical verification.Therefore,this paper selects all A-share non-financial listed companies in China from 2011 to 2023 as the research samples,and uses the double fixed-effects regression model,based on the micro perspective to deepen the influence of micro-level data elements on China ' s corporate green transformation and its mechanism path.The study finds:First,data elements can effectively drive the green transformation of Chinese enterprises.Second,data elements can effectively alleviate the information asymmetry problem faced by enterprises,promote green technological innovation,and then drive the green transformation of enterprises.Third,government subsidies play a positive moderating role in the process of enterprise green transformation driven by data factors,the driving effect of data elements on enterprise green transformation is more significant in the central and western regions,non-heavily polluted enterprises and state-owned enterprises than in the eastern regions,heavily polluted enterprises and non-state-owned enterprises.Based on the conclusions of the previous study,this paper constructs a systematic solution from the three dimensions of institutional innovation,subject empowerment and policy synergy to provide a multidimensional practical path for fully releasing the multiplier effect of data elements in green transformation.It not only provides both theoretical depth and practical value of decision-making reference for enterprise green transformation in the era of digital economy,but also meets the requirements of the “dual-carbon”strategy and high-quality development,and provides a way to crack the structural contradiction in the "transition pain period",and helps our country build a modernized industrial system and a green development model.
Mechanism of Data Element Marketization on the Efficiency of Green Innovation in Enterprises
WANG Jian;ZHAO Huaping;CHEN Long;In the era of digital economy,data has became the fifth key productive element alongside land,labor,technology,and capital.At the same time,enterprises must continuously explore green innovation models and enhance their green innovation efficiency to achieve high-quality development ultimately.However,tight resource constraints and significant technology gaps hamper improvements in enterprise green innovation capabilities.Consequently,the green innovation efficiency of Chinese enterprises falls substantially short of actual development needs.Therefore,whether data element marketization can enhance the efficiency of green innovation in enterprises has become a critical issue demanding urgent attention.Grounded in the resource-based view,transaction cost theory,dynamic capability theory,and the competition escape hypothesis,the study analyzes the theoretical mechanisms through which data element marketization enhances enterprises green innovation efficiency.Utilizing a quasi-natural experiment enabled by data trading platforms and employs panel data from Chinese A-share listed companies from 2012 to 2022,a double machine learning method is constructed to empirically test the impact of data element marketization on the efficiency of green innovation in enterprises.The findings reveal that the data element marketization significantly enhances the efficiency of green innovation in enterprises,a conclusion robust to a series of tests,including instrumental variable estimation,subsample regressions,and model ve-specification.Mechanism analysis indicates that the data element marketization promotes the efficiency of green innovation through dual machanisms:advancing digital transformation and intensifying market competition.The data element marketization exerts heterogeneous effects on enterprises' green innovation efficiency.Specifically,enterprises with smaller scales,those in pollutionintensive industries,and those located in the eastern region exhibit stranger responses to data element marketization.Based on the research findings,the study proposes the following recommendations.Enterprises must build data-driven decision-making,accelerate digital transformation,and leverage AI,big data and cloud computing to expand their "technology pool".This enhances data capabilities and builds infrastructure to sustainably improve green innovation efficiency.Governments should cultivate highquality data trading markets and foster sound competition to encourage strategic enterprise collaboration.Differentiated green innovation policies should prioritize large enterprises,non-heavily polluting industries,and central-western enterpries.Capitalizing on data marketization helps overcome inadequate data supply,poor circulation,and undervaluation,providing momentum for green innovation efficiency.The marginal contributions may lie in the following three aspects:First,by employing the double machine learning method within a nonlinear framework,this study controls for more variables influencing enterprise green innovation efficiency.Second,focusing on innovation efficiency,this study empirically reveals its underlying mechanisms-digital transformation and market competition-thereby enriching the literature on this topic.Finally,heterogeneity is examinedin the facilitative effect of data factor marketization across enterprises of varying size,industry,and region.The conclusions not only extend the research on the driving factors of the efficiency of green innovation in enterprises,but also provide policy guidance for the government to further deepen the market-oriented reform of data elements.
Measurement and Spatio-temporal Evolution Analysis of the Coordinated Transformation Development Level of Enterprise Digitalization and Greening
LI Huiyun;LIU Qianying;LUO Zhenghan;ZHENG Hongrui;Against the backdrop of green low-carbon development and Digital China initiatives,digitalization-greening coordinated transformation development has become critical for enterprises.This study uses A-share listed companies from 2015 to 2023 as the sample,constructing evaluation index systems for digitalization and greening based on the "Insititution-action-effectiveness" framework.Based on the measurement results,using the coupling coordination degree model,the coordinated development level is measured,and its characteristics and spatio-temporal evolution analyzed.The research reveals:(1) The deual-driven collaboration level of enterprises has been increasing year by year.The coordination at the institutional level is the best,followed by the effectiveness level,and the action level is the worst.Enterprises with a high level of digitalization and greening,as well as large-scale enterprises and state-owned enterprises,have a higher level of digitalization-greening coordinated development.(2) Across industries,the coordinated transformation development level has fluctuated upward yearly;high-tech industries outperform non-high-tech ones,and heavily polluting industries exceed non-heavily polluting ones.(3) There is positive spatial autocorrelation and spatial agglomeration in the digitalization-greening coordinated development level of companies across different provinces and municipalities.High-high agglomeration regions are mainly in the eastern areas,while low-low ones are in the western and northeastern regions.Among the five national-level urban agglomerations,enterprises in the Beijing-Tianj in-Hebei urban agglomeration have the highest level of coordinated transformation development with the smallest differences,while those in the Chengdu-Chongqing urban agglomeration have the lowest level of coordinated development with the largest differences.These findings disclose the evolution laws of Chinese listed companies' dual-driven collaboration level and offer new assessment perspectives.
Research on the Framework Design and Compilation of Satellite Accounts for Data Assets
WANG Panpan;JIA Xiao'ai;With the development of the digital economy,data assets have garnered increasing attention from both national governments and enterprises.A critical challenge in data asset accounting lies in rigorously and systematically quantifying the pivotal role of data assets play in driving the national economy.By constructing a satellite account for data asset,it becomes possible to systematically quantify data-driven economic activities and dynamically assess the value of data assets.Grounded in the interconnected structure of data assets within economic operations,this framework implements a multidimensional,hierarchical account system that comprehensively maps their lifecycle and economic contributions.The accounting entities within the data asset satellite account are classified into two dimensions based on the production and utilization of data assets,corresponding to industrial sectors and institutional sectors,respectively.The accounting objects include data products and data assets.Data products are further categorized into eight distinct types based on variations in their supply mechanisms and usage patterns,thereby facilitating the exploration of tailored valuation methodologies for different types of data products.A holistic accounting framework for data assets must systematically address two interconnected dimensions:the endogenous processes governing the creation and accumulation of data assets themselves,and the exogenous spillover effects through which these assets influence broader economic indicators.To operationalize this dual perspective,dedicated endogenous accounts and exogenous accounts and are established as complementary components of the accounting system.The endogenous accounts adhere to the principles of double-entry bookkeeping and the national economic equilibrium(total output,total income,and total expenditure are equal),structured in alignment with the core sequence of national economic accounts.Starting with production account,the framework progresses through flowbased accounts such as income distribution and capital formation.These sequential analyses culminate in stock accounting to evaluate the accumulated value of data assets.This systematic approach enables the compilation of flow accounts(including the goods and services account,the production account,the distribution of income account,and the capital account) alongside dedicated the data asset stock account,thereby capturing both transactional dynamics and cumulative asset valuation within a unified system.The exogenous accounts for data assets take Gross Domestic Product(GDP) as an example,GDP impact accounts are established under distinct GDP accounting methodologies(production,income,and expenditure approaches) to systematically quantify how data capitalization redefines GDP measurement.By extracting and applying relevant data from input-output tables to conduct an empirical case implementation of the accounts,this exercise validates the operational validity and structural coherence of the Data Asset Satellite Account framework,demonstrating its alignment with established economic measurement standards.
Construction and Application of a Time-Varying Higher-Order Moment Dual Component RCARCH Model Based on Good and Bad Volatility
GUO Baocai;In complex financial markets,higher-order moments(skewness and kurtosis) contain valuable information about asset returns and can better characterize the occurrence of extreme events.Additionally,good and bad volatility exert asymmetric effects on future volatility predictions.Therefore,built upon the two-component Realized-GARCH(RGARCH) model by incorporating time-varying higherorder moments and introducing good and bad volatility into the short-term equation,a time-varying higherorder moment two-component RGARCH(RGARCH-RS-SK) model based on good and bad volatility is proposed.This model fully utilizes the information from higher-order moments and is capable of more comprehensively capturing the features of asset return distributions,such as spikes,heavy tails,and the heterogeneity indicated by good and bad volatility.The model is applied to the Shenzhen Composite Index,and the empirical and robustness test results show:The proposed model effectively captures the asymmetry of volatility,the time-varying nature of higher-order moments,and characteristics such as "skewness,spikes,and heavy tails," demonstrating excellent fitting and forecasting performance;The Value at Risk(VaR) obtained from the proposed model serves as a good quantile estimate,and both VaR and Expected Shortfall(ES) predictions pass tests for statistical validity and loss function,allowing for more flexible measurement of market risk.These findings suggest that integrating time-varying higher-order moments,good and bad volatility,and a two-component structure can more accurately predict market volatility and risk values,supporting national economic stability and risk management.
A Review of Technologies on Random Forests
FANG Kuang-nana,b,WU Jian-bina,ZHU Jian-pinga,b,SHIA Bang-changa,b(a.Department of Statistics,School of Economics;b.Data Mining Center,Xiamen University,Xiamen 361005,China)Random Forests is a statistical learning theory,using bootsrap re-sampling method form sample sets,and then combining the tree predictors by majority voting so that each tree is grown using a new bootstrap training set.It is widely applied in medicine,bioinformatics,economics and other fields,because of its high prediction accuracy,good tolerance of noisy data,and the law of large numbers they do not overfit.In this paper we first introduce the concept of random forest and the latest research,then provide some important aspects of applications in economics,and a summary is given in the final section.
[Downloads: 39,911 ] [Citations: 1,960 ] [Reads: 44 ] HTML PDF Cite this article
Analysis on the Reliability and Validity of Questionnaire
ZENG Wu-yi~1,HUANG Bing-yi~2(1.School of Economics,Xiamen University,Xiamen 361005,Fujian;2.School of Management,Xiamen University,Xiamen 361005,Fujian)Study on the reliability and validity of the questionnaire has always been neglected in many(questionnaire) surveys in China.This paper mainly investigates the reliability and validity of a questionnaire and their evaluating methods.It also simply introduces how to use SPSS software to analyze the reliability and(validity) of a questionnaire.
A Summary of Machine Learning and Related Algorithms
CHEN Kai1,ZHU Yu1,2(1.School of Statistics,Renmin University of China,Beijing 100872,China;2.Xi'an University of Finance & Economic,Xi'an 710061,China)Since the computer was invented,people have been wanted to know that whether it can learn.Machine learning is essentially a multidisciplinary field. It absorbed some results of artificial intelligence,probability and statistics,computational complexity theory,control theory,information theory,philosophy,physiology,neurobiological.This paper mainly based on statistical learning wanted to give a brief review and presentation to the perspective of machine learning and the development of related algorithms.
[Downloads: 25,942 ] [Citations: 542 ] [Reads: 37 ] HTML PDF Cite this article
ESG Performance,Institutional Investor Preference and Firm Value of Listed Companies
BAI Xiong;ZHU Yi-fan;HAN Jin-mian;To explore whether the ESG practices of listed companies can create value for the company and whether institutional investors in the capital market have ESG investment preferences will help companies recognize, participate in and practice the concept of ESG sustainable development.Based on the data of 3 400 A-share listed companies in Shanghai and Shenzhen Stock Exchange from 2013 to 2020,the shareholding ratio of institutional investors is introduced to explore the mechanism of ESG performance affecting corporate value and analyze whether institutional investors have ESG investment preference on this basis.The results are as follows:(1) ESG has the function of value creation.Good ESG performance of listed companies can significantly enhance their corporate value.(2) Attracting institutional investors to increase their shares is one of the ways for listed companies to enhance corporate value through ESG practice, and the proportion of institutional investors plays a partial intermediary role in the process of ESG influencing corporate value.(3) Institutional investors have a preference for ESG investment, and to a certain extent, they can tolerate low short-term operating performance of listed companies with good ESG performance All the above conclusions are robust.In the extended study, it is found that there is no heterogeneity in the value creation function of ESG between state-owned and non-state-owned listed companies.The preference of institutional investors ESG has heterogeneity in property rights and industry.Institutional investors prefer the listed companies with good performance of ESG in the secondary and tertiary industries and non-state-owned enterprises.Based on the research conclusions, suggestions are puts forward to accelerating the top-level design of ESG information disclosure and regulatory standards, encouraging companies to strengthen information disclosure, and cultivating medium and long-term institutional investors, which will help build and improve China's ESG development ecosystem and promote high-quality development.
[Downloads: 21,633 ] [Citations: 468 ] [Reads: 31 ] HTML PDF Cite this article
A Review of Technologies on Quantile Regression
CHEN Jian-bao,DING Jun-jun(Macroeconomics Research Center,Xiamen University,Xiamen 361005,Fujian)Ordinary least square(OLS) regression models the relationship between vector of covariate and the conditional mean of a responsegiven.However,quantile regression models the relationship between covariateand the conditional quantiles of given.Taken together the ensemble of estimated conditional quantile offers a much more complete view of the effect of covariates on the location,scale and shape of the distribution of the response variable.It is especially useful in applications where people are interested in upper or lower quantiles of a response.In this paper we first introduce the concept of quantile regression,then provide some brief methods about estimation,hypothesis tests and goodness-of-fit of quantile regression,some important aspects of applications in economics are reviewed,a summary is given in the final section.
[Downloads: 9,970 ] [Citations: 409 ] [Reads: 33 ] HTML PDF Cite this article
A Review of Technologies on Random Forests
FANG Kuang-nana,b,WU Jian-bina,ZHU Jian-pinga,b,SHIA Bang-changa,b(a.Department of Statistics,School of Economics;b.Data Mining Center,Xiamen University,Xiamen 361005,China)Random Forests is a statistical learning theory,using bootsrap re-sampling method form sample sets,and then combining the tree predictors by majority voting so that each tree is grown using a new bootstrap training set.It is widely applied in medicine,bioinformatics,economics and other fields,because of its high prediction accuracy,good tolerance of noisy data,and the law of large numbers they do not overfit.In this paper we first introduce the concept of random forest and the latest research,then provide some important aspects of applications in economics,and a summary is given in the final section.
[Downloads: 39,911 ] [Citations: 1,960 ] [Reads: 44 ] HTML PDF Cite this article
A Summary of Machine Learning and Related Algorithms
CHEN Kai1,ZHU Yu1,2(1.School of Statistics,Renmin University of China,Beijing 100872,China;2.Xi'an University of Finance & Economic,Xi'an 710061,China)Since the computer was invented,people have been wanted to know that whether it can learn.Machine learning is essentially a multidisciplinary field. It absorbed some results of artificial intelligence,probability and statistics,computational complexity theory,control theory,information theory,philosophy,physiology,neurobiological.This paper mainly based on statistical learning wanted to give a brief review and presentation to the perspective of machine learning and the development of related algorithms.
[Downloads: 25,942 ] [Citations: 542 ] [Reads: 37 ] HTML PDF Cite this article
Analysis on the Reliability and Validity of Questionnaire
ZENG Wu-yi~1,HUANG Bing-yi~2(1.School of Economics,Xiamen University,Xiamen 361005,Fujian;2.School of Management,Xiamen University,Xiamen 361005,Fujian)Study on the reliability and validity of the questionnaire has always been neglected in many(questionnaire) surveys in China.This paper mainly investigates the reliability and validity of a questionnaire and their evaluating methods.It also simply introduces how to use SPSS software to analyze the reliability and(validity) of a questionnaire.
ESG Performance,Institutional Investor Preference and Firm Value of Listed Companies
BAI Xiong;ZHU Yi-fan;HAN Jin-mian;To explore whether the ESG practices of listed companies can create value for the company and whether institutional investors in the capital market have ESG investment preferences will help companies recognize, participate in and practice the concept of ESG sustainable development.Based on the data of 3 400 A-share listed companies in Shanghai and Shenzhen Stock Exchange from 2013 to 2020,the shareholding ratio of institutional investors is introduced to explore the mechanism of ESG performance affecting corporate value and analyze whether institutional investors have ESG investment preference on this basis.The results are as follows:(1) ESG has the function of value creation.Good ESG performance of listed companies can significantly enhance their corporate value.(2) Attracting institutional investors to increase their shares is one of the ways for listed companies to enhance corporate value through ESG practice, and the proportion of institutional investors plays a partial intermediary role in the process of ESG influencing corporate value.(3) Institutional investors have a preference for ESG investment, and to a certain extent, they can tolerate low short-term operating performance of listed companies with good ESG performance All the above conclusions are robust.In the extended study, it is found that there is no heterogeneity in the value creation function of ESG between state-owned and non-state-owned listed companies.The preference of institutional investors ESG has heterogeneity in property rights and industry.Institutional investors prefer the listed companies with good performance of ESG in the secondary and tertiary industries and non-state-owned enterprises.Based on the research conclusions, suggestions are puts forward to accelerating the top-level design of ESG information disclosure and regulatory standards, encouraging companies to strengthen information disclosure, and cultivating medium and long-term institutional investors, which will help build and improve China's ESG development ecosystem and promote high-quality development.
[Downloads: 21,633 ] [Citations: 468 ] [Reads: 31 ] HTML PDF Cite this article
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