Panel Data Clustering Research Articles

Using panel data clustering regression analysis to revisit income inequalities in the European Union

ABSTRACT Income inequality has long been a focal point of concern within European Union countries. This paper aims to revisit this topic from a newer methodological framework, employing panel data clustering regression. The iterative partitional algorithm relies entirely on the data when providing the optimal number of clusters and the membership, it allows the estimation of fixed-effects panel models ensuring homogeneity within the cluster, and it highlights the different slope coefficients across clusters. Building upon the stimulating and disturbing main factors identified in the analysis, we discuss possible measures for mitigating income inequality at the EU level, tailored to each resultant cluster. First-cluster countries should prioritize initiatives aimed at enhancing access to public education to alleviate poverty and have better educated people, while public interventions leveraging direct taxes and social transfers are more effective in reducing inequality across the remaining clusters.

EU Countries Tax Evasion Clustering

The problem of tax evasion is becoming more and more prominent, and it is causing large losses in individual state budgets. However, European policies have taken significant steps to eliminate them over the last two decades. National policies also contribute to reducing the estimated extent of tax evasion, as measured by Schneider's MIMIC model. However, these tendencies are not the same in individual countries. Therefore, in our paper, we try to find pattern in tax evasion time trajectories of European Union countries and thus to create homogenous clusters that include countries with similar tax evasion situation. To meet our goal, we use panel data clustering method on our data with information about tax evasion of EU countries from 2000 to 2019. As a next step, we compare created cluster from the perspective of quality indicators of public institutions.

An Exploration of Chinese-Russian Youth Humanistic Exchanges and Cooperation Based on VAR Modeling

Abstract In the cross-cultural context, interpersonal trust and cooperative behaviors become more complex due to mutual adaptation, communication and interaction between different cultures and races. This study explores the self-perception and materialism of Chinese and Russian youth in humanistic exchanges and the impact on interpersonal trust and cooperation behavior. This paper constructs a humanistic exchange and cooperation evaluation model based on VAR. We apply the panel data clustering method based on structural relationships to model and analyze heterogeneous and cross-sectionally independent PVAR. The variable coefficient PVAR is divided into K classes according to the structural relationship aggregation to reduce the number of parameters estimated by the model. The model estimation method of PVAR based on relational clustering in the case of cross-section independence is proposed. Among the various scores of humanistic exchanges between Chinese and Russian youths, the academic self dimension score of self-perception is positively proportional to the materialistic desire dimension score of materialism, with a correlation coefficient of 0.376. From the test of the differences in interpersonal trust and cooperative behavior between Chinese and Russian youths, Chinese youths’ interpersonal trust (M=5.36) and collective behavior (M=36.18) are higher than those of Russian youths’ interpersonal trust (M=4.27) and cooperative behavior (M=33.94). The combined results show that there is still a need to improve the cross-cultural adaptation of Russian youth coming to China and to enhance cooperative behavior with others.

A Random Matrix Model of Regional Economic Disparity Based on Spatial Panel Data Analysis

This paper adopts a random matrix approach to model spatial panel data and applies the model to the analysis of regional economic disparity. A panel data clustering method based on variable influence and response structural relationship clustering is proposed to define the structural relationship distance, cluster the panel data from the structural relationship so that the data within the class have the same structural relationship, and give the specific algorithm for determining the classification number in the clustering process based on the structural relationship. The methods of clustering panel data with the presence of linear, nonlinear, and functional relationships are considered separately to aggregate and classify the panel data in terms of the structural relationships of parameters. Using the classification modeling approach proposed in this paper, the empirical analysis is better than the estimation effect in the case of no classification; the empirical test data also show that there is a strong cross-sectional correlation between the economic and financial development data of different regions, and the estimation effect of the model with the correlation treatment is better than the estimation effect of the model without the correlation treatment. The study finds that there is a significant imbalance between financial and economic development, the phenomenon of financial exclusion is serious, and the dynamic influence relationship between financial and economic development in relatively less developed regions is different from that between financial development and economic development in relatively developed regions. In addition to the influencing factors, we should improve the efficiency of investment utilization, give better play to the geographical advantages, keep the industrial structure rationalized, develop the special economy according to the local conditions, and give full play to the spatial effect as well as improve the economic development.

Deformation Analysis of an Ultra-High Arch Dam under Different Water Level Conditions Based on Optimized Dynamic Panel Clustering

During the operation period, the deformation of an ultra-high arch dam is affected by the large fluctuation of the reservoir water level. Under the dual coupling of the ultra-high dam and the complex water level conditions, the traditional variational analysis method cannot be sufficiently applied to its deformation analysis. The deformation analysis of the ultra-high arch dam, however, is very important in order to judge the dam safety state. To analyze the deformation law of different parts of an ultra-high arch dam, the panel data clustering theory is used to construct a Spatio-temporal characteristic model of dam deformation. In order to solve the difficult problem of the fluctuating displacement of dam deformation with water level effect, three displacement component indexes (absolute quantity, growing, and fluctuation) are proposed to characterize dam deformation. To further optimize the panel clustering deformation model, the objective weight coefficient of clustering comprehensive distance is calculated based on the CRITIC (CRiteria Importance Through Inter-criteria Correlation) method. The zoning rules of the ultra-high arch dam are established by using the idea of the CSP (Constraint Satisfaction Problem) index, and the complex water level of the reservoir is simulated in the whole process. Finally, the dynamic cluster analysis of dam deformation is realized. Through a case study, three typical working conditions including the rapid rise and fall of water level and the normal operation are calculated, and the deformation laws of different deformation zones are analyzed. The results show that the model can reasonably describe the deformation law of an ultra-high arch dam under different water levels, conveniently and intuitively select representative measuring points and key monitoring parts, effectively reducing the analysis workload of lots of measuring points, and improve the reliability of arch dam deformation analysis.

Prediction of the failure point settlement in rockfill dams based on spatial-temporal data and multiple-monitoring-point models

The integrity of monitoring data is important in the study of the deformation law of rockfill dams. Environment, construction, aging, and other factors result in dam monitoring sensors malfunction at the initial stage of operation. Data collection discontinue leads to insufficient monitoring data. The huge amount of missing data is lager than traditional model training samples and increases the difficulty of data recovery in failure points. In this study, the multiple-monitoring-point (MMP) model is established to extend the number of training set samples. MMP model integrates spatiotemporal information to make predictions of long-term missing data in malfunctioning settlement sensors according to the corresponding relationship among the coordinate position, environment values, and settlement. Additionally, this paper presents the study of monitoring point selection and the clustering time period division in the MMP model. The spatiotemporal data clustering analysis is used as the measurement method to determine the settlement similarity to screen the appropriate data. Experiments on large-scale real dam deformation data demonstrate that the MMP model is suitable for the long-term data prediction of failures in rockfill dam settlement monitoring. After the spatiotemporal panel data clustering analysis, the model prediction accuracy is significantly improved. This model provides a new method for dam settlement prediction and analysis.

Application of Clustering Analysis of Panel Data in Economic and Social Research Based on R Software

Panel clustering analysis has been widely used in the study of difference categories in the economic and social fields due to its advantages of analyzing data in both individual and time dimensions. After elaborating on the theory of Univariate panel data and Multivariable panel data clustering method, taking the 2008-2018 provincial panel data of Chinese residents' consumption index as an example, the R software implementation methods, procedures and specific steps of the single index and multiple indices of panel data clustering was investigated in this paper, hoping to provide a reference for the workers and researchers in economic and social statistics analysis.

Clustering Dynamics and Persistence for Financial Multivariate Panel Data

We introduce a new method for dynamic clustering of panel data with dynamics for cluster location and shape, cluster composition, and for the number of clusters. Whereas current techniques typically result in (economically) too many switches, our method results in economically more meaningful dynamic clustering patterns. It does so by extending standard cross-sectional clustering techniques using shrinkage towards previous cluster means. In this way, the different cross-sections in the panel are tied together, substantially reducing short-lived switches of units between clusters (flickering) and the birth and death of incidental, economically less meaningful clusters. In a Monte Carlo simulation, we study how to set the penalty parameter in a data-driven way. A systemic risk surveillance example for business model classification in the global insurance industry illustrates how the new method works empirically.

Research on the difference of Digital inclusive Finance-based on Multi-index Panel data clustering

Objective: to understand the development level of digital inclusive finance in 31 provinces in China in recent years, so that the areas with poor development level can speed up the development. Methods: the data of Peking University Digital inclusive Financial Index from 2012 to 2018 were collected, and the optimal clustering number was determined, and then the cross-sectional data and multi-index panel data were clustered respectively. Conclusion: the level of digital inclusive finance in China shows an upward trend as a whole, but there are great differences in the development level of digital inclusive finance among 31 provinces in China, in which East China is the best, South China and Central China have a better overall development level, and North China, Northwest, Southwest and Northeast are poor in overall development level, but have provincial differences.

Оценка качества кластеризации панельных данных с использованием методов Монте-Карло (на примере данных российской региональной экономики)

В работе рассматривается метод исследования панельных данных, основанный на использовании агломеративной иерархической кластеризации — группировки объектов на основании сходства и разли- чия их признаков в иерархию вложенных друг в друга кластеров. Применялись 2 альтернативных способа вычисления евклидовых расстояний между объектами — расстояния между усредненными по интервалу наблюдений значениями и расстояния с использованием данных за все рассматриваемые годы. Сравнивались 3 альтернативных метода вычисления расстояний между кластерами. В первом случае таким расстоянием считается расстояние между ближайшими элементами из двух кластеров, во втором — среднее по парам элементов, в третьем — расстояние между наиболее удаленными элементами. Исследована эффективность использования двух индексов качества кластеризации — индекса Данна и Силуэта для выбора оптимального числа кластеров и оценки статистической значимости полученных решений. Способ оценивания статистической достоверности кластерной структуры заключался в сравнении качества кластеризации, на реальной выборке с качеством кластеризаций на искусственно сгенерированных выборках панельных данных с теми же самыми числом объектов, признаков и длиной рядов. Генерация производилась из фиксированного вероятностного распределения. Использовались способы симуляции, имитирующие гауссов белый шум и случайное блуждание. Расчеты с индексом Силуэт показали, что случайное блуждание характеризуется не только ложной регрессией, но и ложной кластеризацией. Кластеризация принималась достоверной для данного числа выделенных кластеров, если значение индекса на реальной выборке оказывалось больше значения 95%-ного квантиля для искусственных данных. В качестве выборки реальных данных использован набор временных рядов показателей, характеризующих производство в российских регионах. Для этих данных только Силуэт показывает достоверную кластеризацию на уровне $p < 0.05$. Расчеты также показали, что значения индексов для реальных данных в целом ближе к значениям для случайных блужданий, чем для белого шума, но имеют значимые отличия и от тех, и от других. Визуально можно выделить скопления близко расположенных друг от друга в трехмерном признаковом пространстве точек, выделяемые также в качестве кластеров применяемым алгоритмом иерархической кластеризации.

Regional differences of air pollution in China: comparison of clustering analysis and systematic clustering methods of panel data based on gray relational analysis

Ecological environment deterioration caused by air pollution (AP) seriously threatens human life and health, with academic circles focusing on how to improve AP control efficiency, promote regional collaborative regulatory control of AP, and ensure the safety of human life. This research thus uses time-based systematic clustering of cross-sectional data and gray correlation clustering of panel data to analyze AP situations and regional differences (RD) via AP index data of 31 provinces and cities in China from 2014 to 2018. The results show that the air quality of these provinces and cities has improved and that the concentration of pollutants has declined to varying degrees, but AP in Beijing, Tianjin, and other places is still more serious. While the effect of AP improvement in some areas is not distinct, the trend of regional AP continues to be quite serious. This paper reveals current RD of AP in China and offers some suggestions and recommendations on pollution control, such as further optimizing the industrial structure and energy consumption structure and improving people’s concept of ecological civilization.

An Improved Grey Clustering Model with Multiattribute Spatial-Temporal Feature for Panel Data and Its Application

Due to the complexity and uncertainty of the objective world and the limitation of cognition, it is difficult to extract the information and rules contained in the panel data effectively based on the traditional panel data clustering method. Given this, considering that the absolute amount level, increasing amount level, and volatility level are the main indicators to represent the spatial-temporal feature of the panel data, a novel grey clustering model with the multiattribute spatial-temporal feature of panel data is established, and then it is applied in the regional high-tech industrialization in China. The results show that the proposed model can make full use of the spatial-temporal feature information of the panel data, identify the problems existing in the clustering objects, and make the clustering results more objective and practical.

Semiparametric Estimation and Panel Data Clustering Analysis Based on D-Vine and C-Vine

This paper proposed a panel data clustering model based on D-vine and C-vine and supported a semiparametric estimation for parameters. These models include a two-step inference function for margins, two-step semiparameter estimation, and stepwise semiparametric estimation. In similarity measurement, similarity coefficients are constructed by a multivariate Hierarchical Nested Archimedean Copula (HNAC) model and compound PCC models, which are HNAC and D-vine compound model and HNAC and C-vine compound model. Estimation solutions and models evaluation are given for these models. In the case study, the clustering results of HNAC and D-vine compound model and HNAC and C-vine compound model are given, and the effect of different copula families on clustering results is also discussed. The result shows the models are effective and useful.

Panel data clustering analysis based on composite PCC: a parametric approach

This paper proposed a panel data clustering model based on Hierarchical Nested Archimedean Copula (HNAC) model and compound PCC models. The method provides a new approach to panel data clustering, which breaks through the limitations of the traditional data clustering and time series clustering. This article makes full use of the dependence structure between the sectional individuals, as well as the degree of correlation between time series data. The similar structure was constructed by HNAC and Pair Copula to reflect the change of the clustering results. The selection of Copula clusters is very flexible giving the clustering results more accurate, robust, and easily interpreted. The computing efficiency is high and the estimation for the goodness-of-fit test are given based on compound PCC method in this paper. In the case study, the clustering results of compound PCC models are excellent. The result shows that the compound PCC models are effective and useful.

Analysis of the investment security of the accommodation industry for countries along the B&amp;R

The construction of the B&amp;R (the Silk Road Economic Belt and the 21st-Century Maritime Silk Road) is undergoing promotion. In addition, China’s foreign investment is growing. Moreover, investment in the accommodation industry is an important component of investment in the B&amp;R. Therefore, analysis is necessary to understand the security of the investment environment, the division of the investment security zone and the selection of optimal investments. This research applies a safety evaluation index system on investment in the accommodation industry using relevant data on 24 countries along the B&amp;R from 2005 to 2014 as study samples. The results of the empirical analysis show that factor analysis based on multidimensional panel data estimated the safety of the investment of the sample countries. The coefficient of variation of the sample countries across 10 years was very large, meaning that the investment environment of sample countries was complex. The samples were divided into high, medium and low regions by means of panel data clustering analysis. Variance analysis showed significant differences among the three regions. The research used the technique for order preference by similarity to an ideal solution (TOPSIS) method to identify the optimal country in which to invest. The optimal country was Greece and the worst was Macedonia. Finally, this research provides some analysis on conclusions and data selection.

Variable-intercept panel model for deformation zoning of a super-high arch dam.

This study determines dam deformation similarity indexes based on an analysis of deformation zoning features and panel data clustering theory, with comprehensive consideration to the actual deformation law of super-high arch dams and the spatial–temporal features of dam deformation. Measurement methods of these indexes are studied. Based on the established deformation similarity criteria, the principle used to determine the number of dam deformation zones is constructed through entropy weight method. This study proposes the deformation zoning method for super-high arch dams and the implementation steps, analyzes the effect of special influencing factors of different dam zones on the deformation, introduces dummy variables that represent the special effect of dam deformation, and establishes a variable-intercept panel model for deformation zoning of super-high arch dams. Based on different patterns of the special effect in the variable-intercept panel model, two panel analysis models were established to monitor fixed and random effects of dam deformation. Hausman test method of model selection and model effectiveness assessment method are discussed. Finally, the effectiveness of established models is verified through a case study.

An improved grey relational analysis approach for panel data clustering

An enhanced grey clustering analysis method based on accumulation sequences using grey relational analysis (AGRA) is put forward for specifying hierarchies of clusters in panel data. The clustering method can handle panel data containing N samples, each of which has m time series of indicators for which the observations for a given time series can be measured at different times than other series and contain different numbers of data points compared to other series. The overall clustering approach, which is called the Mean-AGRA clustering method, contains three main parts: a sequence of transformations of each separate time series; appropriate pairwise comparisons of the grey relational degree of an AGRA model for each pair of samples, across all samples as well as appropriate combinations thereafter, for three specific types of grey relational degrees; clustering all samples according to their AGRA degrees. To demonstrate how this new clustering method can be utilized in practice, it is applied to panel data consisting of 12 natural environmental indicators and 8 societal time series (m = 20) for 30 provinces (N = 30) in mainland China. The findings clarify how, for example, the provinces in China can be meaningfully categorized according to topography into two main groups consisting of plateaus and plains. The new method can handle different lengths of time series within a sample and across samples, which is useful when values occur at different times when comparing any two series. Moreover, the new clustering method avoids the problem of combining two samples having a limited degree of similarity, which exists in the traditional method. Consequently, the AGRA model and Mean-AGRA clustering method have expanded the scope of application of grey relational and clustering analysis.

Credit card customer analysis based on panel data clustering

Abstract In this paper, we propose a new distance measurement which can be used in panel data clustering. The distance as we designed can be calculated with weight and without weight. If users put more attention on recent data, a heavier weight can be assigned to the recent data. We use real panel data of a commercial bank’s credit card to examine the performance of our new distance measurement. The results show that our distance measurement can reflect the information of different periods and panel data can be used to cluster to find new knowledge. This study discovers different knowledge structure from the traditional econometrics analysis with the help of data mining algorithms.