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1 – 10 of over 47000Yong Liu, Jun-liang Du, Ren-Shi Zhang and Jeffrey Yi-Lin Forrest
This paper aims to establish a novel three-way decisions-based grey incidence analysis clustering approach and exploit it to extract information and rules implied in panel data.
Abstract
Purpose
This paper aims to establish a novel three-way decisions-based grey incidence analysis clustering approach and exploit it to extract information and rules implied in panel data.
Design/methodology/approach
Because of taking on the spatiotemporal characteristics, panel data can well-describe and depict the systematic and dynamic of the decision objects. However, it is difficult for traditional panel data analysis methods to efficiently extract information and rules implied in panel data. To effectively deal with panel data clustering problem, according to the spatiotemporal characteristics of panel data, from the three dimensions of absolute amount level, increasing amount level and volatility level, the authors define the conception of the comprehensive distance between decision objects, and then construct a novel grey incidence analysis clustering approach for panel data and study its computing mechanism of threshold value by exploiting the thought and method of three-way decisions; finally, the authors take a case of the clustering problems on the regional high-tech industrialization in China to illustrate the validity and rationality of the proposed model.
Findings
The results show that the proposed model can objectively determine the threshold value of clustering and achieve the extraction of information and rules inherent in the data panel.
Practical implications
The novel model proposed in the paper can well-describe and resolve panel data clustering problem and efficiently extract information and rules implied in panel data.
Originality/value
The proposed model can deal with panel data clustering problem and realize the extraction of information and rules inherent in the data panel.
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Panel data-based demand forecasting models have been widely adopted in various industrial settings over the past few decades. Despite being a highly versatile and intuitive method…
Abstract
Purpose
Panel data-based demand forecasting models have been widely adopted in various industrial settings over the past few decades. Despite being a highly versatile and intuitive method, in the literature, there is a lack of comprehensive review examining the strengths, the weaknesses, and the industrial applications of panel data-based demand forecasting models. The purpose of this paper is to fill this gap by reviewing and exploring the features of various main stream panel data-based demand forecasting models. A novel process, in the form of a flowchart, which helps practitioners to select the right panel data models for real world industrial applications, is developed. Future research directions are proposed and discussed.
Design/methodology/approach
It is a review paper. A systematically searched and carefully selected number of panel data-based forecasting models are examined analytically. Their features are also explored and revealed.
Findings
This paper is the first one which reviews the analytical panel data models specifically for demand forecasting applications. A novel model selection process is developed to assist decision makers to select the right panel data models for their specific demand forecasting tasks. The strengths, weaknesses, and industrial applications of different panel data-based demand forecasting models are found. Future research agenda is proposed.
Research limitations/implications
This review covers most commonly used and important panel data-based models for demand forecasting. However, some hybrid models, which combine the panel data-based models with other models, are not covered.
Practical implications
The reviewed panel data-based demand forecasting models are applicable in the real world. The proposed model selection flowchart is implementable in practice and it helps practitioners to select the right panel data-based models for the respective industrial applications.
Originality/value
This paper is the first one which reviews the analytical panel data models specifically for demand forecasting applications. It is original.
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Critics say cryptocurrencies are hard to predict and lack both economic value and accounting standards, while supporters argue they are revolutionary financial technology and a…
Abstract
Purpose
Critics say cryptocurrencies are hard to predict and lack both economic value and accounting standards, while supporters argue they are revolutionary financial technology and a new asset class. This study aims to help accounting and financial modelers compare cryptocurrencies with other asset classes (such as gold, stocks and bond markets) and develop cryptocurrency forecast models.
Design/methodology/approach
Daily data from 12/31/2013 to 08/01/2020 (including the COVID-19 pandemic period) for the top six cryptocurrencies that constitute 80% of the market are used. Cryptocurrency price, return and volatility are forecasted using five traditional econometric techniques: pooled ordinary least squares (OLS) regression, fixed-effect model (FEM), random-effect model (REM), panel vector error correction model (VECM) and generalized autoregressive conditional heteroskedasticity (GARCH). Fama and French's five-factor analysis, a frequently used method to study stock returns, is conducted on cryptocurrency returns in a panel-data setting. Finally, an efficient frontier is produced with and without cryptocurrencies to see how adding cryptocurrencies to a portfolio makes a difference.
Findings
The seven findings in this analysis are summarized as follows: (1) VECM produces the best out-of-sample price forecast of cryptocurrency prices; (2) cryptocurrencies are unlike cash for accounting purposes as they are very volatile: the standard deviations of daily returns are several times larger than those of the other financial assets; (3) cryptocurrencies are not a substitute for gold as a safe-haven asset; (4) the five most significant determinants of cryptocurrency daily returns are emerging markets stock index, S&P 500 stock index, return on gold, volatility of daily returns and the volatility index (VIX); (5) their return volatility is persistent and can be forecasted using the GARCH model; (6) in a portfolio setting, cryptocurrencies exhibit negative alpha, high beta, similar to small and growth stocks and (7) a cryptocurrency portfolio offers more portfolio choices for investors and resembles a levered portfolio.
Practical implications
One of the tasks of the financial econometrics profession is building pro forma models that meet accounting standards and satisfy auditors. This paper undertook such activity by deploying traditional financial econometric methods and applying them to an emerging cryptocurrency asset class.
Originality/value
This paper attempts to contribute to the existing academic literature in three ways: Pro forma models for price forecasting: five established traditional econometric techniques (as opposed to novel methods) are deployed to forecast prices; Cryptocurrency as a group: instead of analyzing one currency at a time and running the risk of missing out on cross-sectional effects (as done by most other researchers), the top-six cryptocurrencies constitute 80% of the market, are analyzed together as a group using panel-data methods; Cryptocurrencies as financial assets in a portfolio: To understand the linkages between cryptocurrencies and traditional portfolio characteristics, an efficient frontier is produced with and without cryptocurrencies to see how adding cryptocurrencies to an investment portfolio makes a difference.
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Yu Yvette Zhang, Qi Li and Dong Li
This chapter reviews the recent developments in the estimation of panel data models in which some variables are only partially observed. Specifically we consider the issues of…
Abstract
This chapter reviews the recent developments in the estimation of panel data models in which some variables are only partially observed. Specifically we consider the issues of censoring, sample selection, attrition, missing data, and measurement error in panel data models. Although most of these issues, except attrition, occur in cross-sectional or time series data as well, panel data models introduce some particular challenges due to the presence of persistent individual effects. The past two decades have seen many stimulating developments in the econometric and statistical methods dealing with these problems. This review focuses on two strands of research of the rapidly growing literature on semiparametric and nonparametric methods for panel data models: (i) estimation of panel models with discrete or limited dependent variables and (ii) estimation of panel models based on nonparametric deconvolution methods.
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Rohit Apurv and Shigufta Hena Uzma
The purpose of the paper is to examine the impact of infrastructure investment and development on economic growth in Brazil, Russia, India, China and South Africa (BRICS…
Abstract
Purpose
The purpose of the paper is to examine the impact of infrastructure investment and development on economic growth in Brazil, Russia, India, China and South Africa (BRICS) countries. The effect is examined for each country separately and also collectively by combining each country.
Design/methodology/approach
Ordinary least square regression method is applied to examine the effects of infrastructure investment and development on economic growth for each country. Panel data techniques such as panel least square method, panel least square fixed-effect model and panel least square random effect model are used to examine the collective impact by combining all countries in BRICS. The dynamic panel model is also incorporated for analysis in the study.
Findings
The results of the study are mixed. The association between infrastructure investment and development and economic growth for countries within BRICS is not robust. There is an insignificant relationship between infrastructure investment and development and economic growth in Brazil and South Africa. Energy and transportation infrastructure investment and development lead to economic growth in Russia. Telecommunication infrastructure investment and development and economic growth have a negative relationship in India, whereas there is a negative association between transport infrastructure investment and development and economic growth in China. Panel data results conclude that energy infrastructure investment and development lead to economic growth, whereas telecommunication infrastructure investment and development are significant and negatively linked with economic growth.
Originality/value
The study is novel as time series analysis and panel data analysis are used, taking the time span for 38 years (1980–2017) to investigate the influence of infrastructure investment and development on economic growth in BRICS Countries. Time-series regression analysis is used to test the impact for individual countries separately, whereas panel data regression analysis is used to examine the impact collectively for all countries in BRICS.
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Yaru Huang, Yaojun Ye and Mengling Zhou
This paper aims to build an improved grey panel clustering evaluation model and evaluate the comprehensive development potential of industrial economy, society and ecological…
Abstract
Purpose
This paper aims to build an improved grey panel clustering evaluation model and evaluate the comprehensive development potential of industrial economy, society and ecological environment in the Yangtze River Economic Belt of China. The purpose of this study is to provide some theoretical basis and tool support for management departments and relevant researchers engaged in industrial sustainable development.
Design/methodology/approach
This study uses the driving force pressure state impact response analysis framework to build a comprehensive evaluation index system. Based on the center point triangle whitening weight function, it classifies the panel grey clustering of improvement time and index weight.
Findings
The results show that there are great differences in the level of industrial ecological development in different regions of the Yangtze River Economic Belt, which further illustrates the scientificity and rationality of the evaluation method proposed in this paper.
Practical implications
Due to the industrial ecological development is in a constantly changing state, and the information is uncertain. Whitening weight function is introduced to represent the complete information of relevant data. The industrial ecological evaluation involves a comprehensive complex system, which belongs to the panel data analysis problem. The improved grey panel clustering evaluation model is applied to grade the industrial ecological development level of the Yangtze River Economic Belt. The results have important guiding significance for the balanced development of industrial ecology in the region.
Social implications
Due to the industrial ecological development is in a constantly changing state, and the information is uncertain. Whitening weight function is introduced to represent the complete information of relevant data. The industrial ecological evaluation involves a comprehensive complex system, which belongs to the panel data analysis problem. In order to improve the effectiveness of industrial ecological evaluation, the improved grey panel clustering evaluation model is applied to grade the industrial ecological development level of the Yangtze River Economic Belt. The results have important guiding significance for the balanced development of industrial ecology in the region.
Originality/value
the new model proposed in this paper complements and improves the grey clustering analysis theory of panel data, that is, aiming at the subjective limitation of using time degree to determine time weight in panel grey clustering, a comprehensive theoretical method for determining time weight is creatively proposed. Combining the DPSIR (Driving force-Pressure-State-Influence-Response) model model with ecological development, a comprehensive evaluation model is constructed to make the evaluation results more authentic and comprehensive.
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Badi H. Baltagi, Georges Bresson and Jean-Michel Etienne
This chapter proposes semiparametric estimation of the relationship between growth rate of GDP per capita, growth rates of physical and human capital, labor as well as other…
Abstract
This chapter proposes semiparametric estimation of the relationship between growth rate of GDP per capita, growth rates of physical and human capital, labor as well as other covariates and common trends for a panel of 23 OECD countries observed over the period 1971–2015. The observed differentiated behaviors by country reveal strong heterogeneity. This is the motivation behind using a mixed fixed- and random coefficients model to estimate this relationship. In particular, this chapter uses a semiparametric specification with random intercepts and slopes coefficients. Motivated by Lee and Wand (2016), the authors estimate a mean field variational Bayes semiparametric model with random coefficients for this panel of countries. Results reveal nonparametric specifications for the common trends. The use of this flexible methodology may enrich the empirical growth literature underlining a large diversity of responses across variables and countries.
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Dang Luo, Lili Ye, Yanli Zhai, Hanyu Zhu and Qicun Qian
Hazard assessment on drought disaster is of great significance for improving drought risk management. Due to the complexity and uncertainty of the drought disaster, the index…
Abstract
Purpose
Hazard assessment on drought disaster is of great significance for improving drought risk management. Due to the complexity and uncertainty of the drought disaster, the index values have some grey multi-source heterogeneous characteristics. The purpose of this paper is to construct a grey projection incidence model (GPIM) to evaluate the hazard of the drought disaster characterised by the grey heterogeneity information.
Design/methodology/approach
First, the index system of the drought hazard risk is established based on the formation mechanism of the drought disaster. Then, the GPIM for the heterogeneous panel data is constructed to assess drought hazard of five cities in Henan Province. Subsequently, based on the assessment results, the grey clustering model is employed for the regional division.
Findings
The findings demonstrate that five cities in central Henan Province are divided into three categories, which correspond to three different risk grades, respectively. With respect to different drought risk areas, corresponding countermeasures and suggestions are proposed.
Practical implications
This paper provides a practical and effective new method for the hazard assessment on drought disaster. Meanwhile, these countermeasures and suggestions can help policy makers to improve the efficiency of drought resistance work and reduce the losses caused by drought disasters in Henan Province.
Originality/value
This paper proposes a new GPIM which resolves the assessment problems of the uncertain systems with grey heterogeneous information, such as real numbers, interval grey numbers and three-parameter interval grey numbers. It not only expands the application scope of the grey incidence model, but also enriches the research of panel data.
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Mohammad Qabaja and Goktug Tenekeci
The research aims to study the regression, cointegration and causality between the construction sector (CS) and the Gross Domestic Product (GDP), considering other variables in…
Abstract
Purpose
The research aims to study the regression, cointegration and causality between the construction sector (CS) and the Gross Domestic Product (GDP), considering other variables in the study such as interest rate, taxation, industry sector, investment and Foreign Direct Investment (FDI), which are analyzed through unique panel models. The study was conducted in Turkey and the ten other countries of the European Union (EU) from 1988 to 2019.
Design/methodology/approach
Regression, cointegration and causality methods were used to investigate the different types of relationships between variables in the models. Data were obtained from official databases and the study contains four main stages, which are explained in detail in the methodology section.
Findings
The study used the analysis methods of regression, cointegration and causality tests and found that the CS and GDP have long-run estimates and the relationship between the two for different countries is negative in a two-way direction. Results are detailed in the analysis section.
Research limitations/implications
No data were available for the variables before 1988 for most countries, which led to a limited number of observations and issues in statistical analysis methods.
Originality/value
Previously, only input and output tables were used in the analysis. The impact of interest rate, taxation, investment and FDI has not been analyzed. Key variables are very relevant for Turkey, which suffers from chronical inflation and taxation regimes. These show variability with the EU countries for comparative analysis and have not been explored to date, remaining as a major gap for the construction industry. No attempts were made to use regression, cointegration and causality methods with variables. These analysis methods enable an understanding of the differences in variance (heteroscedasticity) and the presence of cross-sectional dependence (CSD), both critical for the reliability of the comparison of data sets and analysis.
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The purpose of this study is to develop and verify a methodology for a zoned deformation prediction model for super high arch dams, which is indeed a panel data-based regression…
Abstract
Purpose
The purpose of this study is to develop and verify a methodology for a zoned deformation prediction model for super high arch dams, which is indeed a panel data-based regression model with the hierarchical clustering on principal components.
Design/methodology/approach
The hierarchical clustering method is used to highlight the main features of the time series. This method is used to select the typical points of the measured ambient and concrete temperatures as predictors and divide the deformation observation points into groups. Based on this, the panel data of each zone can be established, and its type can be judged using F and Hausman tests successively. Then hydrostatic–temperature–time–season models for zones can be constructed. Through the comparative analyses of the distributions and the fitted coefficients of these zones, the spatial deformation mechanism of a dam can be identified. A super high arch dam is taken as a case study.
Findings
According to the measured radial displacements during the initial operation period, the investigated pendulums are divided into four zones. After tests, fixed-effect regression models are established. The comparative analyses show that the dam deformation conforms to the natural condition. The factors such as the unstable temperature field and the nonlinear time-dependent effect have obvious effects on the dam deformation. The results show the efficiency of the proposed methodology in zoning and prediction modeling for deformation of super high arch dams and the potential to mining dam deformation mechanism.
Originality/value
A zoned deformation prediction model for super high arch dams is proposed where hierarchical clustering on principal component method and panel data model are combined.
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