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Article
Publication date: 16 July 2019

Yong 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.

Details

Kybernetes, vol. 48 no. 9
Type: Research Article
ISSN: 0368-492X

Keywords

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Article
Publication date: 11 July 2016

Shuyun Ren and Tsan-Ming Choi

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…

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.

Details

Industrial Management & Data Systems, vol. 116 no. 6
Type: Research Article
ISSN: 0263-5577

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Book part
Publication date: 23 November 2011

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…

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.

Details

Missing Data Methods: Cross-sectional Methods and Applications
Type: Book
ISBN: 978-1-78052-525-9

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Article
Publication date: 10 August 2020

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.

Details

Indian Growth and Development Review, vol. 14 no. 1
Type: Research Article
ISSN: 1753-8254

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Book part
Publication date: 15 April 2020

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…

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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Article
Publication date: 6 September 2018

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…

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.

Details

Grey Systems: Theory and Application, vol. 8 no. 4
Type: Research Article
ISSN: 2043-9377

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Article
Publication date: 10 April 2020

Jiang Hu and Fuheng Ma

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…

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.

Details

Engineering Computations, vol. 37 no. 9
Type: Research Article
ISSN: 0264-4401

Keywords

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Article
Publication date: 31 July 2019

Durmuş Çağrı Yıldırım, Seda Yıldırım and Isıl Demirtas

The purpose of this paper is to explore the relationship between energy consumption and economic growth for Brazil, Russia, China, India, South Africa and Turkey (BRICS-T…

Abstract

Purpose

The purpose of this paper is to explore the relationship between energy consumption and economic growth for Brazil, Russia, China, India, South Africa and Turkey (BRICS-T) countries. In this context, this study investigates energy consumption and real output in BRICS-T countries through panel cointegration.

Design/methodology/approach

The data include energy consumption and real output for BRICS-T countries and period of 1990–2014. The variables are transformed into natural logarithm. To analyze these data, this study employed Pedroni cointegration test, the second-generation panel cointegration test, Westerlund and Edgerton (2008) test and FMOLS test.

Findings

Results indicate that there is a bi-directional causality relationship between energy consumption and economic growth for BRICS-T countries. An increase in GDP leads to an increase in energy consumption and an increase in energy consumption leads to an increase in GDP.

Research limitations/implications

This study used data that include the period of 1990–2014 for BRICS-T countries. So, further studies can use different periods of data or different countries.

Originality/value

This study provides important evidence that countries with strong growth performance need to follow bi-directional energy policies to increase both energy investments and ensure energy savings.

Details

World Journal of Science, Technology and Sustainable Development, vol. 16 no. 4
Type: Research Article
ISSN: 2042-5945

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Book part
Publication date: 19 October 2020

Hon Ho Kwok

This chapter develops a set of two-step identification methods for social interactions models with unknown networks, and discusses how the proposed methods are connected…

Abstract

This chapter develops a set of two-step identification methods for social interactions models with unknown networks, and discusses how the proposed methods are connected to the identification methods for models with known networks. The first step uses linear regression to identify the reduced forms. The second step decomposes the reduced forms to identify the primitive parameters. The proposed methods use panel data to identify networks. Two cases are considered: the sample exogenous vectors span Rn (long panels), and the sample exogenous vectors span a proper subspace of Rn (short panels). For the short panel case, in order to solve the sample covariance matrices’ non-invertibility problem, this chapter proposes to represent the sample vectors with respect to a basis of a lower-dimensional space so that we have fewer regression coefficients in the first step. This allows us to identify some reduced form submatrices, which provide equations for identifying the primitive parameters.

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Article
Publication date: 9 January 2017

Nusrate Aziz and M. Niaz Asadullah

While the relationship between military expenditure and economic growth during the Cold War period is well-researched, relatively less is known on the issue for the…

Abstract

Purpose

While the relationship between military expenditure and economic growth during the Cold War period is well-researched, relatively less is known on the issue for the post-Cold War era. Equally how the relationship varies with respect to exposure to conflict is also not fully examined. Therefore, the purpose of this paper is to investigate the causal impact of military expenditure on growth in the presence of internal and external threats for the period 1990-2013 using data from 70 developing countries.

Design/methodology/approach

The main estimates are based on the generalized method of moments (GMM) regression model. But for comparison purposes, the authors also report estimates using fixed and random effects as well as pooled cross-section regressions. The regression specification accounts for non-linear effect of military expenditure allowing for interaction with conflict variable (where distinction is made between external and internal conflict).

Findings

The analysis indicates that methods as well as model specification matter in studying the effect of military spending on growth. Full sample estimates based on GMM, fixed, and random effects models suggest a negative and statistically significant effect of military expenditure. However, fixed effects estimate becomes insignificant for low-income countries. The effect of military spending is also insignificant in the cross-sectional OLS model if conflict is not considered. When the regression model additionally controls for conflict, the effect of military spending conditional upon (internal) conflict exposure is significant and positive. No such effect is present conditional upon external threat.

Research limitations/implications

One important limitation of the analysis is the small sample size – the authors had to restrict analysis to 70 low and middle-income countries for which the authors could construct post-Cold War panel data on military expenditure along with information on armed conflict exposure (the later from the Uppsala Conflict Data Program, 2015).

Originality/value

To the best of the author’s knowledge, this is the first paper to examine the joint impact of military expenditure and conflict on economic growth in post-Cold War period in a sample of developing countries. Moreover, an attempt is made to review and revisit the large Cold War literature where studies vary considerably in terms findings. A key reason for this is the somewhat ad hoc choice of econometric methods – most rely on cross-section data and rarely conduct sensitivity analysis. The authors instead rely on panel data estimates but also report results based on naïve models for comparison purposes.

Details

Journal of Economic Studies, vol. 44 no. 1
Type: Research Article
ISSN: 0144-3585

Keywords

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