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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

Abstract

Details

Travel Survey Methods
Type: Book
ISBN: 978-0-08-044662-2

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

Keywords

Open Access
Article
Publication date: 10 August 2022

Rama K. Malladi

Critics say cryptocurrencies are hard to predict, lack both economic value and accounting standards, while supporters argue they are revolutionary financial technology and…

Abstract

Purpose

Critics say cryptocurrencies are hard to predict, 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

We use 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. Cryptocurrency price, return and volatility are forecasted using five traditional econometric techniques: pooled ordinary least squares (OLS) regression, fixed-effects model (FEM), random-effects 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.

Details

China Accounting and Finance Review, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 1029-807X

Keywords

Book part
Publication date: 24 January 2022

Serdar Yaman and Turhan Korkmaz

Introduction: Financial failure is a concept that may arise from many internal and external factors such as operational, financial, and economic items and may incur…

Abstract

Introduction: Financial failure is a concept that may arise from many internal and external factors such as operational, financial, and economic items and may incur serious losses. Over-indebtedness arising from managerial misjudgments may cause high financial distress, insufficiency, and bankruptcy. In this regard, determination of effects of capital structure decisions on financial failure risk is crucial.

Aim: The main purpose of this study is to explore the relationship between capital structure decisions and financial failure risk. For this purpose, data from Borsa İstanbul (BIST) for listed food and beverage companies for the period from 2004 to 2019 is used. Another purpose of this study is to compare the financial failure models considering capital structure theories.

Method: In the study, capital structure decisions are associated with five different financial ratios; while the financial failure risk is proxied by financial failure scores of Altman (1968), Springate (1978), Ohlson (1980), Taffler (1983), and Zmijewski (1984). Therefore, five different panel data models are used for testing these hypotheses.

Findings: The results of panel data analysis reveal that capital structure decisions have statistically significant effects on financial failure risk for all models; however, those effects vary from one financial failure model to another. Also, the results show that in the models in which financial failure risk is proxied by the Altman (1968) and Taffler (1983) scores, the aggressive financial policies increase the financial failure risk. However, regarding the models in which financial failure risk is proxied by the Springate (1978), Ohlson (1980), and Zmijewski (1984) scores, aggressive financial policies decrease the financial failure risk.

Originality of the Study: To the best of our knowledge, this chapter is original and important in terms of revealing the effects of capital structure decisions on the financial failure risk and comparing the financial failure models.

Implications: The results revealed that the risk of financial failure models represented by Altman (1968) and Taffler (1983) scores are found to be statistically stronger and more successful in meeting theoretical expectations compared to other models. Therefore, it would be more appropriate to refer Altman’s (1968) and Taffler’s (1983) financial failure models in financial failure risk measurements.

Details

Insurance and Risk Management for Disruptions in Social, Economic and Environmental Systems: Decision and Control Allocations within New Domains of Risk
Type: Book
ISBN: 978-1-80117-140-3

Keywords

Article
Publication date: 2 December 2019

Torsten Doering, Nallan C. Suresh and Dennis Krumwiede

Longitudinal investigations are often suggested but rarely used in operations and supply chain management (OSCM), mainly due to the difficulty of obtaining data. There is…

Abstract

Purpose

Longitudinal investigations are often suggested but rarely used in operations and supply chain management (OSCM), mainly due to the difficulty of obtaining data. There is a silver lining in the form of existing large-scale and planned repeated cross-sectional (RCS) data sets, an approach commonly used in sociology and political sciences. This study aims to review all relevant RCS surveys with a focus on OSCM, as well as data and methods to motivate longitudinal research and to study trends at the plant, industry and geographic levels.

Design/methodology/approach

A comparison of RCS, panel and hybrid surveys is presented. Existing RCS data sets in the OSCM discipline and their features are discussed. In total, 30 years of Global Manufacturing Research Group data are used to explore the applicability of analytical methods at the plant and aggregate level and in the form of multilevel modeling.

Findings

RCS analysis is a viable alternative to overcome the confines associated with panel data. The structure of the existing data sets restricts quantitative analysis due to survey and sampling issues. Opportunities surrounding RCS analysis are illustrated, and survey design recommendations are provided.

Practical implications

The longitudinal aspect of RCS surveys can answer new and untested research questions through repeated random sampling in focused topic areas. Planned RCS surveys can benefit from the provided recommendations.

Originality/value

RCS research designs are generally overlooked in OSCM. This study provides an analysis of RCS data sets and future survey recommendations.

Book part
Publication date: 6 September 2021

Rachel S. Rauvola, Cort W. Rudolph and Hannes Zacher

In this chapter, the authors consider the role of time for research in occupational stress and well-being. First, temporal issues in studying occupational health…

Abstract

In this chapter, the authors consider the role of time for research in occupational stress and well-being. First, temporal issues in studying occupational health longitudinally, focusing in particular on the role of time lags and their implications for observed results (e.g., effect detectability), analyses (e.g., handling unequal durations between measurement occasions), and interpretation (e.g., result generalizability, theoretical revision) were discussed. Then, time-based assumptions when modeling lagged effects in occupational health research, providing a focused review of how research has handled (or ignored) these assumptions in the past, and the relative benefits and drawbacks of these approaches were discussed. Finally, recommendations for readers, an accessible tutorial (including example data and code), and discussion of a new structural equation modeling technique, continuous time structural equation modeling, that can “handle” time in longitudinal studies of occupational health were provided.

Details

Examining and Exploring the Shifting Nature of Occupational Stress and Well-Being
Type: Book
ISBN: 978-1-80117-422-0

Keywords

Article
Publication date: 1 October 2008

P. de Jager

Empirical accounting research frequently makes use of data sets with a time‐series and a cross‐sectional dimension ‐ a panel of data. The literature review indicates that…

Abstract

Empirical accounting research frequently makes use of data sets with a time‐series and a cross‐sectional dimension ‐ a panel of data. The literature review indicates that South African researchers infrequently allow for heterogeneity between firms when using panel data and the empirical example shows that regression results that allow for firm heterogeneity are materially different from regression results that assume homogeneity among firms. The econometric analysis of panel data has advanced significantly in recent years and accounting researchers should benefit from those improvements.

Details

Meditari Accountancy Research, vol. 16 no. 2
Type: Research Article
ISSN: 1022-2529

Keywords

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

Keywords

Article
Publication date: 19 June 2019

Aarthee Ragunathan and Ezhilmaran Devarasan

The offence against femininity has not only destroyed India’s development but also its future. When it comes down to the most important factor like sex, the social evils…

Abstract

Purpose

The offence against femininity has not only destroyed India’s development but also its future. When it comes down to the most important factor like sex, the social evils like “sati” and “dowry” that had been plaguing our country have been banned in India. India is the most dangerous nation in regard to sexual violence against women, according to the summary of the Thomson Reuters Foundation, 2018. The purpose of this paper is to determine the relationship between the total populations of women with other different types of women crime in all states in India.

Design/methodology/approach

This paper will review existing panel data analysis literature and apply this knowledge in finding the highly occurred women crimes in India. Using R software the following models are analysed: pooled ordinary least squares, fixed effects models and random effects models for analysing the women crimes in India.

Findings

In this paper, the authors identify that the fixed effects model is more appropriate for the analysis of women crimes in India.

Practical implications

Violence against women is a social, economic, developmental, legal, educational, human rights and health issue. This paper can be used to find the importance of women crime types. Moreover, the police or legal department can take actions according to the crime types.

Originality/value

There is a lack of literature considering the crimes against women. This will help the society to understand women crime types because the only type of violence that has received much attention by the media is rape. But, through our panel data analysis, we conclude that kidnapping, abduction and dowry death are the most occurred crimes against women in India.

Details

Journal of Criminological Research, Policy and Practice, vol. 5 no. 2
Type: Research Article
ISSN: 2056-3841

Keywords

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