Search results

1 – 10 of over 14000
To view the access options for this content please click here
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

Keywords

To view the access options for this content please click here
Book part
Publication date: 19 November 2014

Daniel Felix Ahelegbey and Paolo Giudici

The latest financial crisis has stressed the need of understanding the world financial system as a network of interconnected institutions, where financial linkages play a…

Abstract

The latest financial crisis has stressed the need of understanding the world financial system as a network of interconnected institutions, where financial linkages play a fundamental role in the spread of systemic risks. In this paper we propose to enrich the topological perspective of network models with a more structured statistical framework, that of Bayesian Gaussian graphical models. From a statistical viewpoint, we propose a new class of hierarchical Bayesian graphical models that can split correlations between institutions into country specific and idiosyncratic ones, in a way that parallels the decomposition of returns in the well-known Capital Asset Pricing Model. From a financial economics viewpoint, we suggest a way to model systemic risk that can explicitly take into account frictions between different financial markets, particularly suited to study the ongoing banking union process in Europe. From a computational viewpoint, we develop a novel Markov chain Monte Carlo algorithm based on Bayes factor thresholding.

To view the access options for this content please click here
Article
Publication date: 12 January 2015

Shimiao Jiang, Shuqin Cai, Georges Olle Olle and Zhiyong Qin

More and more e-commerce web sites are using online customer reviews (OCRs) for customer segmentation. However, for durable products, customer purchases, and reviews only…

Abstract

Purpose

More and more e-commerce web sites are using online customer reviews (OCRs) for customer segmentation. However, for durable products, customer purchases, and reviews only once for a long time, as while the product review score may highly affected by service factors or be “gently” evaluated. Existing regression or machine learning-based methods suffer from low accuracy when applied to the OCRs of durable products on e-commerce web sites. The purpose of this paper is to propose a new approach for customer segment analysis base on OCRs of durable products.

Design/methodology/approach

The research proposes a two-stage approach that employs latent class analysis (LCA): the feature-mention matrix construction stage and the LCA-based customer segmentation stage. The approach considers reviewers’ mention on product features, and the probability-based LCA method is adopted upon the characteristics of online reviews, to effectively cluster reviewers into specified segmentations.

Findings

The research finding is that, using feature-mention instead of feature-opinion records makes segment analysis more effective. The research also finds that, LCA method can better explain the characteristics of the OCR data of durable products for customer segmentation.

Practical implications

The research proposes a new approach to durable product review mining for customer segmentation analysis. The segment analysis result can provide supports for new product design and development, repositioning of existing products, marketing strategy development and product differentiation.

Originality/value

A new approach for customer segmentation analysis base on OCRs of durable products is proposed.

Details

Kybernetes, vol. 44 no. 1
Type: Research Article
ISSN: 0368-492X

Keywords

Content available
Article
Publication date: 29 March 2021

Nikiforos T. Laopodis

This paper aims to investigate the impact of global macro and other risk factors of the New York Stock Exchange (NYSE)- and National Association of Securities Dealers…

Abstract

Purpose

This paper aims to investigate the impact of global macro and other risk factors of the New York Stock Exchange (NYSE)- and National Association of Securities Dealers Automated Quotation (NASDAQ)-listed shipping companies’ stock returns from January 2001 to December 2019.

Design/methodology/approach

The methodological design includes multi-factor regressions for individual companies, augmented versions of these regressions to examine the likely impact of additional factors and finally panel regressions to assess the impact risk factors on all companies simultaneously. Estimations are done via ordinary least squares and the generalized method of moments.

Findings

Multi-factor model results showed that some of the US-specific and global macro risk factors surfaced as statistically significant for most of the companies and appeared to exhibit a consistent pattern in the way they affected shipping stocks. Thus, these companies’ exposures emanate mostly from the general US market’s movements and to a lesser extent from other firm-specific factors. Second, from the results of panel specifications, this study observes that domestic risk factors such as unemployment, inflation rates and industrial production growth emerged as significant for the NYSE-listed companies. As regard, the NASDAQ-listed ones, it was found that Libor and the G20 inflation rate were also affecting their stock returns.

Research limitations/implications

Companies examined are listed only in the US’s NYSE and NASDAQ. Hence, companies listed elsewhere were excluded. It may be concluded that these US exchange-listed companies abide mostly by domestic fundamentals and to some extent to selected global factors.

Practical implications

The significance of the findings in this study pertains to global investors and shipping companies’ managers alike. Specifically, given the differential sensitivities of the shipping companies to various risk factors (and the global business cycle, in general), it is possible to view the shipping companies’ stocks as a separate, alternate asset class in a global, well-diversified portfolio. Thus, such a broader portfolio would permit investors to earn positive returns and reduce overall risk. Managers of shipping companies would also benefit from the findings in this study in the sense that they should better understand the varying exposures of their companies to changing global and domestic macro conditions and successfully navigate their companies through business cycles.

Originality/value

Research on the global shipping industry has lagged behind and was mainly concentrated on the investigation of the sources of shipping finance and capital structure of shipping companies, investment and valuation, corporate governance and risk measurement and management. Empirical research on the potential micro and macro determinants of the stock returns of shipping companies, however, is scant. This paper fills the gap in the literature of identifying and evaluating the various macroeconomic, US and international risk, factors that affect shipping companies’ stock returns in a highly financially integrated world.

Details

Maritime Business Review, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 2397-3757

Keywords

To view the access options for this content please click here
Case study
Publication date: 1 September 2017

Yu Wen

With the development of inclusive financial business in China in recent years, this case describes the credit risk control of “mobile credit”, a smart online credit…

Abstract

With the development of inclusive financial business in China in recent years, this case describes the credit risk control of “mobile credit”, a smart online credit platform launched by Shanghai Mobanker Co. Ltd. (referred to as “Mobanker”, previously named as “Shanghai Mobanker Financial Information Service Co., Ltd.) which provides technical services for inclusive finance industry.

Details

Management School, Fudan University, vol. no.
Type: Case Study
ISSN: 2632-7635
Published by: Management School, Fudan University

Keywords

To view the access options for this content please click here
Article
Publication date: 4 December 2017

Linhai Wu, Guangqian Qiu, Jiao Lu, Minghua Zhang and Xiaowei Wen

The purpose of this paper is to investigate the responsibility that should be taken by different pork supply chain participants to ensure pork quality and safety, with the…

Abstract

Purpose

The purpose of this paper is to investigate the responsibility that should be taken by different pork supply chain participants to ensure pork quality and safety, with the aim of providing some guidance for strengthening the supervision of pork quality and safety.

Design/methodology/approach

The pig farmer survey and the pork consumer survey were conducted in Funing County, Jiangsu Province, using the best-worst scaling (BWS) and a mixed logit model.

Findings

The results showed that the designation of responsibility for ensuring pork quality and safety was of, in descending order, feed producers and suppliers, backyard farmers and farms of designated size, pork processing workshops and companies of and above designated size, slaughterhouses, supermarkets, farmer’s markets, pig transporters, and consumers. Both pig farmers and pork consumers believed that those involved in the initial pork supply chain should take greater responsibility for pork quality and safety.

Originality/value

Allocation of responsibilities across the entire pork industry chain was investigated from the perspective of pig farmers and pork consumers using the BWS and a mixed logit model. The results of this study might explain the unique problems that occur in pork supply chain management in large developing countries like China.

Details

British Food Journal, vol. 119 no. 12
Type: Research Article
ISSN: 0007-070X

Keywords

To view the access options for this content please click here
Article
Publication date: 11 November 2020

Himanshu Gupta, Sarangdhar Kumar, Simonov Kusi-Sarpong, Charbel Jose Chiappetta Jabbour and Martin Agyemang

The aim of this study is to identify and prioritize a list of key digitization enablers that can improve supply chain management (SCM). SCM is an important driver for…

Abstract

Purpose

The aim of this study is to identify and prioritize a list of key digitization enablers that can improve supply chain management (SCM). SCM is an important driver for organization's competitive advantage. The fierce competition in the market has forced companies to look the past conventional decision-making process, which is based on intuition and previous experience. The swift evolution of information technologies (ITs) and digitization tools has changed the scenario for many industries, including those involved in SCM.

Design/methodology/approach

The Best Worst Method (BWM) has been applied to evaluate, rank and prioritize the key digitization and IT enablers beneficial for the improvement of SC performance. The study also used additive value function to rank the organizations on their SC performance with respect to digitization enablers.

Findings

The total of 25 key enablers have been identified and ranked. The results revealed that “big data/data science skills”, “tracking and localization of products” and “appropriate and feasibility study for aiding the selection and adoption of big data technologies and techniques ” are the top three digitization and IT enablers that organizations need to focus much in order to improve their SC performance. The study also ranked the SC performance of the organizations based on digitization enablers.

Practical implications

The findings of this study will help the organizations to focus on certain digitization technologies in order to improve their SC performance. This study also provides an original framework for organizations to rank the key digitization enablers according to enablers relevant in their context and also to compare their performance with their counterparts.

Originality/value

This study seems to be the first of its kind in which 25 digitization enablers categorized in four main categories are ranked using a multi-criteria decision-making (MCDM) tool. This study is also first of its kind in ranking the organizations in their SC performance based on weights/ranks of digitization enablers.

Details

Industrial Management & Data Systems, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 0263-5577

Keywords

To view the access options for this content please click here
Article
Publication date: 1 August 2018

Chau Ngoc Dang and Long Le-Hoai

The purpose of this paper is to develop several predictive models for estimating the structural construction cost and establish range estimation for the structural…

Abstract

Purpose

The purpose of this paper is to develop several predictive models for estimating the structural construction cost and establish range estimation for the structural construction cost using design information available in early stages of residential building projects.

Design/methodology/approach

Information about residential building projects is collected based on project documents from construction companies with regard to the design parameters and the actual structural construction costs at completion. Storey enclosure method (SEM) is fundamental for determining the building design parameters, forming the potential variables and developing the cost estimation models using regression analysis. Nonparametric bootstrap method is used to establish range estimation for the structural construction cost.

Findings

A model which is developed from an integration of advanced SEM, principle component analysis and regression analysis is robust in terms of predictability. In terms of range estimation, cumulative probability-based range estimates and confidence intervals are established. While cumulative probability-based range estimates provide information about the level of uncertainty included in the estimate, confidence intervals provide information about the variability of the estimate. Such information could be very crucial for management decisions in early stages of residential building projects.

Originality/value

This study could provide practitioners with a better understanding of the uncertainty and variability included in the cost estimate. Hence, they could make effective improvements on cost-related management approaches to enhance project cost performance.

Details

Engineering, Construction and Architectural Management, vol. 25 no. 7
Type: Research Article
ISSN: 0969-9988

Keywords

To view the access options for this content please click here
Article
Publication date: 10 December 2020

Dareen Ryied Al-Tawal, Mazen Arafah and Ghaleb Jalil Sweis

Cost estimation is one of the most significant steps in construction planning, which must be undertaken in the preliminary stages of any project; it is required for all…

Abstract

Purpose

Cost estimation is one of the most significant steps in construction planning, which must be undertaken in the preliminary stages of any project; it is required for all projects to establish the project's budget. Confidence in these initial estimates is low, primarily due to the limited availability of suitable data, which leads the construction projects to frequently end up over budget. This paper investigated the efficacy of artificial neural networks (ANNs) methodologies in overcoming cost estimation problems in the early phases of the building design process.

Design/methodology/approach

Cost and design data from 104 projects constructed over the past five years in Jordan were used to develop, train and test ANN models. At the detailed design stage, 53 design factors were utilized to develop the first ANN model; then the factors were reduced to 41 and were utilized to develop the second predictive model at the schematic design stage. Finally, 27 design factors available at the concept design stage were utilized for the third ANN model.

Findings

The models achieved average cost estimation accuracy of 98, 98 and 97% in the detailed, schematic and concept design stages, respectively.

Research limitations/implications

This paper formulated the aims and objectives to be applicable only in Jordan using historical data of building projects.

Originality/value

The ANN approach introduced as a management tool is expected to provide the stakeholders in the engineering business with an indispensable tool for predicting the cost with limited data at the early stages of construction projects.

Details

Engineering, Construction and Architectural Management, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 0969-9988

Keywords

To view the access options for this content please click here
Article
Publication date: 5 April 2019

Sang Quang Van, Long Le-Hoai and Chau Ngoc Dang

The purpose of this paper is to predict implementation cost contingencies for residential construction projects in flood-prone areas, where floods with storms frequently…

Abstract

Purpose

The purpose of this paper is to predict implementation cost contingencies for residential construction projects in flood-prone areas, where floods with storms frequently cause serious damage and problems for people.

Design/methodology/approach

Expert interviews are conducted to identify the study variables. Based on bills of quantities and project documents, historical data on residential construction projects in flood-prone areas are collected. Pearson correlation analysis is first used to check the correlations among the study variables. To overcome multicollinearity, principal component analysis is used. Then, stepwise multiple regression analysis is used to develop the cost prediction model. Finally, non-parametric bootstrap method is used to develop range estimation of the implementation cost.

Findings

A list of project-related variables, which could significantly affect implementation costs of residential construction projects in flood-prone areas, is identified. A model, which is developed based on an integration of principle component analysis and regression analysis, is robust. Regarding range estimation, 10, 50 and 90 percent cost estimates, which could provide information about the uncertainty levels in the estimates, are established. Furthermore, implementation cost contingencies which could show information about the variability in the estimates are determined for example case projects. Such information could be critical to cost-related management of residential construction projects in flood-prone areas.

Originality/value

This study attempts to predict implementation cost contingencies for residential construction projects in flood-prone areas using non-parametric bootstrap method. Such contingencies could be useful for project cost budgeting and/or effective cost management.

Details

International Journal of Managing Projects in Business, vol. 12 no. 4
Type: Research Article
ISSN: 1753-8378

Keywords

1 – 10 of over 14000