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The purpose of this paper is to provide a comprehensive review of the respondents’ fraud phenomenon in online panel surveys, delineate data quality issues from surveys of broad and narrow populations, alert fellow researchers about higher incidence of respondents’ fraud in online panel surveys of narrow populations, such as logistics professionals and recommend ways to protect the quality of data received from such surveys.

This general review paper has two parts, namely, descriptive and instructional. The current state of online survey and panel data use in supply chain research is examined first through a survey method literature review. Then, a more focused understanding of the phenomenon of fraud in surveys is provided through an analysis of online panel industry literature and psychological academic literature. Common survey design and data cleaning recommendations are critically assessed for their applicability to narrow populations. A survey of warehouse professionals is used to illustrate fraud detection techniques and glean additional, supply chain specific data protection recommendations.

Surveys of narrow populations, such as those typically targeted by supply chain researchers, are much more prone to respondents’ fraud. To protect and clean survey data, supply chain researchers need to use many measures that are different from those commonly recommended in methodological survey literature.

For the first time, the need to distinguish between narrow and broad population surveys has been stated when it comes to data quality issues. The confusion and previously reported “mixed results” from literature reviews on the subject have been explained and a clear direction for future research is suggested: the two categories should be considered separately.

Specific fraud protection advice is provided to supply chain researchers on the strategic choices and specific aspects for all phases of surveying narrow populations, namely, survey preparation, administration and data cleaning.

This paper can greatly benefit researchers in several ways. It provides a comprehensive review and analysis of respondents’ fraud in online surveys, an issue poorly understood and rarely addressed in academic research. Drawing from literature from several fields, this paper, for the first time in literature, offers a systematic set of recommendations for narrow population surveys by clearly contrasting them with general population surveys.

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.

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.

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.

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.

The proposed model can deal with panel data clustering problem and realize the extraction of information and rules inherent in the data panel.

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.

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.

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.

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.

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.

This paper is the first one which reviews the analytical panel data models specifically for demand forecasting applications. It is original.

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.

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.

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.

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.

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.

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.

This paper aims to deal with the grey relational problem of panel data with an attribute value of interval numbers. The grey relational model of interval number for panel data is constructed in this paper.

First, three kinds of interval grey relational operators for the behavior sequence of a dimensionless system are proposed. At the same time, the positive treatment method of interval numbers for cost-type and moderate-type indicators is put forward. On this basis, the correlation between the three-dimensional interval numbers of panel data is converted into the correlation between the two-dimensional interval numbers in time series and cross-sectional dimensions. The grey correlation coefficients of each scheme and the ideal scheme matrix are calculated in the two dimensions, respectively. Finally, the correlation degree of panel interval number and scheme ordering are obtained by arithmetic mean.

This paper proves that the grey relational model of the panel interval number still has the properties of normalization, uniqueness and proximity. It also avoids the problem that the results are not unique due to the different orders of objects in the panel data.

The effectiveness and practicability of the model is verified by taking supplier selection as an example. In fact, this model can also be widely used in agriculture, industry, society and other fields.

The accuracy of the relational results is higher and more accurate compared with the previous studies.

A linear interpolation (Lerp) approach, utilizing a common stochastic trend, is explored to impute missing values in nonstationary panel data models. The Lerp algorithm is considerably faster and easier to use than the leading methods recommended in the statistics literature. It shows through a set of simulations that the Lerp works well, whereas other existing methods fail to perform properly, when the panel data contain a high degree of missingness and/or a strong correlation across cross-sectional units. As an illustration, the method is applied to study the cost-of-living-index dataset with missing values. The test on the imputed panel data provides the supporting evidence for the U.S. economy convergence that depends on the state physical spatial proximities and the state industrial development similarities.

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.

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.

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

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.

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.

Hedonic models are commonly used in housing markets studies to obtain quantitative measures of various implicit prices. The use of panel data in other fields of research has proved to be valuable when accounting for unobserved heterogeneity. Given that houses are extremely heterogeneous, and given that it is impossible to include all relevant attributes in hedonic models, removing unobserved heterogeneity by basic panel data models sounds appealing. This paper seeks to compare results between models that use pooled cross section data and panel data. The main research question is whether the pooled model gives unbiased estimates on some basic implicit prices.

The paper applies the hedonic methodology. It uses regression analysis and estimate basic and parsimonious models that use either pooled time series and cross section data or panel data. The empirical results when using the two different approaches are compared.

The paper illustrates that the results from the pooled timeseries and cross section model could be biased for some basic implicit prices. With some nuances, it is illustrated that in specific situations the use of a basic panel data estimator could be a simple solution to the problem of misspecification due to omitted, time‐invariant explanatory variables.

Most of the included variables do not change over time, however. In these cases potential bias using a basic fixed effects approach could not be checked for. It is also problematic that the variation in some of the time‐varying variables is not reliable and small. Finally, there could be a problem with sample selection bias. This may limit the usefulness of using panel data in disaggregated hedonic house price studies.

Hedonic house price models are frequently used in housing market research. It is therefore important to study in various ways whether the traditional approaches provide unbiased results. In this paper models that use panel data are compared to models that use more traditional time series and cross section data. To the author's knowledge, this approach has not been followed before.