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This article examines the history, development, and application of the sandwich estimate of variance. In describing this estimator, we pay attention to applications that have appeared in the literature and examine the nature of the problems for which this estimator is used. We describe various adjustments to the estimate for use with small samples, and illustrate the estimator’s construction for a variety of models. Finally, we discuss interpretation of results.

This chapter addresses the growing need to analyze the relationship between COVID-19 vulnerability and disability status at the national scale in the US. It presents a quantitative study that seeks to determine whether US counties more vulnerable to the COVID-19 pandemic contain significantly higher percentages of people with disabilities (PwDs), in general, and socially disadvantaged PwDs (based on their ethnicity/race, biological sex, age poverty, and employment status), in particular.

Vulnerability to COVID-19 is measured using the COVID-19 Pandemic Vulnerability Index (PVI) model developed by the National Institute of Environmental Health Sciences, which integrates multiple variables into relevant indicators that are weighted and combined to formulate a county-level PVI score. These scores are linked to a wide range of disability-related variables from the 2019 American Community Survey five-year estimates. Statistical analyses are based on multivariable generalized estimating equations that extend the generalized linear model to account for spatial clustering.

US counties more vulnerable to the COVID-19 pandemic are characterized by significantly higher percentages of PwDs, when vaccination is considered in estimating the PVI. These counties also contain significantly higher percentages of ethnic/racial minority, female, below poverty, and unemployed PwDs, in multiple timeframes of the pandemic.

The findings provide important insights and new knowledge on the relationship between COVID-19 vulnerability and socially disadvantaged PwDs in the US. The county-level associations highlight the need for additional data and more detailed analysis to examine the differential impacts of this pandemic on PwDs, as well as formulate appropriate intervention strategies.

The purpose of this paper is to consider whether regional currency integration in sub-Saharan Africa ameliorates global macroeconomic shocks by considering the impact of the 2008-2009 global financial crisis on economic growth. This suggests that Central Africa Franc Zone (CFAZ) eurocurrency union membership amplifies the effects of global business cycles in sub-Saharan Africa.

The authors estimate the parameters of a quantity theory model of economic growth within a Generalized Estimating Equation (GEE) Framework.

Parameter estimates from GEE specifications reveal that the contraction in credit during the financial crisis of 2008-2009 had larger adverse growth effects on sub-Saharan African countries who were members of the CFAZ eurocurrency union. The authors also find that sub-Saharan African countries who were members of the CFAZ eurocurrency union were more likely to experience a contraction in credit.

As far as the authors can discern, no existing empirical growth models use a GEE framework to estimate parameters of interest. The GEE parameter estimates are distribution-free, robust with respect to unknown forms of heteroskedasticity, and control for a wide variety of error structures that can induce bias in panel data parameter estimates.

The aim of this study is to identify factors that influence drivers' route choice response to travel time information about both the expressway and local streets provided by variable message signs on arterial roads.

A stated preference questionnaire survey was conducted to collect behavioral data. The generalized estimating equations (GEEs) method with a logit link function was used to model driver response and account for correlations within repeated observations from the same respondent. Four GEEs‐based estimations with different working correlation structures were conducted and compared with each other as well as the conventional maximum likelihood estimation.

Driving experiences, expressway delays, causes of delay, and the number of traffic lights on local streets are factors influencing route choice decisions. A new finding is that there exist differences in response behavior among employer‐provided car, taxi and private car drivers. On the modeling aspect, the exchangeable structure was the most appropriate in this study.

This study indicates the effectiveness and appropriateness of the GEEs method and suggests further examination of GEEs' performance.

The route choice probability model established by this study will facilitate better investment, design and assessment of dynamic information services in transportation management.

The dynamic information this study concerns has rarely been addressed in the literature. Little literature to date has applied the GEEs method in information response modeling. This study reaches solider conclusions about the GEEs method.

Longitudinal regression analysis is conducted to clarify causal relations and control for unwanted influences from actor heterogeneity and state dependence on theoretically important coefficient estimates. Because strategic management contains theory on how firms differ and how firm actions are influenced by their current strategic position and recent experiences, consistency of theory and methodology often requires use of longitudinal methods. We describe the theoretical motivation for longitudinal methods and outline some common methods. Based on a survey of recent articles in strategic management, we argue that longitudinal methods are now used more frequently than before, but the use is still inconsistent and insufficiently justified by theoretical or empirical considerations. In particular, strategic management researchers should use dynamic models more often, and should test for the presence of actor effects, autocorrelation, and heteroscedasticity before applying corrections.

This paper aims to examine the impact of gender diversity both at operational and leadership levels on the financial performance of firms in India.

The study is based on a panel data set of 100 large Indian corporate firms. This study uses the Blau index and Shannon index to compute gender diversity. First, this paper uses system generalized method of moments model to deal with the potential endogeneity issue in the association between gender diversity and firm performance. Second, to unveil heterogeneity in such a relationship, the study applies panel data quantile regression model. Finally, the study adopts a generalized estimating equation model to investigate such relationships for group affiliated and standalone firms.

This study finds a significant positive impact of workforce gender diversity and board gender diversity on the financial performance of firms. Further, the results of the quantile regression model indicate that the impact of gender diversity (workforce and board) on firm performance is more pronounced at higher quantiles of the conditional distribution of firm performance. However, the study fails to extricate any significant impact of audit committee gender diversity on firm performance. Finally, the study also finds a significant positive impact of gender diversity at both workforce and board level for a group affiliated, as well as standalone firms.

The present study makes a novel contribution to the extant literature on the association between gender diversity and financial performance of firms by examining such diversity at both operational and leadership levels in the context of an emerging country such as India that captures the complex realities pertaining to gender issues. Further, the study contributes to the empirical literature regarding the heterogeneous impact of gender diversity on firm performance in the Indian context.

The authors aim to investigate what influences a Farm Credit System association to make a patronage refund payment. In particular, they seek to investigate what causes the regional heterogeneity in the patronage refund payment decision. It is unclear whether patronage refunds have been used more as a capital management tool or as a member recruitment and retention tool. This study aims to bring some clarity to this issue.

The authors use an empirical logistic model to estimate the probability of a positive patronage refund payment by a Farm Credit System association, controlling for variables related to the associations' balance sheet as reported in the associations' quarterly call reports.

The authors find there is evidence that Farm Credit Service associations use patronage refunds as a capital management tool, at least in part. However, they also find that there are still significant regional differences in the patronage refund payment decision even after controlling for variables affecting the associations' balance sheet. The authors conclude that this likely represents member heterogeneity in preferences for patronage refunds versus a discounted interest rate.

The present study is one of the few empirical papers to examine a broad panel of financial cooperatives. Because of this, the authors' paper provides valuable insight into the aggregate behavior of Farm Credit Service associations, particularly into whether they use patronage refunds as a capital management tool, or as a marketing and retention tool.

This study aims to investigate the factors affecting daily demand in an emergency department (ED) and to provide a forecasting tool in a public hospital for horizons of up to seven days.

In this study, first, the important factors to influence the demand in EDs were extracted from literature then the relevant factors to the study are selected. Then, a deep neural network is applied to constructing a reliable predictor.

Although many statistical approaches have been proposed for tackling this issue, better forecasts are viable by using the abilities of machine learning algorithms. Results indicate that the proposed approach outperforms statistical alternatives available in the literature such as multiple linear regression, autoregressive integrated moving average, support vector regression, generalized linear models, generalized estimating equations, seasonal ARIMA and combined ARIMA and linear regression.

The authors applied this study in a single ED to forecast patient visits. Applying the same method in different EDs may give a better understanding of the performance of the model to the authors. The same approach can be applied in any other demand forecasting after some minor modifications.

To the best of the knowledge, this is the first study to propose the use of long short-term memory for constructing a predictor of the number of patient visits in EDs.

In the context of investment decisions, the intricate interplay between exogenous shocks and their influence on investor confidence significantly shapes their behaviors and, consequently, their outcomes. Investment decisions are influenced by uncertainties, exogenous shocks as well as the sentiments and confidence of investors, factors typically overlooked by decision-makers. This study will meticulously examine these multifaceted influences and discern their intricate hierarchical nuances in the sentiments of industrial entrepreneurs during the COVID-19 pandemic.

Employing the robust framework of the generalized linear latent and mixed models (GLLAMM), this research will thoroughly investigate individual and group idiosyncrasies present in diverse data compilations. Additionally, it will delve deeply into the exogeneity of disturbances across different sectors and regions.

Relevant insights gleaned from this research elucidate the adverse influence of exogenous forces, including pandemics and financial crises, on the confidence of industrial entrepreneurs. Furthermore, a significant discovery emerges in the regional analysis, revealing a notable homogeneity in the propagation patterns of industrial entrepreneurs' perceptions within the sectoral and regional context. This finding suggests a mitigation of regional effects in situations of global exogenous shocks.

Within the realm of academic inquiry, this study offers an innovative perspective in unveiling the intricate interaction between external shocks and their significant impacts on the sentiment of industrial entrepreneurs. Furthermore, the utilization of the robust GLLAMM captures the hierarchical dimension of this relationship, enhancing the precision of analyses. This approach provides a significant impetus for data-informed strategic directions.

This study aims to analyse the impact of company performance, company size, ownership structure, board characteristics and other company characteristics on the disclosure of managerial remuneration in 134 listed companies in India from the year 2003 to 2012.

A disclosure and compliance index is developed on the basis of 14 statements prepared regarding the disclosure of managerial remuneration in corporate governance reports of companies. The Papke and Wooldridge (2008) approach is adopted to estimate fractional response models, and fractional probit model is estimated using the generalised estimating equation approach, with an independent working correlation matrix to determine the effect of various company attributes on managerial remuneration disclosure.

The study shows that company size and the presence of remuneration committee are significantly related with the disclosure and compliance index of managerial remuneration. Remuneration disclosure is found to be time-dependent as time dummies for all years are found to be significant.

This study highlights the importance of the formation of remuneration committees on corporate boards. The findings of the present study can be used as inputs for promoting better compliance and comprehensive executive remuneration disclosure.

Nothing concrete in the field of managerial remuneration disclosure (to the best of researcher’s knowledge) has yet been done in an emerging economy such as India. This study aims to address this gap by deriving a disclosure and compliance index for managerial remuneration disclosure and examining the impact of various corporate attributes on it.