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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.

The paper aims to propose a clustering model for panel data. More specifically, the paper aims to construct a gray incidence model for panel data to solve the classification with multi-factors and multi-attributes.

The paper opted for a clustering theory study using gray incidence theory based on dynamic weighted function. The paper presents an example to verify the rationality of the new model, which suggests that the new model can reflect the incidence degree of panel data.

The paper provides a new gray incidence model based on a dynamic weighted function that can amplify the characteristics of the sample to some extent. The properties of the new incidence model, such as normalization, symmetry and nearness, are all satisfied. The paper also shows that the new incidence model performs very well on cluster discrimination.

The new model in this paper has supplemented and improved the gray incidence analysis theory for panel data.

The empirical analysis dealt in this paper emphasizes on the impact of military expenditures on out of pocket (OOP) healthcare payments. A sizeable body of defence economics literature has investigated the trade-off between military and public health expenditure, by testing the crowding-out or growth-stimulating hypothesis; does military expenditure scaling up crowd-out or promote governmental resources for social and welfare programs, including also state health financing?

In this study, panel data from 2000 to 2018 for 129 countries is used to examine the impact of military expenditure on OOP healthcare payments. The dataset of countries is categorized into four income-groups based on World Bank's income-group classification. Dynamic panel data methodology is applied to meet study objectives.

The findings of this study indicate that military expenditure positively affects OOP payments in all the selected groups of countries, strongly supporting in this way the crowding-out hypothesis whereby increased military expenditure reduces the public financing on health. Study econometric results are robust since different and alternative changes in specifications and samples are applied in our analysis.

Under the economic downturn backdrop for several economies in the previous decade and on the foreground of a potential limited governmental fiscal space related to the Covid-19 pandemic adverse economic effects, this study provides evidence that policy-makers have to adjust their government policy initiatives and prioritize Universal Health Coverage objectives. Consequently, the findings of this study reflect the necessity of governments as far as possible to moderate military expenditures and increase public financing on health in order to strengthen health care systems efficiency against households OOP spending for necessary healthcare utilization.

Despite the fact that a sizeable body of defence economics literature has extensively examined the impact of military spending on total and public health expenditures, nevertheless to the best of our knowledge there is no empirical evidence of any direct effect of national defence spending on the main private financing component of health systems globally; the OOP healthcare payments.

The purpose of this paper is to locate the specific items from the financial statements that are responsible for the dirty surplus accounting flows and how important they are in its explanation.

It is generally accepted that some country accounting rules allow some operations that can generate dirty surplus in the annual statements. Working on this basis, it is necessary to consider information at the same time across firms and across time, using panel data econometric techniques. A static panel data estimated by generalized least squares can be used to correct correlations between firms and account numbers or a dynamic panel data estimated by GMM‐SYS with instrumental variables to avoid endogeneity.

Results show that in a static panel data model, the income statement items have a lower explicative power of balance sheet items variations, having higher explicative power a dynamic one (AR(1)). Results show that, specifically, financial assets, debts and book value capture the dirty accounting flows.

Working in differences reduces the explicative power of the income statement and working in levels could be inconsistent if it is impossible to contrast, first, stationary in data due to their shortage. It is suggested that future works increase the frequency of the observed data, and contrast the cointegration as a way to check the accounting relationships.

It is important to evaluate whether the income statement can (or cannot) explain the financial position of a firm. Also it is important to know where dirty surplus accounting flows are located can be useful for firms' valuation.

The econometric technique proposed in the paper deals with the main limitation in accounting research: information is bigger in cross‐section (number of firms) than in time series (economic periods).

This study aims to estimate the impact of refugee inflows on host countries’ entrepreneurial rates. The refugee crisis led to an increased scientific and public policy interest in the impact of refugee inflows on host countries. One important perspective of such an impact, which is still underexplored, is the impact of refugee inflows on host countries entrepreneurial rates. Given the high number of refugees that flow to some countries, it would be valuable to assess the extent to which such countries are likely to reap the benefits from increasing refugee inflows in terms of (native and non-native) entrepreneurial talent enhancement.

Resorting to dynamic (two-step system generalized method of moments) panel data estimations, based on 186 countries over the period between 2000 and 2019, this study estimates the impact of refugee inflows on host countries’ entrepreneurial rates, measured by the total early-stage entrepreneurial activity (TEA) rate and the self-employment rate.

In general, higher refugee inflows are associated with lower host countries’ TEA rates. However, refugee inflows significantly foster self-employment rates of “medium-high” and “high” income host countries and host countries located in Africa. These results suggest that refugee inflows tend to enhance “necessity” related new ventures and/ or new ventures (from native and non-native population) operating in low value-added, low profit sectors.

This study constitutes a novel empirical contribution by providing a macroeconomic, quantitative assessment of the impact of refugee from distinct nationalities on a diverse set of host countries' entrepreneurship rates in the past two decades resorting to dynamic panel data models, which enable to address the heterogeneity of the countries and deal with the endogeneity of the variables of the model.

This study aims to examine the influence of corporate governance (CG) mechanisms and ownership structures on corporate governance disclosure (CGD) in listed Mauritian companies.

Multivariate regression techniques, both static and dynamic panel data models, were employed to analyse the effect of the determinants on the CGD level of 42 Mauritian listed companies (38 non-financial and four financial firms) from 2009 to 2019.

In the static model comprising 42 firms, CG attributes such as board size, board meeting frequency, CG committee meeting frequency and audit committee meeting frequency are major determinants of CGD, whereas ownership structure variables such as managerial ownership and institutional ownership do not influence CGD. In the dynamic model, only the CG meeting frequency is a major determinant. The determinants of CGD vary between non-financial and financial firms.

This study is limited to CGD in listed firms, excluding mandatory disclosures and unlisted firms. Future research can use qualitative approaches to better understand CGD behaviour with an extension to mandatory disclosures and non-listed firms.

Policymakers can rely on determinants to draw policy measures to raise CG standards further. Domestic and foreign investors may also depend on the determinants of their expectations of CGD while making investment and credit decisions.

This study contributes to the extant literature by examining a new determinant of CGD: CG committee meeting frequency. It also investigates any differences in the determinants between financial and non-financial firms with different listing status.

The study examines whether the growth effect of government spending is contingent on the level of institutional environment prevalent in Economic Community of West African States (ECOWAS).

The study adopts the more refined and more appropriate dynamic threshold panel by Seo and Shin (2016) and made applicable be Seo et al. (2019). The technique models a nonlinear asymmetric dynamics and cross-sectional heterogeneity simultaneously in a dynamic threshold panel data framework.

The results show that there is a threshold effect in the government spending-growth relationship. Specifically, the authors found that the impact of government spending on economic growth is positive and statistically significant only above a certain threshold level of institutional development. Below that threshold, the effect of government spending on growth is insignificant and negative at best. The findings suggest that government spending-growth nexus is contingent on the level of Institutional quality.

Unlike previous studies that adopt the linear interaction model which pre-impose a priori conditional restrictions, this study adopts the dynamic threshold panel framework which allows the lagged dependent variable and endogenous covariates.

As a standard source of capital for entrepreneurs, crowdfunding has recently gained wide attention in business and academia. With scientific endorsement, some research is conducted to explore the antecedents of online crowdfunding success. The factors that can influence the backers’ investment which is the key to success are information from prior backers’ and creators’ behaviors. Based on the signaling theory, the purpose of this paper is to systematically investigate the dynamic influences and interaction effects of signals with different forms (action-based or opinion-based signals) and sources (creator-sourced or backer-sourced signals) on backers’ investment behaviors over a project-funding cycle.

A panel data set of 3,010 projects with 640,625 transaction records from April 28, 2013 to September 31, 2017 is collected from a famous online crowdfunding platform – Zhongchou.cn in China and the negative binomial panel data model with fixed effect is used to obtain our empirical results.

The findings demonstrate that the work of different signals is significantly effective at the early stage of a project and decreases with time. Furthermore, our results show that there are both synergistic effect and substitution effect among different signals. Specifically, the direction of interaction effect depends on the forms of signals and the backers’ sensitivity toward that signal, and the interaction effects are also dynamic.

This paper has shed light on the roles of different signal types and their interactions in influencing funding behavior over a project-funding cycle, enriched the literature on crowdfunding and provided both theoretical and practical implications.