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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 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.

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.

The purpose of this paper is to examine the causal relationship between inward foreign direct investment (FDI) and economic growth in Southern African Development Community (SADC) countries over the period 1980-2012. It also investigates whether the causal relationship between FDI inflows and economic growth is dependent on the level of income.

In order to assess whether the causal relationship between FDI inflows and economic growth is dependent on the level of income, the study divided the SADC countries into two groups, namely, the middle-income countries and the low-income countries. The study used the recent panel-data analysis methods to examine this linkage. The Granger causality test for the middle-income countries was conducted within a vector-error correction mechanism framework; while that of the low-income countries was conducted within a vector autoregressions framework.

The results for the middle-income countries’ panel show that there is a uni-directional causal flow from GDP to FDI, and not vice versa. However, for the low-income countries’ panel, there was no evidence of causality in either direction. The study concludes that the FDI-led growth hypothesis does not apply to SADC countries.

Methodology applied in this study is a bivariate framework which is likely to suffer from the omission of variable bias (Odhiambo, 2008, 2011). Second, the Granger causality analysis employed in this only investigates the direction of causality and whether each variable can be used to explain another, but does not directly test for the mechanisms through which FDI leads to economic growth and economic growth leads to FDI.

Future studies may include a third variable such as domestic savings, exports, or financial development in a trivariate or multivariate panel causality model. A more complete analysis which seeks to explain the channels through which FDI impacts growth is suggested for future studies. Lastly, sector level analysis will help policy makers draft effective industrial policies, which can guide allocation of incentives.

The results of this study support the Growth-led FDI hypothesis, but not the FDI-led growth hypothesis. In other words, it is economic growth that drives FDI inflows into the SADC region and into Southern Africa, and not vice versa. This implies that the recent high economic growth rates that have been recorded in some of the SADC countries, especially the middle-income countries, have led to a massive inflow of FDI into this region.

At the regional level, SADC as a regional bloc has been actively pursuing policies and strategies aimed at attracting FDI into the region. Despite the important role of FDI in economic development, and the increase in FDI inflows into SADC countries in particular, there is a significant dearth of literature on the causal relationship between FDI and economic growth. The study used the recent panel-data analysis methods to examine the causal relationship between FDI and economic growth in SADC countries.

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.

This study investigates the patterns and determinants of Korea’s horizontal intra-industry trade by employing a dynamic panel data analysis. The main findings of the study are as follows. An increase in the market size of both trading partners is associated with a higher level of horizontal intra-industry trade. Dissimilar economic sizes and per capita incomes have a negative effect on trade in horizontally differentiated products. Geographical distance and trade imbalance serve as obstacles to horizontal intra-industry trade. These findings have policy implications for reconsidering the orientation of promoting trade. Inclusive economic growth in the region, taking advantage of neighboring nations and making efforts to reduce trade imbalances between trading economies, can accelerate further trade expansion through horizontal intra-industry trade.

The purpose of this paper is to investigate whether foreign direct investment (FDI) benefitted Thai workers in domestic firms.

By utilizing existing firm-level unbalanced panel data from the survey of the Office of Industrial Economics, Ministry of Industry, Thailand, between 2004 and 2013, this study applies dynamic panel data analysis, using the generalized method of moments proposed by Arellano and Bond (1991), to estimate the wage spillover from multinational enterprises (MNEs) to domestic firms in Thailand.

The study reveals that there is a positive wage spillover from the presence of MNEs in the industry to domestic firms. Furthermore, a wage spillover also exists in the low-technology industry, as well as in firms located in the Metropolitan and Northern regions. These findings confirmed that FDI offers a significant advantage in Thailand’s labor market.

This study is the empirical research to utilize existing firm-level unbalanced panel data in Thailand, applying dynamic panel data analysis to data from 2004 to 2013 to estimate the wage spillover from MNEs to domestic firms.

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 paper aims to find out the information content of performance measures from accounting and value-based measures that best explain the total shareholder return.

To achieve this aim, static and dynamic panel data regression analysis is applied to the sample of 56 Indian companies taken from the Nifty Midcap 100 Index, between 2012 and 2019.

It is found that accounting-based measures have more relative information content in predicting total shareholder return as compared to value-based measures. Economic value added (EVA) and cash value added (CVA) do not add to the information content provided by accounting-based measures. A combination of accounting-based measures and value-added intellectual coefficient (VAIC) adds marginally to the information content provided by accounting-based measures in explaining the total shareholder return. Dynamic panel regression analysis shows that return on assets (ROA), return on capital employed (ROCE), return on equity (ROE) and EVA have a significant impact on total shareholder return.

In this study, along with EVA, other measures from value-based measures, i.e. CVA are empirically tested to explain the total shareholder return. Intellectual capital efficiency computed by VAIC is also empirically tested along with accounting-based measures, EVA, CVA and market value added (MVA). To bring robustness to findings, data are tested by using dynamic panel regression analysis.

In this paper, the authors assess the responsiveness of OOP healthcare expenditure to macro-fiscal factors, as well as to tax-based, SHI, mixed systems and voluntary PHI financing. Although the relationship between OOP expenditure, macroeconomy, aggregate public and PHI financing is well documented in the existing empirical literature, little is known for the impact of several macro-fiscal drivers and the existing health financing arrangements associated with voluntary PHI on OOP expenditure.

The authors gather panel data by applying three official organizations’ databases. They elaborate static and dynamic panel data methodology to a dataset of 49 European and OECD countries from 2000 to 2015.

The authors’ findings do not indicate a considerable impact of GDP growth and general government debt as a share of GDP on OOP payments. Unemployment rate presents as a positive driver of OOP payments in all three compulsory national health systems post to the 2008 economic crisis. OOP payments are significantly influenced by countries’ fiscal capacity to increase general government expenditure to GDP in SHI and mixed health systems. Additionally, study findings present that government health financing, irrespective of the different health systems structure characteristics, and OOP healthcare payments follow different directions. Voluntary PHI financing considerably counteracts OOP payments only in tax-based health systems.

In the backdrop of a new economic crisis associated to the COVID-19 epidemic, health policy planners have to deal with the emerging unprecedented challenges in financing of health systems, especially for these economies that have to face the fiscal capacity constraints owing to the 2008 financial crisis and its severe recession.

To the best of authors’ knowledge, there is no empirical consensus on the effects of macro-fiscal parameters, different compulsory health systems financing associated with the parallel voluntary PHI institution funding on OOP expenditure, for the majority of European and OECD settings.