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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.

Renewable energy (RE) is an important component to the complex portfolio of technologies that have the potential to reduce CO₂ emissions and to enhance the security of energy supplies. Despite RE's potential to reduce CO₂ emissions, the expenditure on renewable energy research, development, and demonstration (RERD&D) as a percentage of total government energy research, development, and demonstration (ERD&D) investment remains low in developed countries. The declining ERD&D expenditure prompted this research to explore the relationship between CO₂ emissions per capita and RERD&D as opposed to ERD&D.

An econometric analysis of annual CO₂ emissions per capita during the period 1990‐2004 for the 15 pre‐2004 European Union (EU15) countries was carried out. It was hypothesized that the impact of RERD&D expenditure on the reduction of CO₂ emissions would be higher than that of ERD&D expenditure, primarily due to several RE technologies being close to carbon neutral. Country‐level gross domestic product per capita and an index of the ratio between industry consumption and industrial production were introduced in the analysis as proxies to control for activities that generate CO₂ emissions. A number of panel data econometric models that are able to take into account both country‐ and time‐specific unobserved effects were explored.

It was found that random effect models were more appropriate to examine the study hypothesis. The results suggest that expenditure on RERD&D is statistically significant and negatively associated with CO₂ emissions per capita in all models, whereas expenditure on ERD&D is statistically insignificant (ceteris paribus).

The findings of this paper provide useful insight into the effectiveness of RERD&D investment in reducing CO₂ emissions and are of value in the development of policies for targeted research, development, and demonstration investment to mitigate the impacts of climate change.

This paper aims to discuss multilevel modeling for longitudinal data, clarifying the circumstances in which they can be used.

The authors estimate three-level models with repeated measures, offering conditions for their correct interpretation.

From the concepts and techniques presented, the authors can propose models, in which it is possible to identify the fixed and random effects on the dependent variable, understand the variance decomposition of multilevel random effects, test alternative covariance structures to account for heteroskedasticity and calculate and interpret the intraclass correlations of each analysis level.

Understanding how nested data structures and data with repeated measures work enables researchers and managers to define several types of constructs from which multilevel models can be used.

The purpose of this paper is to investigate the impact of firm-specific (i.e. firm size, profitability, leverage, dividend, growth opportunities, management quality and firm age) and country-specific (i.e., gross domestic product [GDP] growth) variables on compensation/remuneration offered to chief executive officers (CEOs) working in different industries of Pakistan.

Panel data techniques, namely, pooled ordinary least squares, fixed effects and random effects methods are used to estimate the results. Moreover, Hausman test is used to choose which estimation method, either fixed effects or random effects, is better to explain the results.

Firm size, profitability, leverage, growth opportunities and age are some important firm-specific factors that have mixed (i.e. positive/negative) impact on CEO compensation in different industries. Variations in results are due to industry dynamics. However, it is important to mention that three key variables, namely, dividend, management quality and GDP growth have shown consistent positive impact on CEO compensation in most of the industries. In sum, results show that firm-specific and country-specific variables have material effects on CEO compensation. Moreover, results are found consistent with the predictions of agency theory and human capital theory.

The authors are sure that findings of this study provide some support to the board of directors to determine the pay slice for CEOs. Moreover, findings provide support to the regulatory authorities in formulating mechanisms to determine the compensation package for CEOs working in different industries and to improve the Code of Corporate Governance.

To the best of the authors’ knowledge, no empirical study in Pakistan has yet estimated the effects of firm-specific and country-specific variables on compensation offered to CEOs working in different industries. Thus, industry-wise analysis provides some new insights to the decision-makers and lays some foundation upon which a more detail analysis could be based.

The objective of the study is to analyse the impact of research and development investment on the firm performance of “small” scale firms vis-a-vis “medium”-scale firms.

The dataset comprised of a balanced panel of 486 research and development conducting Indian manufacturing small and medium enterprises, constructed for the period of 2006–2017. Fixed Effects, Random Effects Model and Hausmann test were used to analyse the determinants of firm performance in manufacturing small and medium enterprises in India.

It was found that from firms’ research and development (R&D) investments in terms of performance could be attained if simultaneously internationalisation and higher capital intensity could be achieved.

Managers could pay specific attention to the antecedents of firm performance and calibrate their R&D investment, internationalisation efforts and capital intensity simultaneously to achieve higher growth and productivity. For policymakers, the results provide an insight into how the firms in both categories could be differently incentivised, such that resources are better utilised.

The study analysed the determinants of firm performance in small and medium-sized firms at a disaggregate level as well as at a sectoral level using fixed effects, random effects and lagged effects to arrive at novel results, which have important implications for their competitiveness.

The purpose of this paper is multifold. First, it is to investigate the relationship between social network sites (SNSs) usage and youth's school absenteeism. Second, it is to identify causal relationship between SNSs usage and absenteeism. Third, it is to explore whether SNSs usage causally affects youth's study–work choice after leaving high school. In addition to SNSs usage in general, abnormal SNSs usage is further discussed.

The Longitudinal Study of Australian Children (LSAC) data are utilised. Lagged variable analysis is used to alleviate reverse causality. Instrumental variable approach and the Lewbel method are used to identify causality. Random effects panel data approach (without and with IVs) is additionally applied to increase efficiency and account for individual-specific effects. Random effects approach allowing for within and between effects is applied, enabling us to control for fixed effects. The primary instrument is a dummy indicating whether a youth more often communicates with close friend electronically or face-to-face.

Using SNSs leads to significantly higher probability of a teenager being late for school, skipping class and having trouble not following school rules. The effect is more consistent regarding abnormal SNSs usage, compared to SNSs usage in general. Additionally, SNSs usage decreases the probability of a youth studying after 18 years old, even after controlling for absenteeism.

The findings in this paper highlight the importance of preventing youth (e.g. via enabling children-safe mode or setting up maximum daily access time) from overusing SNSs.

With the transition to hybrid (mixing remote and face-to-face) learning during and after COVID-19, online interactions are becoming inevitable in students' learning. The findings in this paper indicate that usage, especially abnormal usage, of SNSs increases the probability of absenteeism call for attention from stakeholders including teachers, parents and youth themselves.

This paper provides the first causal and longitudinal evidence linking SNSs usage to absenteeism and youth labor outcomes.

The purpose of this paper is to examine the impact of the size of government and public debt on real economic growth, for a panel of 175 countries around the world.

The paper utilizes the fixed‐effects and random‐effects techniques to estimate the panel regressions.

The results indicate that both the size of government and the extent of government indebtedness have negative effects on economic growth.

The findings suggest that the authorities ought to take the necessary steps to curtail excessive government spending and public debts, in order to promote economic growth.

The contribution of the paper is its application of the fixed‐ and random‐effects techniques in modeling the relation of real economic growth to the size of government and public debt, for a panel of 175 countries around the world.

The purpose of this study is to verify all factors that promote renewable energy (RE) consumption. Past studies have shown that financial development (FD) and economic growth (EG) are the major drivers toward RE development, while oil prices had mixed outcomes in different regions by different studies.

Global warming effects have been the major reason of the transition by nations from fossil fuel use to RE sources that are considered as friendly to the environment. This research uses the fixed effects and random effects techniques, to ascertain the factors which impact RE development. The generalized linear model is also used to check the robustness of the Fixed Effects and Random Effects models’ results, while the Kao, Pedroni and Westerlund tests are used to check cointegration in the specified model.

The major findings of this study show the importance of EG and FD in promoting RE development. Oil prices, inflation rate and public sector credit present a negative effect on RE development, while foreign direct investment does not significantly impact RE development.

This research recommends the use of FD in promoting RE sources, as well as the stabilization of oil prices and consumer prices.

This research is important because it specifies the three proxies of FD, together with foreign direct investment inflation rate, EG and oil prices, in modeling RE. By investigating the impact of oil prices on RE in the emerging seven economies, this research becomes one of the few studies done in this region, as per the authors’ knowhow.

The purpose of this paper is to theorize the heightened exposure to information via the internet can lead citizens to be more critical about political conditions in their countries because using social media increases the likelihood of being exposed to dissident information. Further, the authors argue that the degree to which information is restricted, or internet access is limited, across countries can decrease this effect simply because the likelihood of exposure to a dissident flow is diminished.

The authors used survey data from the 2010 Latino Barometer to estimate a series of multilevel models to test whether citizens’ attitudes about the political conditions and about democracy in their respective countries worsen, and whether this effect is stronger in countries with higher internet freedom.

The results confirm that social media use has a negative influence on citizens’ attitudes about their national political conditions. In addition, respondents from those countries with more internet freedom tended to have more positive attitudes about their democracy and political conditions, generally. However, as a result of more internet freedom, the negative effects of internet and social media use on these attitudes was more pronounced in countries with more internet freedom.

These results suggest that the flow of information via the internet has substantial effect on how people feel about their government. This could be consequential for political stability, particularly in countries the conditions are not favorable. That said, these results also suggest that governments can actively decrease the odds of this dissidence building by controlling the flow of information.

Foreign Direct Investment (FDI) is an important means of boosting the agricultural sectors of developing economies. The first necessary step to formulate effective public policies to encourage agricultural FDI inflow to a host country is to develop a comprehensive understanding of the main determinants of FDI inflow to the agricultural sector, which is the main objective of the present study.

In view of this, we take a comprehensive approach to exploring the macroeconomic and institutional determinants of FDI inflow to the agricultural sector by examining a large panel data set on agricultural FDI inflows of 37 countries, investigating both groups of developed and developing countries, incorporating a large list of potentially relevant macroeconomic and institutional variables, and applying panel-data econometric models and estimation structures, including pooled, fixed-effects and random-effects regression models.

The general pattern of our findings implies that the degree of openness of an economy has a negative effect on FDI inflows to agricultural sectors, suggesting that the higher the degree of openness in an economy, the lower the level of agricultural protection against foreign trade and imports, and thus the less incentive for FDI to inflow to the agricultural sector of the economy. Additionally, our results show that economic growth (as an indicator of the rate of market-size growth in the host economy) and per-capita real GDP (as an indicator of the standard of living in the host country) are both positively related to FDI inflows to agricultural sectors. Our other results suggest that agricultural FDI tends to flow more to developing countries in general and more to those with higher standards of living and income levels in particular.

FDI inflow has not received much attention with respect to the identification of its main determinants in the context of agricultural sectors. Additionally, there are very few panel-data studies on the determinants of FDI, and even more surprisingly, there are no such studies on the main determinants of FDI inflow to the agricultural sector. We have taken a comprehensive approach by studying FDI inflow variations across countries as well as over time.