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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.

This paper aims to test the significance of unobservable firm-specific effects on a capital structure model.

The paper employs the restricted least squares method to test the significance of unobservable firm-specific effects in a fixed effects model that includes unobservable effects against a pooled ordinary least squares model that excludes unobservable effects.

The empirical findings indicate that models that include unobservable firm-specific effects are correctly specified.

The limitation of this study comes from lack of data to measure unobservable effects such as managerial ability or managerial skills. Future research can develop index measures of managerial ability or managerial skills and borrow from management theory to explain the connection between managerial ability or managerial skills and firms' capital structure.

The findings imply that a capital structure model that excludes firm-specific effects could be mis-specified because such a model does not control for unobservable firm-specific factors such as managerial ability or managerial skills which have significant effects on firms' capital structure decisions.

The findings are important because the paper applies the restricted least squares method to test the significance of unobservable firm-specific effects. This technique has not been applied previously. The paper contributes to capital structure research in the fast growing South Africa.

The New Zealand (NZ) dairy industry faces the challenge of increasing productivity and dealing with public concerns over nutrient pollution. The effective policy needs to address regional differences in productivity and fertilizer use. The purpose of this paper is to investigate how spatial effects influence the relationship between dairy yields and intensive farming practices across regions in NZ.

This paper employs spatial panel data models to establish whether unobserved spatial effects exist in the relationship between dairy yields and nutrient inputs regionally and nationally using 2002, 2007 and 2012 data from Statistics NZ and DairyNZ.

The results show positive spatial spillovers for most intensive inputs. The high level of effluent use and estimated negative yield response to nitrogen suggests that an opportunity exists for greater use of effluent as a substitute for nitrogenous fertilizer. Substitution has the potential to reduce dependence on fertilizer and contribute to a reduction in the nutrient pollution.

This paper is the first empirical application of spatial econometric methods to examine the spatial relevance of dairy yields and intensive farming in NZ. In particular, the spatial panel data model accounts for cross-sectional dependence and controls for heterogeneity. The results contribute to an understanding of how farmers can improve their management of intensive inputs and contribute to the formation of regional environmental policy that recognizes regional heterogeneity.

This paper aims to analyse factors that influence child schooling outcomes in India, specifically the role of gender.

This paper uses data from the nationally representative Indian National Family Health Surveys 1995-1996 and 2005-2006 and estimates Heckman sample selection, cluster fixed-effects and household fixed-effects econometric models. The dependent variables are the child’s enrolment status and conditional on enrolment child’s years of schooling.

This analysis finds statistically significant evidence of male advantage both in schooling enrolment as well as years of schooling. However, using a cluster fixed-effects model, our analysis finds that within a village, conditional on being enrolled, girls spend more years in school relative to boys. Other results show that parental schooling has a positive and statistically significant impact on child schooling. There is statistically significant wealth effect, community effect and regional disparities between states in India.

The large sample size and the range of questions available in this data set, allows us to explore the influence of individual, household and village level social, economic and cultural factors on child schooling. The role of gender on child schooling within a village, intrahousehold resource allocation for schooling and regional gender differences in schooling are important issues in India, where education outcomes remain poor for large segments of the population.

The study aims to understand how published accounting information relates to share prices in a developed market in Asia, outside Japan. More specifically, the study aims to extend the international literature in market‐based accounting research by examining empirical evidence on relationships between share prices and the two summary accounting variables of equity book value and earnings for firms listed on the stock exchange in Malaysia.

As one of the main purposes of financial statements is to provide relevant information for investors, relationships between share prices and accounting variables have been widely researched. Early studies focus mainly on earnings, but attention has turned in recent years to valuation models that include the book value of the equity. Many of these studies cite the residual income model as their theoretical base and, with the growing emphasis on shareholder value, residual income measures are more commonly used in the business community to track financial performance. Given such trends, the purpose of this paper is to review the theoretical background of the residual income model and discuss results of empirical studies that use it.

The study seeks an understanding of how published accounting information relates to share prices in the developed market in Asia, outside Japan. More specifically, the study aims to extend the international literature in market based accounting research by examining empirical evidence on relationships between share prices and the two summary accounting variables of equity book value and earnings for firms listed on the stock exchange in Malaysia.

The findings imply that, the two accounting variables summarising the balance sheet and the income statement, respectively, are significant factors in the valuation process, and that managers are justified in using the accounting system as a primary source of information for monitoring financial performance.

These findings should be of interest to other researchers, and to managers and investors who currently use or are planning to use residual income to monitor business performance.

The purpose of this paper is to investigate the impact of demand uncertainty on inventory turnover performance through empirical modeling. In particular the authors use the inaccuracy of quarterly sales forecasts as a proxy for demand uncertainty and study its impact on firm-level inventory turnover ratios.

The authors use regression analysis to study the effect of various measures on inventory performance. The authors use a sample financial data for 304 publicly listed US retail firms for the 25-year period from 1985 to 2009.

Controlling for the effects of retail segments and year, it is found that inventory turnover is negatively correlated with mean absolute percentage error of quarterly sales forecasts and gross margin and positively correlated with capital intensity and sales surprise. These four variables explain 73.7 percent of the variation across firms and over time and 93.4 percent of the within-firm variation in the data.

In addition to conducting an empirical investigation for the sources of variation in a major operational metric, the results in this study can also be used to benchmark a retailer’s inventory performance against its competitors.

The authors develop a new proxy to measure the demand uncertainty that a firm faces and show that this measure may help to explain the variation in inventory performance.

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.

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.

While the relationship between military expenditure and economic growth during the Cold War period is well-researched, relatively less is known on the issue for the post-Cold War era. Equally how the relationship varies with respect to exposure to conflict is also not fully examined. Therefore, the purpose of this paper is to investigate the causal impact of military expenditure on growth in the presence of internal and external threats for the period 1990-2013 using data from 70 developing countries.

The main estimates are based on the generalized method of moments (GMM) regression model. But for comparison purposes, the authors also report estimates using fixed and random effects as well as pooled cross-section regressions. The regression specification accounts for non-linear effect of military expenditure allowing for interaction with conflict variable (where distinction is made between external and internal conflict).

The analysis indicates that methods as well as model specification matter in studying the effect of military spending on growth. Full sample estimates based on GMM, fixed, and random effects models suggest a negative and statistically significant effect of military expenditure. However, fixed effects estimate becomes insignificant for low-income countries. The effect of military spending is also insignificant in the cross-sectional OLS model if conflict is not considered. When the regression model additionally controls for conflict, the effect of military spending conditional upon (internal) conflict exposure is significant and positive. No such effect is present conditional upon external threat.

One important limitation of the analysis is the small sample size – the authors had to restrict analysis to 70 low and middle-income countries for which the authors could construct post-Cold War panel data on military expenditure along with information on armed conflict exposure (the later from the Uppsala Conflict Data Program, 2015).

To the best of the author’s knowledge, this is the first paper to examine the joint impact of military expenditure and conflict on economic growth in post-Cold War period in a sample of developing countries. Moreover, an attempt is made to review and revisit the large Cold War literature where studies vary considerably in terms findings. A key reason for this is the somewhat ad hoc choice of econometric methods – most rely on cross-section data and rarely conduct sensitivity analysis. The authors instead rely on panel data estimates but also report results based on naïve models for comparison purposes.