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This chapter extends the work of Baltagi, Bresson, Chaturvedi, and Lacroix (2018) to the popular dynamic panel data model. The authors investigate the robustness of Bayesian panel data models to possible misspecification of the prior distribution. The proposed robust Bayesian approach departs from the standard Bayesian framework in two ways. First, the authors consider the ε-contamination class of prior distributions for the model parameters as well as for the individual effects. Second, both the base elicited priors and the ε-contamination priors use Zellner’s (1986) g-priors for the variance–covariance matrices. The authors propose a general “toolbox” for a wide range of specifications which includes the dynamic panel model with random effects, with cross-correlated effects à la Chamberlain, for the Hausman–Taylor world and for dynamic panel data models with homogeneous/heterogeneous slopes and cross-sectional dependence. Using a Monte Carlo simulation study, the authors compare the finite sample properties of the proposed estimator to those of standard classical estimators. The chapter contributes to the dynamic panel data literature by proposing a general robust Bayesian framework which encompasses the conventional frequentist specifications and their associated estimation methods as special cases.

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 review recent developments for analysis of deteriorating stiffened panels subjected to static and explosive forces.

The first part reviews numerical procedures developed for stiffened panels subjected to explosive forces. The structural idealization, the theoretical basis, and the merits of these methods are discussed. The second part reviews the probabilistic procedures developed for analysis of deteriorating stiffened panels. The third part reviews recent work developed in several finite element modelling philosophies for analysis of stiffened panels. The influence of various parameters affecting the structural performance, such as geometric and material imperfections, corrosion, residual stresses, etc. is discussed. The fourth part reviews hybrid procedures developed to provide approximate solutions for the designers. Numerical procedure is presented using combination of energy formulations and mathematical programming techniques to model the interaction between the box girder components.

Localized damage largely affects the performance of stiffened panels and must be accounted for in the design phase. Little emphasis was given in the published literature to developing simplified analytical models that can be used in practice to compute the residual strength of the stiffened panels under these types of loadings. Furthermore, analytical expressions are required to compute the reduction in the stiffness induced due to the structural or material defects. These expressions must be dependent on the type of damage. It must be noted that some of this damages is localized in nature and must be accounted for by using specialized functions to assess the structural defect accurately. Research work is required in this direction.

The paper provides useful resource material for the engineers in practice regarding recent techniques developed to assess damaged stiffened panels subject to static and explosive loadings. The paper reviews work developed over the past 20 years that can be used as a baseline for future developments.

Very limited literature dealt with the ultimate strength of damaged stiffened structure under static and explosive forces. No guidelines are available in current design codes to assess the damage in predicting the strength of deteriorating stiffened panels.

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 re-examine the relationship between a country’s aggregate stock market and general economic development for 14 emerging economies for the period from 1995 to 2014.

The methodological approach of the paper is multifold. First, the authors use cointegration analysis to determine the simple dynamics among the variables. Second, the authors utilize vector autoregression analysis to study the dynamics among the variables for the 14 countries. Third, the authors employ panel analysis to determine common variations among the variables and across countries.

When examining the linkage between the stock market and economic development, proxied by gross domestic product growth or with gross fixed capital formation growth, the authors did not find a meaningful relationship between them. However, when the authors included additional control variables strong, dynamic interactions between the two magnitudes surfaced. Specifically, it was found that the stock market is positively and robustly correlated with contemporaneous and future real economic development and, thus, it directly contributed to a country’s economic development either through the production of goods and services or the accumulation of real capital. Thus, it can be inferred that the stock market alone is not capable of boosting economic development in these countries unless being part of a comprehensive financial system (which includes banks) as well as investment in real capital.

The policy implications are clear. Government authorities must recognize that the stock market alone is not a driver of economic development and that a sound, efficient financial system (which includes banks) must be present in order to contribute and foster economic development.

The study is original in the sense that it examines various financial and economic variables to determine the degree of (or dynamic interactions among) the stock market and the real economy for each and all emerging markets in the sample.

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.

The purpose of the study is to investigate the impact of tax planning activities on the firm value of FTSE 100 firms. We employ static and dynamic panel regression analyses on a sample of 70 companies drawn from the UK FTSE 100 over a five-year period (2006–2010). Empirical evidence suggests that tax planning activity as measured by the proxies based on reported accounting information has a negative impact on firm value. Moreover, the results from the Generalized Methods of Moments (GMM) models suggest significant dynamics in firm value, i.e., the current firm value is positively affected by the past firm value. The findings imply the need for a full review of the adequacy and relevance of tax accounting disclosure and therefore have policy implications for accounting standard setters.