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Intellectual capital (IC) has been considered as a valuable asset in the wealth creation and sustainability of the company; however, limited and mixed results are found on its impact on firm financial performance and market value (MV). This paper aims to investigate the influence of IC toward MV and financial performance of publicly listed firms in Indonesia. In addition, this research also presents the comparison of the high and low level of knowledge industries regarding IC performance.

A balanced panel data of 127 firms from 12 industries in Indonesia during 2010 until 2017 was evaluated using dynamic panel regression and administering a well-developed Blundell–Bond instrument (dynamic panel data estimator) to account for endogeneity problem.

The results of this study showed that IC had a significant and positive impact on firm performance. Specifically, structural capital efficiency and capital employed (CE) efficiency have been contributed to the value creation of the company, after controlling for firm size and type of industry. Different to the theoretical expectation, this research found no significant relationship between IC and MV of the firm. However, when the sample was clustered into high-level and low-level knowledge industry, CE displayed positive and significant relationship in high-level industry.

This research contributes to IC research by having a larger sample of Indonesian firms from all industries except banks and financial institutions and using Modified Value Added Intellectual Capital measurement model. To address the endogeneity problem, dynamic panel regression using system generalized method of moment was applied.

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.

This study aims to explore the relationship between the growth threshold effect on renewable energy consumption (REC) in the major oil-producing countries in sub-Saharan Africa (SSA) over the period 1990–2018.

This article used a dynamic panel threshold regression model introduced by Hansen (1996, 1999 and 2000) threshold (TR) models. The procedure is achieved using 5,000 bootstrapping replications and the grid search to obtain the asymptotic distribution and p-values. For the long-run relationship among our variables, the author followed the process in Pesaran et al. (1999) pooled mean group (PMG) for heterogeneous panels. Furthermore, for the robustness of our empirical results due to the sensitivity of the results to outliers, the author used the approach by Cook (1979) distance measure. The author applied quantile (QR) regression to explore the distribution of dependent variables following Bassett and Koenker (1982) and Koenker and Bassett (1978) approaches.

The results from the threshold effect test and threshold regression revealed a significant single threshold effect of growth level on REC. Furthermore, the result from the PMG estimation showed the growth of the variable, energy intensity, consumer prices and CO₂ emissions play a significant role in REC in major oil-producing countries in SSA. The growth threshold estimation results indicated one significant threshold value of 1.013% at one period lagged of real growth. The outlier’s sensitivity detention greatly influenced our empirical results.

The article filled the literature gap by applying a combined measure that is robustness to detect outliers in the data, which none of the studies in the literature addresses hitherto. Further, the article extends the quantile regression to growth – REC literature.

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.

This research investigates the threshold effects of the interplay between finance, development and carbon emissions across 97 countries, including 50 low-income and 47 high-income countries, during the period from 1991 to 2019.

Employing various econometric modeling techniques such as dynamic linear regression, dynamic panel threshold regression and in/out of sample splitting, this study analyzes the data obtained from the World Bank's world development indicators.

The results indicate that low-income countries require a minimum financial development threshold of 0.354 to effectively reduce carbon emissions. Conversely, high-income countries require a higher financial development threshold of 0.662 to mitigate finance-induced carbon emissions. These findings validate the presence of a finance-led Environmental Kuznet Curve (EKC). Furthermore, the study highlights those high-income countries exhibit greater environmental concern compared to their low-income counterparts. Additionally, a minimum GDP per capita of US$ 10,067 is necessary to facilitate economic development and subsequently reduce carbon emissions. Once GDP per capita surpasses this threshold, a rise in economic development by a certain percentage could lead to a 0.96% reduction in carbon emissions across all income levels.

This study provides a novel contribution by estimating practical financial and economic thresholds essential for reducing carbon emissions within countries at varying levels of development.

Empirically, the purpose of this paper is to investigate policy variables that determine monetary policy and economic growth of some selected countries within the economic bloc of Southern Africa Development Community (SADC). The selected countries are Botswana, Democratic Republic of Congo, Lesotho, Madagascar, Malawi, Mozambique, Namibia, South Africa, Swaziland, Zambia and Zimbabwe.

Annual time series data for a panel of 11 Southern African countries spanning 1980–2015 were employed in the study. The major instrument of estimation is the dynamic regression panel model. In order to conform to econometric principles, robustness checks were carried out on the variables of interest so as to avoid spurious results. An estimation of impulse response and variance decomposition analyses were to complement the approach to the study.

The result of the long-run dynamic panel regression reveals that GDP growth rate, inflation rate, exchange rate, money supply and oil and commodity prices do have profound impact on monetary policy within SADC. It was further revealed from the study that commodity price shock is the major exogenous determinant of monetary policy dynamics and the effect is transmitted via exchange rate channel to macroeconomics of the region; with inflation rate and money supply playing a major role in the transmission mechanism as it affects the economies of the countries in this region.

The policy implication is that inflation is seen as a major challenge to the countries under review. Among other things, a hybrid of inflation and monetary targeting should be adopted to complement each other as policy combination within the region.

The study accounts for the determinants of monetary policy vis-à-vis growth potentials of some selected countries in SADC, using a combination of dynamic regression panel approach and SVAR elements.

The purpose of this paper is to examine the relationship between the quality of different dimensions of institutional and economic growth in a panel of 15 member ECOWAS.

The study adopts Driscoll and Kraay′s nonparametric covariance matrix estimator, and the spatial error model to account for cross-section dependency, cross-country heterogeneity and spatial dependence inherent in empirical modelling, which has largely been ignored in previous studies. This is because, the likelihood that corruption and human capital cluster in space is very high because factors that affect these phenomena disperse across borders. Similarly, to test the threshold effect, the study adopts the more refined and more appropriate dynamic panel data which models a nonlinear asymmetric dynamics and cross-sectional heterogeneity, simultaneously, in a dynamic threshold panel data framework.

The empirical evidence supports findings by previous researchers that better-quality political and economic institutions can have positive effects on economic growth. Similarly, our results support a nonlinear relationship between political institutions and economic institution, confirming the “hierarchy of institution hypothesis” in ECOWAS. Specifically, the findings show that economic institutions will only have the desired economic outcome in ECOWAS, only when political institution is above a certain threshold.

Unlike previous studies which assume cross-sectional and spatial independence, the authors account for cross-section dependency and cross-country heterogeneity inherent in empirical modelling.

The peer review history for this article is available at: https://publons.com/publon/10.1108/IJSE-10-2019-0630

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.

The purpose of this paper is to examine the relationship between urbanization and economic growth in ASEAN countries for the period 1993-2014.

The Granger causality test and the regression estimation method with static and dynamic panel data (FE, RE, Driscoll and Kraay, D-GMM and PMG) were used. The sample includes seven ASEAN countries: Brunei, Cambodia, Indonesia, Malaysia, Philippines, Thailand and Vietnam.

The results show that at least a causal relationship exists between urbanization and economic growth and urbanization positively impacts economic growth. However, the relationship between urbanization and economic growth is non-linear. The urbanization reaches a threshold after which it may impede the economic growth. The estimated threshold is 69.99 percent for the static model and 67.94 percent for the dynamic model.

The evidence from this study suggests that there is a non-linear relationship between urbanization and the economic growth. Urbanization has the potential to accelerate the economic growth, and this potential will depend on the establishment of favorable institutions and investments in appropriate public infrastructure.

The decision on the model of urbanization needs to be based on social and environmental considerations as well as market-based economic efficiency. The quality of urbanization manifests in the way that people and businesses perceive when they come to cities and their position in the labor market, urban housing, niche commodity markets, supply chain, collaborative network and physical space for the operation of the business. Most ASEAN countries have not yet reached a high level of urbanization, despite having a number of policies for promoting urbanization to contribute to the economic growth. However, policymakers should find ways to facilitate the development of urbanization that contributes to economic growth, employment growth, environmental sustainability, rather than the pursuit of speeding up the process of urbanization.

Between urbanization and economic growth at least a causal relationship exists. Urbanization positively impacts economic growth. However, the relationship between urbanization and economic growth is non-linear. The urbanization reaches a threshold after which it may impede the economic growth. The estimated threshold is 69.99 percent for the static model and 67.94 percent for the dynamic model.