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This study aims to examine the nexuses between economic growth, trade openness, renewable energy consumption and environmental degradation among organization of petroleum exporting countries (OPEC) members over the period 1990–2019.

The empirical strategy for the study includes dynamic heterogeneous panel pooled mean group (PMG), mean group (MG) estimators and dynamic panel threshold regression (TR) analysis. For clarity, PMG and MG are used to explore the long-run relationship between the variables, whereas TR is used to uncover the actionable and complementary policy thresholds in the nexuses between green growth and environmental degradation.

The empirical evidence is based on the significant estimates from PMG and TR. First, using PMG, the study finding revealed a long-run relationship between economic growth and environmental degradation via the PMG estimator. Second, using TR, the study revealed an actionable threshold for carbon dioxide emissions (CO₂) metrics tons per capita (mtpc) not beyond a critical mass of 4.88mtpc, and the complementary policy threshold of 85% of the share of trade to gross domestic product, respectively.

The policy relevance of the thresholds is apparent to policymakers in the cartel and for policy formulation. The policy implication of this study is straightforward.

The novelty of this study stalk in the extant literature on providing policymakers with an actionable threshold for CO₂ emissions with the corresponding complementary threshold for trade policies in the nexuses between green growth and the environment.

Shadow banking has been evolving rapidly in China, with banks actively using wealth management products (WMPs) to evade regulatory restrictions. These products are the largest constituent of China's shadow banking sector. A large number of these products are off-balance-sheet and considered a substitute for bank deposits. China's banking sector, especially the small and medium-sized banks (SMBs), uses these products to avoid regulatory restrictions and sustainability risk in the deposit market.

This study empirically examined how banks in China, specifically SMBs, utilize these products on a short and long-run basis to manage and control their deposit levels. This study utilized a quarterly panel dataset from 2010 to 2019 for the top 30 Chinese banks, by first implementing a Panel ARDL-PMG model. For cross-sectional dependence, this study further executed a cross-sectional augmented autoregressive distributive lag model (CS-ARDL).

Under regulations avoidance theory, the findings revealed that WMPs and deposits have a stable long-run substitute relationship. Furthermore, the WMP–Deposit substitute relationship was only significant and consistent for SMBs, but not for large four banks. The findings further revealed that the WMP–Deposit substitute relationship existed, even after the removal of the deposit rate limit imposed by the People's Bank of China (PBOC) to control the deposit rates.

The individual bank-issued WMPs' amount data is not available in any database. Therefore, this study utilized the number of WMPs as a proxy for China's banking sector's exposure to the wealth management business.

This research helps policymakers to understand the Deposit–WMP relationship from the off-balance-sheet perspective. During the various stages of interest rate liberalization, banks were given more control to establish their deposit and loan interest rates. However, the deposit rates are still way below the WMP returns, making WMPs more competitive. This research suggests that policymakers should formulate a more balanced strategy regarding deposit rates and WMPs returns.

This study contributes to the existing literature on China's shadow banking by concentrating on the WMPs. This research represents one of the few studies that analyze regulatory arbitrage in terms of the WMP–Deposit relationship. Moreover, the implementation of CS-ARDL panel data models and multiple data sources makes this study's findings more reliable and significant.

This article investigates the dynamic implication of healthcare expenditure on economic growth in the selected ten Sub-Saharan African countries over the period 2000–2018.

The study methodology included dynamic heterogenous panel, using mean group and pooled mean group estimators. The investigation of the healthcare expenditure and economic growth nexus was achieved while controlling the effects of investment, savings, labor force and life expectancy via interaction terms.

The results from linear healthcare expenditure have a significant positive impact on economic growth, while the nonlinear estimates through the interaction terms between healthcare expenditure and investment have a negative statistically significant impact on growth. The marginal effect of healthcare expenditure evaluated at the minimum and maximum level of investment is positive, suggesting the impact of health expenditure on growth does not vary with the level of investments. This result responds to the primary objective of the article.

In policy terms, the impact of investment on healthcare is essential to addressing future health crises. The impact of coronavirus disease 2019 (COVID-19) can never be separated from the shortages or low prioritization of health against other sectors of the economy. The article also provides an insight to policymakers on the demand for policy reform that will boost and make the health sector attractive to both domestic and foreign direct investment.

Given the vulnerability of SSA to the health crisis, there are limited studies to examine this phenomenon and first to address the needed investment priorities to the health sector infrastructure in SSA.

This paper aims to use the newly developed panel data cointegration analysis and the dynamic time series modeling approach to examine the linkages between financial structure (market‐based vs bank‐based) and economic growth in African economies.

The research investigates the dynamic relationship between financial structure and economic growth in a panel of a group of seven African developing countries over the period of 1986‐2007. The paper uses various indicators/measures of financial structure and financial system, and employs the traditional time‐series analysis for causality as well as the newly developed panel unit root and cointegration techniques and estimated finance‐growth relationship using FMOLS for heterogeneous panel.

From the dynamic heterogeneous panel approach, the paper firstly finds that market‐based financial system is important for explaining output growth through enhancing efficiency and productivity. Second, the authors' empirical evidence supports the view that higher levels of banking system development are positively associated with capital accumulation growth and lead to faster rates of economic growth.

Panel cointegration, group mean panel FMOLS and country‐by‐country time series investigations indicate that the market‐based financial system is important for explaining output growth through enhancing efficiency and productivity, whereas the development of banking system is significantly associated with capital accumulation growth. Further results from the time‐series approach show evidence of unidirectional causality running from market‐oriented as well as bank‐oriented financial systems to economic growth.

This paper develops a cross-sectionally augmented distributed lag (CS-DL) approach to the estimation of long-run effects in large dynamic heterogeneous panel data models with cross-sectionally dependent errors. The asymptotic distribution of the CS-DL estimator is derived under coefficient heterogeneity in the case where the time dimension (T ) and the cross-section dimension (N ) are both large. The CS-DL approach is compared with more standard panel data estimators that are based on autoregressive distributed lag (ARDL) specifications. It is shown that unlike the ARDL-type estimator, the CS-DL estimator is robust to misspecification of dynamics and error serial correlation. The theoretical results are illustrated with small sample evidence obtained by means of Monte Carlo simulations, which suggest that the performance of the CS-DL approach is often superior to the alternative panel ARDL estimates, particularly when T is not too large and lies in the range of 30–50.

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 discussion on international migration has become a significant part of globalization and a topical issue in international relations, especially in developing economies which mostly relies on migrant remittances. The purpose of the study is to examine whether financial market development (equity market development and banking sector development) really drives migrant remittance flow in Sub-Saharan Africa (SSA).

The study employs the dynamic heterogeneous panel data approach-the pool mean group (PMG) and the mean group (MG) techniques in analyzing the model based on data obtained from 27 SSA countries covering the period 2000–2020.

The findings of the study revealed that financial market development (equity market development and banking sector development) is a key driver of migrant remittances flows in the SSA region. In addition, the study revealed that the following macroeconomic variables such as real interest rate, unemployment rate, global growth, emigration, and economic growth are also determinants of migrant remittances flows in the SSA region.

The reviewed empirical literature revealed that several studies documents that the macroeconomic determinants of migrant remittances include inflation, GDP, interest rate, exchange rate, population growth, financial sector development and unemployment rate. Most of these studies fail to capture both equity market development and robust banking sector development (financial market development) as critical drivers of migrant remittances flow in SSA. Also, this study uses a robust measure of equity market development and banking sector development, unlike previous studies.

The peer review history for this article is available at: https://publons.com/publon/10.1108/IJSE-05-2023-0361

The purpose of this paper is to investigate the optimal inflation targets for an appropriate exchange rate policy in 15 major oil exporting countries in Sub-Saharan African (SSA).

Dynamic heterogeneous panel threshold techniques are used via threshold-effect test and threshold regression. This procedure is achieved through a grid search and bootstrapping replications method to stimulate the asymptotic distribution of the likelihood ratio test of the null hypothesis on no-threshold as against the alternative hypothesis. The p-values validate the threshold estimates.

Findings revealed that the optimal inflation target has a turning point and its impact on the real exchange rate is up to a threshold level of 14.47 per cent. Furthermore, the inflation rate above the threshold level overwhelmingly revealed its effect on real exchange regimes.

It would have been a good idea to investigate optimal inflation targets for all African countries but due to inadequate data the selection criteria was narrowed to oil-exporting countries in Sub-Saharan Africa.

Inflation targeting beyond the threshold level would have serious implications on the monetary policy.

To the best of the knowledge, this is the first study to look at optimal inflation targets for 15 major oil exporting countries in general and SSA countries in particular. The findings provide a critical analysis of an inflation regime for a typical oil-producing country that oil exports being their source of revenue.

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.