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The authors provide a way to represent spatial and temporal equilibria in terms of error correction models in a panel setting. This requires potentially two different processes for spatial or network dynamics, both of which can be expressed in terms of spatial weights matrices. The first captures strong cross-sectional dependence, so that a spatial difference, suitably defined, is weakly cross-section dependent (granular) but can be non-stationary. The second is a conventional weights matrix that captures short-run spatio-temporal dynamics as stationary and granular processes. In large samples, cross-section averages serve the first purpose and the authors propose the mean group, common correlated effects estimator together with multiple testing of cross-correlations to provide the short-run spatial weights. The authors apply this model to the 324 local authorities of England, and show that our approach is useful for modeling weak and strong cross-section dependence, together with partial adjustments to two long-run equilibrium relationships and short-run spatio-temporal dynamics. This exercise provides new insights on the (spatial) long-run relationship between house prices and income in the UK.

This paper aims to investigate the effects of financial access, financial depth, financial efficiency and financial stability pillars on income inequality and poverty among a panel of sub-Saharan African (SSA) countries.

This paper captures cross-sectional dependence among the income groups through the dynamic common correlated effect approach for a data set of 28 selected SSA countries from 2000 to 2017.

This study reveals that the financial development pillars exert positive and significant impacts on income inequality across the income groups. The results show that the effects of the financial development metrics on poverty are different across the income groups. The results also indicate that the pillars improve poverty reduction for low- and lower-middle-income countries. However, there is a minimal effect on poverty reduction in upper-middle-income countries. The differences among these income categories suggest the need for policymakers to account for income levels when prescribing policies that could engender financial development and poverty reduction in the region.

This paper examines the effects of financial development on both income inequality and poverty by using the newly developed World Bank financial development strategic metrics. It captures cross-sectional dependence in the full sample of selected SSA countries and their income categories.

This study examines the impact of trade openness, human capital, public expenditure and institutional performance on unemployment in various income groups of Organization of Islamic Cooperation (OIC) countries.

Traditional panel data methodologies neglect the issue of cross-sectional dependence and provide ambiguous outcomes. A novel approach, “dynamic common correlated effects (DCCE)”, is utilized in this study to tackle with aforementioned issue. Pooled mean group (PMG) estimation is also applied to verify the robustness of the findings.

The long-run estimates show that trade openness has a significant and negative relationship with the unemployment rate in overall and lower-income OIC economies and a positive correlation with unemployment in higher-income OIC countries. Public expenditure is negatively and significantly correlated with unemployment in higher-income and overall OIC economies. Moreover, human capital reduces unemployment in higher-income and overall OIC countries while increases unemployment in lower-income OIC economies.

The research tends to endorse the argument for continuous trade openness policy along with efficient use of public expenditure and improved institutional performance to reduce unemployment in OIC countries.

The DCCE approach in this research considers heterogeneity and cross-sectional dependence between cross-sectional units and thus gives robust outcomes.

This chapter advances a panel vector autoregressive/vector error correction model (PVAR/PVECM) framework for purposes of examining the sources and determinants of cross-country variations in macroeconomic performance using large cross-country data sets. Besides capturing the simultaneity of the potential determinants of cross-country variations in macroeconomic performance and carefully separating short- from long-run dynamics, the PVAR/PVECM framework advanced allows to capture a variety of other features typically present in cross-country macroeconomic data, including model heterogeneity and cross-sectional dependence. We use the PVAR/PVECM framework we advance to reexamine the dynamic interrelation between investment in physical capital and output growth. The empirical findings for an unbalanced panel of 90 countries over the time period from at most 1950 to 2000 suggest for most regions of the world surprisingly strong support for a long-run relationship between output and investment in physical capital that is in line with neoclassical growth theory. At the same time, the notion that there would be even a long-run (let alone short-run) causal relation between investment in physical capital and output (or vice versa) is strongly refuted. However, the size of the feedback from output growth to investment growth is estimated to strongly dominate the size of the feedback from investment growth to output growth.

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.

The study examined the impact of financial development, foreign direct investment, market size and trade openness on domestic investment for 119 countries divided into four panels that are low-income countries (LIC), lower middle-income countries (LMIC), upper middle-income countries (UMIC) and high-income countries (HIC) between 1995 and 2019.

The present study bases its empirical procedure on the bases of the data mix. To this end, based on the presence of cross-sectional dependence, covariate-augmented Dickey–Fuller unit root and Westerlund cointegration second-generation tests were employed to validate the stationarity and cointegration of the variables, respectively. The novel Dynamic Common Correlation Effects estimator was employed to estimate the heterogeneous parameters while the Dumitrescu and Hurlin test was used to test for causality direction of the highlighted variables.

The empirical results show that market size and trade openness had a positive and statistically significant effect on domestic investment for all the income groups. Results also show that financial development had a positive and statically significant effect on domestic investment only for LMIC and HIC economies, while a positive and statistically insignificant effect was obtained for LIC, UMIC and the global panel. The causality results revealed a bidirectional relationship between domestic investment and the exogenous variables – financial development, foreign direct investment, market size and trade openness.

It is therefore, recommended that LIC and LMIC need to consider harmonising the financial system to lower credit limitations and adopt business-friendly policies. HIC and UMIC should seek more outward FDI policies and harmonise their trade policy, to reap more benefits from FDI and international trade.

On novelty, previous studies have been criticised for the effect on technical innovation of bank financing and institutional quality. This research tackles the deficiency using systematic institutional quality indicators and by taking other variables into account.

The transformation of coronavirus disease (COVID-19) from a regional health crisis in a Chinese city to a global pandemic has caused severe damage not only to the natural and economic lives of human beings but also to the financial markets. The rapidly pervading and daunting consequences of COVID-19 spread have plummeted the stock markets to their lowest levels in many decades especially in South Asia. This concern motivates us to investigate the stock markets’ response to the COVID-19 pandemic in four South Asian countries: Bangladesh, India, Pakistan and Sri Lanka. This study aims to investigate the causal impact of the number of confirmed COVID-19 cases on stock market returns using panel data of the countries stated above.

This study collects and analyzes the daily data on COVID-19 spread and stock market return over the period May 28, 2020 to October 01, 2020. Using Dumitrescu and Hurlin panel Granger non-causality test, the empirical results demonstrate that the COVID-19 spread measured through its daily confirmed cases in a country significantly induces stock market return. This paper cross-validates the results using the pairwise Granger causality test.

The empirical results suggest unidirectional causality from COVID-19 to stock market returns, indicating that the spread of COVID-19 has a dominant short-term influence on the stock movements. To the best of the knowledge, this study provides the first empirical insights into the impact of COVID-19 on the stock markets of selected South Asian countries taking the cross-sectional dependence into account. The results are also in line with the findings of other existing literature on COVID-19. Moreover, the results are robust across the two tests used in this study.

The findings are equally insightful to the fund managers and investors in South Asian countries. Taking into account the possible impact of COVID-19 on stock markets’ returns, investors can design their optimal portfolios more effectively. This study has another important implication in the sense that the impact of COVID-19 on the stock markets of South Asian countries may have spillover effects on other developing or even developed countries.

The purpose of this study is to examine two issues, namely the degree of current account deficit (CAD) sustainability and the degree of capital mobility.

The sample for this study comprises 24 Latin American and Caribbean countries, including three regional agreements: Andean Community, MERCOSUR (Mercado Común del Sur), and SICA (Central American Integration System). This study employs the dynamic common correlated effects mean group (DCCEMG) estimator in a panel data set to investigate the long-run relationship between savings and investment along with short-run dynamics.

The findings indicate that CAD is weakly sustainable in the Latin American and Caribbean region, MERCOSUR, and SICA, while CAD is strongly unsustainable in the Andean Community. The sub-period analysis reveals that CAD has been adversely affected by the 2008 crisis. However, in the post-crisis period, CAD has been slowly decreasing in the Latin American and Caribbean region and Andean Community, whereas CAD has continued increasing in MERCOSUR and SICA. Further, the estimates of error-correction terms and short-run coefficients indicate that the Andean Community and MERCOSUR observe a higher degree of long-run and short-run capital mobility than SICA.

The results carry fundamental implications for policy-making processes aimed at maintaining sustainable CADs.

This study gives an alternative interpretation of the “Feldstein-Horioka” coefficient in terms of CAD sustainability and analyses the saving–investment relationship in light of Chudik and Pesaran (2015).

This study investigates the relationship among economic growth, energy usage, urbanization, trade openness, carbon emission, ecological footprint, and ecological carbon footprint with the sample of G-7 countries over the period 1991–2017. The Continuously-Updated and Fully-Modified (CUP-FM) estimator method applied to the panel series is utilized. As a result of empirical analysis, an increase in the real per capita GDP increases the ecological footprint by 0.844%. Also, there are positive impacts that are detrimental to the environment among the environmental quality and explanatory variables for each established model. The findings indicate that a relationship exists among the variables in the long run, and at the same time, carbon emission as an indicator of pollution should be noticed as well as the ecological footprint and ecological carbon footprint. It is observed that a positive relationship exists among the variables such as economic growth, energy usage, and environmental pollution indicator. In this context, G-7 countries indicate that, upon taking their developed economic structures and global pollution levels into account, the Environment Kuznets curve (EKC) hypothesis becomes valid, and that the U-shaped behavior is valid for the total ecological footprint. It is been seen that, besides economic growth and energy usage, other explanatory variables contribute to environmental degradation in trade openness. In compliance with these results, in terms of the sustainability of economic development, G-7 countries should pay attention to economic activities that would impair the environmental quality and control them with regulations.

Textile, listed as one of the highly environmentally sensitive goods, its trade is susceptible to be influenced by the implementation of stringent environmental policies. This paper aims to investigate the long-run relationship between revealed comparative advantage (RCA) and Environmental Policy Stringency Index (EPSI) for textile exports of G20 countries in panel data setup.

Apart from trend analysis, the authors have employed Pedroni and Westerlund panel cointegration method and fully modified ordinary least square (FMOLS) method to study the long-run relationship between RCA and EPSI in presence of cross-sectional dependence.

A strong link between trade and environmental stringency is observed for textile in the present study. For G20 countries, slight evidence of the Pollution Haven Hypothesis has also been witnessed in the study. Correspondingly, the results reveal the presence of long-run association between the variables under study, implying that stringent environmental policies reduce RCA for some countries, whereas some countries witness the Porter hypothesis.

The results imply that policy formulation should not aim at limiting the efforts of connecting RCA to environmental stringency but to set trade policies in a wider framework, considering environmental concerns, as these are inseparable subjects. However, this study also provides relevant real-world implications that can support further research.

The present study has important implications for textile exporters such as green innovations. The Porter hypothesis can be a beneficial tool for G20 exporters in enhancing their export performance, especially for the ones dealing in environmentally sensitive goods. This study offers relevant policy implications and provides directions for future research on global trade and environment nexus.

This study deals in a debatable area of research that evaluates the interlinkages between environmental stringency and global trade flows in the G20 countries. An important observation of the study is the asymmetrical nature of policy stringency across different countries and its impact on trade. The unavailability of updated data is the limitation of the present study.