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This study examines the roles of cross-sectional dependence, asymmetric structure and country-to-country policy variations in the inflation-poverty reduction causal nexus in selected sub-Saharan African (SSA) countries from 1981 to 2019.

To account for cross-sectional dependence, heterogeneity and policy variations across countries in the inflation-poverty reduction causal nexus, this study uses robust Hatemi-J data decomposition procedures and a battery of second-generation techniques. These techniques include cross-sectional dependency tests, panel unit root tests, slope homogeneity tests and the Dumitrescu-Hurlin panel Granger non-causality approach.

Unlike existing studies, the panel and country-specific findings exhibit several dimensions of asymmetric causality in the inflation-poverty nexus. Positive inflationary shocks Granger-causes poverty reduction through investment and employment opportunities that benefit the impoverished in SSA. These findings align with country-specific analyses of Botswana, Cameroon, Gabon, Mauritania, South Africa and Togo. Also, a decline in poverty causes inflation to increase in the Congo Republic, Madagascar, Nigeria, Senegal and Togo. All panel and country-specific analyses reveal at least one dimension of asymmetric causality or another.

All stakeholders and policymakers must pay adequate attention to issues of asymmetric structures, nonlinearities and country-to-country policy variations to address country-specific issues and the socioeconomic problems in the probable causal nexus between the high incidence of extreme poverty and double-digit inflation rates in most SSA countries.

Studies on the inflation-poverty nexus are not uncommon in economic literature. Most existing studies focus on inflation’s effect on poverty. Existing studies that examine the inflation-poverty causal relationship covertly assume no asymmetric structure and nonlinearity. Also, the issues of cross-sectional dependence and heterogeneity are unexplored in the causal link in existing studies. All panel studies covertly impose homogeneous policies on countries in the causality. This study relaxes this supposition by allowing policies to vary across countries in the panel framework. Thus, this study makes three-dimensional contributions to increasing understanding of the inflation-poverty nexus.

Critics say cryptocurrencies are hard to predict and lack both economic value and accounting standards, while supporters argue they are revolutionary financial technology and a new asset class. This study aims to help accounting and financial modelers compare cryptocurrencies with other asset classes (such as gold, stocks and bond markets) and develop cryptocurrency forecast models.

Daily data from 12/31/2013 to 08/01/2020 (including the COVID-19 pandemic period) for the top six cryptocurrencies that constitute 80% of the market are used. Cryptocurrency price, return and volatility are forecasted using five traditional econometric techniques: pooled ordinary least squares (OLS) regression, fixed-effect model (FEM), random-effect model (REM), panel vector error correction model (VECM) and generalized autoregressive conditional heteroskedasticity (GARCH). Fama and French's five-factor analysis, a frequently used method to study stock returns, is conducted on cryptocurrency returns in a panel-data setting. Finally, an efficient frontier is produced with and without cryptocurrencies to see how adding cryptocurrencies to a portfolio makes a difference.

The seven findings in this analysis are summarized as follows: (1) VECM produces the best out-of-sample price forecast of cryptocurrency prices; (2) cryptocurrencies are unlike cash for accounting purposes as they are very volatile: the standard deviations of daily returns are several times larger than those of the other financial assets; (3) cryptocurrencies are not a substitute for gold as a safe-haven asset; (4) the five most significant determinants of cryptocurrency daily returns are emerging markets stock index, S&P 500 stock index, return on gold, volatility of daily returns and the volatility index (VIX); (5) their return volatility is persistent and can be forecasted using the GARCH model; (6) in a portfolio setting, cryptocurrencies exhibit negative alpha, high beta, similar to small and growth stocks and (7) a cryptocurrency portfolio offers more portfolio choices for investors and resembles a levered portfolio.

One of the tasks of the financial econometrics profession is building pro forma models that meet accounting standards and satisfy auditors. This paper undertook such activity by deploying traditional financial econometric methods and applying them to an emerging cryptocurrency asset class.

This paper attempts to contribute to the existing academic literature in three ways: Pro forma models for price forecasting: five established traditional econometric techniques (as opposed to novel methods) are deployed to forecast prices; Cryptocurrency as a group: instead of analyzing one currency at a time and running the risk of missing out on cross-sectional effects (as done by most other researchers), the top-six cryptocurrencies constitute 80% of the market, are analyzed together as a group using panel-data methods; Cryptocurrencies as financial assets in a portfolio: To understand the linkages between cryptocurrencies and traditional portfolio characteristics, an efficient frontier is produced with and without cryptocurrencies to see how adding cryptocurrencies to an investment portfolio makes a difference.

This study undertakes a comprehensive investigation into the far-reaching repercussions of Covid-19 stimulus packages and containment policies on stock returns, meticulously examining a diverse array of 14 distinct markets.

This study employed the Panel SVAR model to analyze the relationships between various policies and stock market performance during the Covid-19 outbreak. The sample comprises 5432 daily observations spanning from December 2020 to January 2022 for the 14 selected markets, with missing data excluded.

The findings reveal three consistent impacts across all 14 markets. Firstly, stock returns immediately reversed and decreased within a day when Governments tightened containment policies. Secondly, economic stimulus packages led to a fall in stock returns. Thirdly, an increasing death rate caused the stock return to decrease in the following two days. These findings are supported by the uniform impulse responses in all three shocks, including common, composite and idiosyncratic shocks. Furthermore, all inverse root tests satisfy the stability conditions, indicating the stability and reliability of Panel SVAR estimations.

One vital implication is that all government decisions and measures taken against the shock of Covid-19 must consider economic impacts to avoid unnecessary financial losses and support the effective functioning of stock markets during similar shocks. Secondly, investors should view the decline in stock returns due to Covid-19 effects as temporary, resulting from anxiety about the outbreak. The study highlights the importance of monitoring the impact of policies on financial markets and the broader economy during crises. Overall, these insights can prove helpful for investment decisions and policymaking during future crises.

This study constitutes a noteworthy addition to the literature on behavioural finance and the efficient market hypothesis, offering a meticulous analysis of the multifaceted repercussions of Covid-19 on market interactions. In particular, it unveils the magnitude, duration and intricate patterns of market volatilities linked to significant shock events, encompassing a comprehensive dataset spanning 14 distinct markets.

This paper aims to build an improved grey panel clustering evaluation model and evaluate the comprehensive development potential of industrial economy, society and ecological environment in the Yangtze River Economic Belt of China. The purpose of this study is to provide some theoretical basis and tool support for management departments and relevant researchers engaged in industrial sustainable development.

This study uses the driving force pressure state impact response analysis framework to build a comprehensive evaluation index system. Based on the center point triangle whitening weight function, it classifies the panel grey clustering of improvement time and index weight.

The results show that there are great differences in the level of industrial ecological development in different regions of the Yangtze River Economic Belt, which further illustrates the scientificity and rationality of the evaluation method proposed in this paper.

Due to the industrial ecological development is in a constantly changing state, and the information is uncertain. Whitening weight function is introduced to represent the complete information of relevant data. The industrial ecological evaluation involves a comprehensive complex system, which belongs to the panel data analysis problem. The improved grey panel clustering evaluation model is applied to grade the industrial ecological development level of the Yangtze River Economic Belt. The results have important guiding significance for the balanced development of industrial ecology in the region.

Due to the industrial ecological development is in a constantly changing state, and the information is uncertain. Whitening weight function is introduced to represent the complete information of relevant data. The industrial ecological evaluation involves a comprehensive complex system, which belongs to the panel data analysis problem. In order to improve the effectiveness of industrial ecological evaluation, the improved grey panel clustering evaluation model is applied to grade the industrial ecological development level of the Yangtze River Economic Belt. The results have important guiding significance for the balanced development of industrial ecology in the region.

the new model proposed in this paper complements and improves the grey clustering analysis theory of panel data, that is, aiming at the subjective limitation of using time degree to determine time weight in panel grey clustering, a comprehensive theoretical method for determining time weight is creatively proposed. Combining the DPSIR (Driving force-Pressure-State-Influence-Response) model model with ecological development, a comprehensive evaluation model is constructed to make the evaluation results more authentic and comprehensive.

Drawing on legitimacy theory, this study aims to investigate whether the benefits of the external assurance process pass beyond the current period and help firms improve corporate social responsibility (CSR) performance in the subsequent periods. Furthermore, the authors examine whether corporate governance (CG) and firm visibility moderate the relationship between assurance and CSR performance.

The authors retrieved data from Thomson Reuters from 2002 to 2019 and executed a fixed-effects (FE) panel regression analysis. The country-level sample distribution includes 63 countries with 4,625 unique firms and 29,054 data points within these countries. The authors run several robustness tests using an alternative subsample, instrumental variable regression analysis, country-industry-year FE regression analysis, excluding the financial sector and including additional control variables and regression analysis based on propensity score matching.

The findings indicate that external assurance helps firms achieve greater CSR performance in the current period and the subsequent two periods following external assurance. However, external assurance exerts its strongest positive impact on CSR performance in the current period, and its influence extends, albeit at a weaker level, to the following two periods. Furthermore, the first moderation analysis reveals that governance structure helps firms translate the assurance process into the greater social performance but does not help to achieve higher environmental performance. The second moderation analysis reveals that firm visibility/size positively moderates between the assurance process and governance and social performance but not between the assurance process and environmental performance.

Despite the concurrent association between CSR performance and assurance being examined before, the lag-lead relationship is the novelty of the study to highlight the long-term effect of assurance on CSR performance. Besides, although the direct effect of both CG practices and firm visibility on CSR performance and the external assurance process has been investigated before, the authors extend the literature by examining the moderating effect of CG practices and firm visibility on the external assurance and CSR performance relationship. This provides a better explanation of the extent to which the effect of external assurance on CSR performance is constructed and conditioned by CG practices and firm visibility, thereby drawing attention to contingencies’ role in firms’ practices.

The study's objective is to examine the relevance of globalization in affecting the size of the shadow economy in selected African nations.

To do this, the authors employ the KOF globalization index and implement both static and dynamic common correlated mean group estimators on a panel of 24 African nations from 1995–2017. This technique accommodates the issue of cross-sectional dependence, sample bias and endogenous regressors. Panel threshold analysis is also conducted to establish the nonlinearity between globalization and the shadow economy. To examine the causality between the variables, the study employs Dumitrescu and Hurlin's panel causality test.

The results show that globalization reduces the size of the shadow economy. The results of the nonlinear analysis suggest a U-shaped relationship. Overall globalization has a threshold impact of 48.837%, economic globalization has 45.615% and political globalization has 66.661% while social globalization has a threshold value of 35.744%. The results of the panel causality show that there is a bidirectional causality between the two variables.

The results suggest that the government and other relevant authorities need to introduce capital controls and other policy measures to moderate the degree of social, political and cultural diffusion. Appropriate policies should be formulated to monitor the extent of African economic openness to other continents to maximize the gains from globalization.

Apart from being the first study in the African region that evaluates the relevance of globalization in controlling the shadow economy, it also analyzes the dynamics and threshold analysis between the two variables using advanced panel econometrics which makes the study unique. The study suggests that globalization tools are useful for affecting the size of the shadow economy in Africa. This study provides fresh empirical evidence on the impact of globalization on the shadow economy in the case of Africa.

The objective is to assess the relationship between financial inclusion and bank profitability in emerging economies, i.e. “Bangladesh, Egypt, Indonesia, Mexico, Nigeria, Pakistan, Philippines, and Vietnam”.

The second-generation econometrics of panel data has been applied to examine the cross-section independence and control the heterogeneity between cross sections. Additionally, the authors employ the following tests for the analysis: “the unit root test, Westerlund's (2007) bootstrap cointegration, Pedroni cointegration, fully modified ordinary least square (FMOLS), and heterogeneous panel causality techniques”. The annual data consist of the period from 2000 to 2019.

The findings reveal that financial inclusion fosters bank profitability. Therefore, easier access to financial services and products will maximize banks' profitability. Additionally, the association between financial inclusion and bank profitability is unidirectional.

This research is a first attempt to bring a novel contribution to the subject of emerging economies by investigating the association between financial inclusion and bank profitability. Another unique addition to the literature is the use of a novel financial inclusion index. At last, a panel cointegration technique, FMOLS and heterogeneous panel non-causality tests are taken into consideration for the in-depth analysis.

Agricultural sector not only meets the nutritional requirements of all living creatures but also generates the primary source of the raw material provided by various branches of industry to fulfill their functions. It is of great importance to increase studies on oilseeds which have an important role in Turkey's agricultural products. They are grown in almost all of the country, which are vital for the nutrition and many sectors. The main purpose of the study is to offer an integrated approach to determine potential warehouse locations for oilseeds.

This is the first study that integrates Delphi, analytical hierarchical process (AHP), technique for order preference by similarity to ideal solution (TOPSIS), P-Median and Panel data analysis in a real case. This integrated approach consists of the following steps, respectively: (1) The criteria were determined by the Delphi method. (2) The weights of the criteria were calculated by AHP and the provinces with the highest oilseed warehouse potential in seven regions of Turkey were specified by TOPSIS. (3) Oilseed warehouse numbers and locations were obtained by P-Median. (4) In order to answer whether the distribution network is profitable in the future with the determined center locations, a forecast model based on panel data analysis was created. (5) Regional representatives were determined for 2030, and the distribution network was analyzed again. (6) The costs that arose in 2018 and 2030 were computed and compared by cost analysis. (7) The effect of the change in criteria weights on the alternative results was tested by scenario analysis.

The findings indicated that oilseed crop production potential and oilseed crop production area turned out to be the most important criteria. Furthermore, the results showed that this model is robust and suitable for warehouse location selection studies.

The study can serve as a guide for local and central policy makers with both the criteria it uses and the model it develops.

The main contribution of this study is that the integrated approach has been used for the first time in location selection in a real case.

Higher economic growth accompanied by rising energy demand poses severe challenges to the long-term environmental sustainability of E7 economies, including Brazil, China, India, Indonesia, Mexico, Russia and Turkey. Thus, this paper explores the influence of foreign direct investment (FDI) inflows on energy diversification for E7 economies.

The dataset is panel data for emerging seven (E7) economies, covering the period 1992–2017. The empirical investigation relies on econometric techniques: panel cointegration test and panel autoregressive distributed lag model.

The findings reveal that energy diversification and FDI inflows are cointegrated. In the long run, higher FDI inflows encourage energy diversification, but energy efficiency improvements discourage energy diversification. In the short run, the effects of FDI inflows on energy diversification vary across E7 economies, highlighting the role of country-specific factors in determining the short-run influence of FDI inflows on energy diversification.

The findings suggested that FDI policies should encourage the adoption of nonconventional energy resources to stimulate energy diversification in E7 economies. Besides, better coordination between energy diversification and energy efficiency policies is required in the long run for a successful transition towards low-carbon economy goals.

This study is a unique empirical exercise that uncovers a cointegrating relationship between energy diversification and FDI inflows for E7 economies. Moreover, the analysis provides homogenous long-run and heterogeneous (country-specific) short-run coefficient estimates for the effect of FDI inflows on energy diversification.

This study aims to examine the effect of corporate social responsibility (CSR) adoption on differentiation and cost leadership strategies and how governance structure moderates this CSR–strategy relationship.

The study data were retrieved from Thomson Reuters for non-financial firms between 2013 and 2019, and a fixed-effects panel regression analysis was executed.

The results indicate that CSR fosters cost leadership strategy but weakens differentiation strategy. This result supports the value generation school for cost leaders but also confirms the agency theory perspective for differentiators. Moreover, the governance structure does not moderate the relationship between a firm's CSR engagement and its business strategy, which implies a lack of corporate policies that concurrently consider both its CSR investment and strategies.

The findings of this study imply that cost leaders can integrate CSR practices into their business strategy and use their CSR engagement to increase their competitive position by stimulating cost efficiency and creating greater turnover. On the contrary, for differentiators, there is a trade-off between environmental and social engagement and business strategies. Thus, they are advised to enrich their unique product development abilities through the integration of environmental and social practices and reinforce their competitive position by addressing stakeholders' interests. The practical implication of the moderation analysis is that there is no rooted corporate policy behind the connection between CSR and firm strategy for both cost leaders and differentiators, which constitutes a missing link.

The findings of this study are of critical importance for firms, offering justification for the integration of two vital perspectives: social and environmental sustainability and financial sustainability. The moderating effect of governance performance tests the upper echelon's role in maintaining both sustainability perspectives concurrently and strengthening the legitimacy of the firms in society. Although maintaining a business strategy is important for shareholders' interests, pursuing a social and environmental sustainability strategy is crucial for meeting the expectations of all stakeholders.