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The author develops and extends the asymptotic F- and t-test theory in linear regression models where the regressors could be deterministic trends, unit-root processes, near-unit-root processes, among others. The author considers both the exogenous case where the regressors and the regression error are independent and the endogenous case where they are correlated. In the former case, the author designs a new set of basis functions that are invariant to the parameter estimation uncertainty and uses them to construct a new series long-run variance estimator. The author shows that the F-test version of the Wald statistic and the t-statistic are asymptotically F and t distributed, respectively. In the latter case, the author shows that the asymptotic F and t theory is still possible, but one has to develop it in a pseudo-frequency domain. The F and t approximations are more accurate than the more commonly used chi-squared and normal approximations. The resulting F and t tests are also easy to implement – they can be implemented in exactly the same way as the F and t tests in a classical normal linear regression.

This study aims to investigate how the association between corporate social responsibility (CSR) and firm performance, documented in prior research, is affected by the joint effects of managerial ability and attributes of the firm's governance structure.

Unbalanced panel contains the essence of cross-sectional time-series data. A significant F-test proves the inappropriateness of pooled OLS regression to the sample. Further, the rejection of the Hausman test null favors fixed-effects over random-effects. However, statistically significant results from Shapiro–Wilk test, Breusch–Pagan test and Wooldridge test reveal non-normal distribution of the dependent variable, the presence of heteroscedasticity and the existence of first-order autocorrelation, respectively. Thus, this study applies feasible generalized least squares with panel-specific autocorrelation structure (hence, a slightly smaller sample) controlling for heteroskedasticity to all models after lagging all the explanatory variables by a year.

This study finds that higher levels of managerial ability enable firms to benefit more/less from their CSR investments depending on the presence/absence of appropriate governance devices. While CEO ability may be seen as an indicator of how well the CEO might serve the firm in the market-domain strategies, the results suggest that this may not be the case in the non-market domain in the absence of appropriate governance mechanisms.

The arguments and analyses in this study support two important contributions to the growing literature on CSR. First, the current study is one of the few to identify CEO ability as an important factor that may influence the dynamics of the firm's CSR (see also Garcì-Sànchez et al., 2019 and Yuan et al., 2019). Second, this study examines whether governance robustness minimizes the potential for opportunistic behavior of more able CEOs or constraints the effectiveness of more able CEOs in decisions pertaining to CSR.

The main goal of this paper is to investigate whether there is long-memory behavior in the CBOE Brazil ETF volatility index (named here VIXBR). As structural breaks may create a spurious long-range dependence, the presence of structural breaks is also gauged.

The study considers the period from October 2011 to March 2021, using daily data. To test the long-memory behavior, three empirical approaches are adopted: GPH, ELW and robust GPH (RGPH) estimator. To estimate the structural break points adopted to date the subsamples, the ICSS algorithm is used.

Results considering the total period (TP) and subsamples show that the breaks did not create a spurious long-memory behavior and together with the rolling estimation, reveal strong evidence of the long-range dependence in the CBOE Brazil ETF volatility index. The higher degree of persistent of the VIXBR series suggests an extended period of increased uncertainty that agents need consider when making their investment decision.

As possible extension of this study is to investigate the behavior of long memory and structural breaks for different frequencies (weekly, monthly, among others).

The presence of long-range dependence in the CBOE Brazil ETF volatility index reveals that the past information is important for the predictability of risks, and therefore, can help to protect against market risks, which has important implications regarding the future decisions of economic agents (for example, policy makers and investors).

Brazil is an emerging capital market (ECM) that has attracted a great deal of attention from investors and investment funds seeking to diversify its assets. This paper contributes to the empirical financial literature, by studying the long-memory behavior of the CBOE Brazil ETF volatility index, considering possible structural breaks. To the best of knowledge, this has not been done so far.

This paper aims to test three hypotheses in city growth literature documenting the poverty reduction observed in Brazil and exploring a rich spatial dataset for 5,564 Brazilian cities observed between 1991 and 2010. The large sample and the author's improved econometric methods allows one to better understand and measure how important income growth is for poverty reduction, the patterns of agglomeration and population growth in all Brazilian cities.

The author identifies literature gaps and use a sizeable spatial dataset for 5,564 Brazilian cities observed in 1991, 2000 and 2010 applying instrumental variables methods. The bias-corrected accelerated bootstrap percentile interval supports the author's point estimates.

This manuscript finds that Brazilian data for cities does not support Gibrat's law, raising the scope for urban planning and associated policies. Second, economic growth on a sustainable basis is still a vital source of poverty reduction (The author estimates the poverty elasticity at four percentage points). Lastly, agglomeration effects positively affect the city's productivity, while negative externalities underlie the city's development patterns.

Data for cities in Brazil possess unique characteristics such as spatial autocorrelation and endogeneity. Applying proper methods to find more reliable answers to the above three questions is a desirable procedure that must be encouraged. As the author points out in the manuscript, dealing with endogenous regressors in regional economics is still a developing matter that regional scientists could more generally apply to many regional issues.

Because the dynamic characteristics of the stock market are nonlinear, it is unclear whether stock prices can be predicted. This paper aims to explore the predictability of the stock price index from a long-memory perspective. The authors propose hybrid models to predict the next-day closing price index and explore the policy effects behind stock prices. The paper aims to discuss the aforementioned ideas.

The authors found a long memory in the stock price index series using modified R/S and GPH tests, and propose an improved bi-directional gated recurrent units (BiGRU) hybrid network framework to predict the next-day stock price index. The proposed framework integrates (1) A de-noising module—Singular Spectrum Analysis (SSA) algorithm, (2) a predictive module—BiGRU model, and (3) an optimization module—Grid Search Cross-validation (GSCV) algorithm.

Three critical findings are long memory, fit effectiveness and model optimization. There is long memory (predictability) in the stock price index series. The proposed framework yields predictions of optimum fit. Data de-noising and parameter optimization can improve the model fit.

The empirical data are obtained from the financial data of listed companies in the Wind Financial Terminal. The model can accurately predict stock price index series, guide investors to make reasonable investment decisions, and provide a basis for establishing individual industry stock investment strategies.

If the index series in the stock market exhibits long-memory characteristics, the policy implication is that fractal markets, even in the nonlinear case, allow for a corresponding distribution pattern in the value of portfolio assets. The risk of stock price volatility in various sectors has expanded due to the effects of the COVID-19 pandemic and the R-U conflict on the stock market. Predicting future trends by forecasting stock prices is critical for minimizing financial risk. The ability to mitigate the epidemic’s impact and stop losses promptly is relevant to market regulators, companies and other relevant stakeholders.

Although long memory exists, the stock price index series can be predicted. However, price fluctuations are unstable and chaotic, and traditional mathematical and statistical methods cannot provide precise predictions. The network framework proposed in this paper has robust horizontal connections between units, strong memory capability and stronger generalization ability than traditional network structures. The authors demonstrate significant performance improvements of SSA-BiGRU-GSCV over comparison models on Chinese stocks.

This paper examines the time-varying efficiency of nine major Middle East and North Africa (MENA) stock markets namely Egypt, Bahrain, UAE, Jordan, Saudi Arabia, Oman, Qatar, Morocco and Tunisia during times of COVID-19 pandemic outbreak and vaccines.

The authors use two econometric approaches: (1) autocorrelation tests including the wild bootstrap automatic variance ratio test, the automatic portmanteau test and the Generalized spectral test, and (2) a non-Bayesian generalized least squares-based time-varying model with statistical inferences.

The results show that the degree of stock market efficiency of Egyptian, Bahraini, Saudi, Moroccan and Tunisian stock markets is influenced by the COVID-19 pandemic crisis. Furthermore, the authors find a tendency toward efficiency in most of the MENA markets after the announcement of the COVID-19's vaccine approval. Finally, the Jordanian, Omani, Qatari and UAE stock markets remain globally efficient during the three sub-periods of the COVID-19 pandemic outbreak.

The results have important implications for asset allocations and financial risk management. Portfolio managers may maximize the benefit of arbitrage opportunities by taking strategic long and short positions in these markets during downward trend periods. Policymakers should implement the action plans and reforms to protect the stock markets from global shocks and ensure the stability of the stock markets.

Corporate governance across small and medium enterprises (SMEs) is undergoing unremitting changes, primarily due to the listing of SMEs on SME exchanges. The changing aspects of governance may influence the financial performance of SMEs. This paper examines how corporate governance influences the financial performance of listed SMEs in the context of developing economies like India. Ownership concentration (promoters' holding) and information disclosures measure corporate governance in this examination.

The sample for this study includes 88 listed SMEs from the Bombay Stock Exchange (BSE) SME platform in India. The data are collected for the period between 2018 and 2020. The study employs panel data analysis. The fixed effects model, coupled with the computation of cluster robust standard errors, is used to test the relationship between variables.

The results demonstrate that ownership concentration is not significantly related to financial performance. Further, information disclosures are inversely significant for financial performance. The results show that agency problems and information asymmetry plague the sampled firms. Further, the results of the study are indicative of inefficiencies in the governance structures of SMEs. Thus, it is evident that listed SMEs fail to reap the benefits of corporate governance.

The study's findings should enlighten SME owners and managers on the benefits of corporate governance for SMEs. This is a pressing need at current times as the listing of SMEs is shifting the landscape of SME governance. Today, all firms, including SMEs, are expected to adopt and maintain near internationally benchmarked corporate governance standards. Secondly, the study's implications on how the ownership and information disclosures can be used to influence the financial outcomes of SMEs will benefit the overall business ecosystem. The policyholders and academics can use this study to boost the regulations and research in line with each other.

Reforming monitoring mechanisms of firm activities and restructuring disclosure practices are essential for SMEs to produce better financial outcomes. The true benefits of corporate governance cannot be realized without attention to financial performance. The study is relevant to practitioners, lawmakers and academics to advance corporate governance for SMEs.

Entrepreneurial activity is a phenomenon that increases the economic growth of countries and improves their social welfare. The economic development levels of countries have significant effects on these entrepreneurial activities. This research examines which institutional and macroeconomic variables explain early-stage entrepreneurship activities in developed and developing economies.

The authors conducted panel data analysis on the data from the Global Entrepreneurship Monitor (GEM) and International Monetary Fund (IMF) surveys covering the years 2009–2018.

First, the authors' results reveal that cognitive, normative and regulatory institutions and macroeconomic factors affect early-stage entrepreneurial activity in developed and developing countries differently. Second, the authors' findings indicate that cognitive, normative and regulatory institutions affect early-stage entrepreneurship more positively in developed than developing countries. Finally, the authors' results report that macroeconomic factors are more effective in early-stage entrepreneurial activity in developing countries than in developed countries.

This study provides a better understanding of the components that help explain the differences in entrepreneurship between developed and developing countries regarding institutions and macroeconomic factors. In this way, it contributes to developing entrepreneurship literature with the theoretical achievements of combining institutional theory and macroeconomic indicators with entrepreneurship literature.

This study investigates the effect of managerial ability on labor productivity by analyzing various methods in the firm-year panel data of listed firms in South Korea from 2002 to 2019. Managerial ability was analyzed using the measurement method of Demerjian et al. (2012), while labor productivity was analyzed using value-added and sales. The authors find that managerial ability has a positive effect on labor productivity. In other words, the productivity of employees improves with the appointment of a manager with higher abilities. The study’s findings suggest that firms should consider managerial ability as a means of improving labor productivity.