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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 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.

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.

The main objective of this research is to investigate if there is a long-term relationship between “terrorism” and sustainable “economic growth” in Big Ten Countries.

The data was tested via Panel ARDL Analysis. The growth rate (GR) is the dependent variable, and the “Global Terror Index (GTI)” is the independent variable as the terror indicator. The ratio of Foreign Direct Investment (FDI) to the Gross Domestic Product (GDP), and the ratio of External Balance (EB) to Gross Domestic Product (GDP) are included in the model as the control variables due to their effect on the growth rate. A Panel ARDL analysis is conducted to examine the existence of long-term co-integration between terror and the economy. The planning of the study, the formation of its theoretical and conceptual framework, and the literature research were carried out in 2 months, and the collection of data, the creation of the methodology and the analysis of the analyzes were carried out in 2 months, the interpretation of the findings and the development of policy recommendations were carried out within a period of 1 month. The entire study was completed in a total of 5 months.

Results showed that “Terror” has a negative impact on “Growth Rate” in the long term while “External Balance” and “Foreign Direct Investment” positively affect the Growth Rate. The coefficients for the short term are not statistically significant.

The sample is only limited to Big Ten including China, India, Indonesia, South Korea, Argentina, Brazil, Mexico, Turkey, Poland and South Africa. The period for annual data collection covers the years between 2002 and 2019 and due to the unavailability of data.

Considering the risks and the mutual negative effect that turns into a vicious circle between terrorism and the economy, it is necessary to eliminate the problems that cause terrorism in the mentioned countries, on the one hand, and to develop policies that will improve economic performance on the other.

Trustful law enforcement bodies have to be established and supported by all technological means to prevent terror. The conditions causing terror have to be investigated carefully and the problems causing terror or internal conflict have to be solved. International cooperation against terrorism has to be strengthened and partnerships, information, experience sharing have to be supported at the maximum levels.

It is certain that terror might have a negative influence on the performance of economies. But the limited number of studies within this vein and the small size of their sample groups mostly including single-country case studies require conducting a study by using a larger sample group of countries. Big Ten here represents at least half of the population of the world and different regions of the Globe.

Estimation of (in)efficiency became a popular practice that witnessed applications in virtually any sector of the economy over the last few decades. Many different models were deployed for such endeavors, with Stochastic Frontier Analysis (SFA) models dominating the econometric literature. Among the most popular variants of SFA are Aigner, Lovell, and Schmidt (1977), which launched the literature, and Kumbhakar, Ghosh, and McGuckin (1991), which pioneered the branch taking account of the (in)efficiency term via the so-called environmental variables or determinants of inefficiency. Focusing on these two prominent approaches in SFA, the goal of this chapter is to try to understand the production inefficiency of public hospitals in Queensland. While doing so, a recognized yet often overlooked phenomenon emerges where possible dramatic differences (and consequently very different policy implications) can be derived from different models, even within one paradigm of SFA models. This emphasizes the importance of exploring many alternative models, and scrutinizing their assumptions, before drawing policy implications, especially when such implications may substantially affect people’s lives, as is the case in the hospital sector.

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.

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 research aims to study the regression, cointegration and causality between the construction sector (CS) and the Gross Domestic Product (GDP), considering other variables in the study such as interest rate, taxation, industry sector, investment and Foreign Direct Investment (FDI), which are analyzed through unique panel models. The study was conducted in Turkey and the ten other countries of the European Union (EU) from 1988 to 2019.

Regression, cointegration and causality methods were used to investigate the different types of relationships between variables in the models. Data were obtained from official databases and the study contains four main stages, which are explained in detail in the methodology section.

The study used the analysis methods of regression, cointegration and causality tests and found that the CS and GDP have long-run estimates and the relationship between the two for different countries is negative in a two-way direction. Results are detailed in the analysis section.

No data were available for the variables before 1988 for most countries, which led to a limited number of observations and issues in statistical analysis methods.

Previously, only input and output tables were used in the analysis. The impact of interest rate, taxation, investment and FDI has not been analyzed. Key variables are very relevant for Turkey, which suffers from chronical inflation and taxation regimes. These show variability with the EU countries for comparative analysis and have not been explored to date, remaining as a major gap for the construction industry. No attempts were made to use regression, cointegration and causality methods with variables. These analysis methods enable an understanding of the differences in variance (heteroscedasticity) and the presence of cross-sectional dependence (CSD), both critical for the reliability of the comparison of data sets and 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.

The empirical analysis dealt in this paper emphasizes on the impact of military expenditures on out of pocket (OOP) healthcare payments. A sizeable body of defence economics literature has investigated the trade-off between military and public health expenditure, by testing the crowding-out or growth-stimulating hypothesis; does military expenditure scaling up crowd-out or promote governmental resources for social and welfare programs, including also state health financing?

In this study, panel data from 2000 to 2018 for 129 countries is used to examine the impact of military expenditure on OOP healthcare payments. The dataset of countries is categorized into four income-groups based on World Bank's income-group classification. Dynamic panel data methodology is applied to meet study objectives.

The findings of this study indicate that military expenditure positively affects OOP payments in all the selected groups of countries, strongly supporting in this way the crowding-out hypothesis whereby increased military expenditure reduces the public financing on health. Study econometric results are robust since different and alternative changes in specifications and samples are applied in our analysis.

Under the economic downturn backdrop for several economies in the previous decade and on the foreground of a potential limited governmental fiscal space related to the Covid-19 pandemic adverse economic effects, this study provides evidence that policy-makers have to adjust their government policy initiatives and prioritize Universal Health Coverage objectives. Consequently, the findings of this study reflect the necessity of governments as far as possible to moderate military expenditures and increase public financing on health in order to strengthen health care systems efficiency against households OOP spending for necessary healthcare utilization.

Despite the fact that a sizeable body of defence economics literature has extensively examined the impact of military spending on total and public health expenditures, nevertheless to the best of our knowledge there is no empirical evidence of any direct effect of national defence spending on the main private financing component of health systems globally; the OOP healthcare payments.