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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.

The purpose of this paper is to empirically investigate whether inward remittance leads to export performance in selected South Asian economies over the time period of 1993–2017.

The stationarity of the variables is checked by Levin, Lin and Chu t, Breitung t-stat., Im, Pesaran and Shin W-stat., ADF–Fisher and Philips–Perron–Fisher panel unit root tests. Panel Granger Causality is used to verify the short-run causality. Pedroni’s, Kao’s and Johansen–Fisher panel cointegration approaches are employed to examine the long-run relationship among the variables. Panel VECM is used to confirm the existence of a long-run relationship among the variables.

Panels FMOLS and DOLS show that remittance inflows have negatively impacted the export performance of the selected South Asian countries during the study period. Granger Causality and VECM test confirm the existence of short-run and long-run relationship among the variables. The authors conclude that inward remittance is affecting export performance negatively during the study period. Furthermore, inward remittances occupy a major source of development finance for selected South Asian countries.

The study uses a dynamic macroeconomic modeling framework to assess the inward remittance on export performance in South Asian countries. Taking into account the diversity of the level of growth experienced by the five countries in the Asian region, the study uses an appropriate regression technique, i.e. panel dynamic OLS whose results are robust. As exports are a proven way to further economic growth, this study fills a vital gap in the literature by ascertaining the degree of impact of remittances in influencing outbound exports from the South Asian region.

The purpose of this paper is to review and analyse Vision 2023: the Turkish National Technology Foresight project. The paper aims to review the process of conducting the project, how it was implemented afterwards and how it compares to other national technology foresight projects

Through a literature search, a process framework was conducted. The analysis was then conducted in four phases. First a process review, second a comparative review, third content review and finally a post project review. Expert interviews and site visits to Turkish State Planning Organization and TUBITAK (Scientific and Technical Research Council of Turkey) helped the authors to collect the data on Vision 2023 including how it was established, which areas were involved and what the recommendations were. Finally an expert panel was organized as part of a recent Portland International Conference on Management of Engineering and Technology. This included experts involved in the project as well as leading researchers who have been analyzing this project. This panel helped to validate the results.

Processes used in the Turkish project were similar to the other national projects, however lack of political ownership and change in leadership had been blocking the recommendations coming out of this project from being implemented. A second effort is required to modify the results of the first one and to establish political ownership and leadership. Several other national projects had multiple rounds before solid actions were taken. Industry needs to be a part of the effort as the panelists indicated that several key corporations were missing in the first project.

The project provides comparative details on running national technology foresight projects. This should be useful for those responsible for planning similar projects.

The paper reviews the project implementation process and what happened after the implementation providing feedback on what should have been done or should be done in similar foresight projects.

The purpose of this paper is to present the studies to develop structural highly orthotropic panels. These types of panels provide the capabilities required for structural morphing. They are highly deformable in one given direction; and in the perpendicular direction, one must be compliant to sustain the internal loads per certification stiffness and strength requirements.

Neither classic orthotropic panels nor regular cellular solids Bloch wave theories are strictly applicable on their study. A combination of computational method, MATLAB-coded, to analyze stability and classic structural beam theory is studied. Then, non-linear finite element method models are developed for an aerospace control surface application; their results are compared with reported Bloch wave sequences on periodic cellular solid panels.

The stability along stiffer direction is a requirement to obtain a continuous deformation and plasticization sequence of the cell rows in the perpendicular direction. A sample panel is sized and 3D modeled, and then produced using additive layer manufacturing process to demonstrate the initial stages of a validation and verification campaign.

This paper provides a new method to mechanical characterize highly orthotropic panels.

The paper aims to study the relationship between economic growth, nuclear energy consumption and carbon dioxide (CO₂) emissions for a panel of 25 countries over a period of 1993-2010. Through this study, the author has provided an insight into one of the available sources of energy, i.e. nuclear energy and its impact on economic growth and CO₂ emissions.

Separate panels are created for developing and developed economies. Short- and long-run causalities between the variables are established using error correction mechanism.

For the developed countries, short-run causality running from CO₂ emissions to economic growth was estimated, whereas strong form of causality indicated the dependence of CO₂ emissions on economic growth and nuclear energy consumption was seen to impact CO₂ emissions. For the developing countries, both the short-run and strong-form causality estimates indicate that economic growth causes CO₂ emissions.

On policy front, developing countries can safely adopt CO₂ cut-back policies as they are not found to impact economic growth. For the developed countries, such policies may impede growth in the short run, but in the long run these policies do not affect the economic growth.

Keeping in mind the significance of nuclear energy consumption in economic growth and less/no GHG emissions generated by nuclear energy, this study validates its significance. This study, to the best of the author's knowledge, considers the largest panel (i.e. 25 countries) to date and the only study that focuses on studying three different panels (complete dataset, developed countries, developing countries) in one study and applies the vector error correction mechanism to study the causal relationship between nuclear energy consumption, CO₂ emissions and economic growth.

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.

The purpose of this paper is to examine the relationship between financial development and rural-urban income inequality (INQ) in South Asian Association for Regional Cooperation (SAARC) countries using panel data from 1986-2012.

The stationarity properties are checked by the LLC and IPS panel unit root tests. The paper applied the Pedroni’s panel co-integration test to examine the existence of the long-run relationship and coefficients of co-integration are examined by fully modified ordinary least squares. The short-term and long-run causality is examined by panel Granger causality.

The results of Pedroni co-integration test indicate that there exists a long-run relationship among the variables. The findings suggest that financial development increases rural-urban inequality whereas trade openness reduces rural-urban inequality. The empirical results of panel Granger causality indicate evidence of short-run causality confirms that economic growth and financial development causes rural-urban INQ.

The present study recommends for appropriate economic and financial reforms focusing on financial inclusion to reduce rural-urban INQ in SAARC countries. Financial policies geared toward agriculture and rural population should be adopted to reduce the prevailing rural-urban INQ in SAARC region.

Till date, there is hardly any study exploring the causal relationship between financial development and rural-urban INQ for SAARC countries by using panel co-integration and causality techniques. So the contribution of the paper is to fill these research gaps in the literature.

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.

Purpose — This study proposes an optimal survey design method for multi-day and multi-period panels that maximizes the statistical power of the parameter of interest under the conditions that non-linear changes in response to a policy intervention over time can be expected.

Design/methodology/approach — The proposed method addresses balances among sample size, survey duration for each wave and frequency of observation. Higher-order polynomial changes in the parameter are also addressed, allowing us to calculate optimal sampling designs for non-linear changes in response to a given policy intervention.

Findings — One of the most important findings is that variation structure in the behaviour of interest strongly influences how surveys are designed to maximize statistical power, while the type of policy to be evaluated does not influence it so much. Empirical results done by using German Mobility Panel data indicate that not only are more data collection waves needed, but longer multi-day periods of behavioural observations per wave are needed as well, with the increase in the non-linearity of the changes in response to a policy intervention.

Originality/value — This study extends previous studies on sampling designs for travel diary survey by dealing with statistical relations between sample size, survey duration for each wave, and frequency of observation, and provides the numerical and empirical results to show how the proposed method works.

Academic libraries have endured rapid change in the past two decades that has had repercussions on how they manage their organization and deliver library services. Skyrocketing costs, especially for journals, explosive growth in new technologies, fiscal exigencies caused by a tightening of public financing of most academic institutions, demands for greater accountability, and the onslaught of electronic delivery of networked information, are just some of the major obstacles libraries are encountering (Lubans, 1996; Riggs, 1993; Shaughnessy, 1987). Customers of academic libraries are increasingly less satisfied because of limited resources and the difficulties they encounter in accessing printed material in a traditional library facility (Doughtery, 1992). The emergence of textual materials in electronic form has added a new dimension to this discontent. While such resources have the potential for meeting the information needs more dynamically, the costs for information have been exorbitant, particularly since full electronic texts have not been sufficient in coverage to supplant printed resources (Tenopir, 1993). These phenomena require academic libraries to use a more integrated and flexible approach to problem solving (Gapen, Hampton & Schmitt, 1993).