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This study probes the psychological interplay between investor sentiment and the returns of cryptocurrencies Bitcoin and Ethereum. Employing the Granger causality test, the authors aim to gauge how extensively the Fear and Greed Index (FGI) can predict cryptocurrency return movements, exploring the intricate bond between investor emotions and market behavior.

The authors used the Granger causality test to achieve research objectives. Going beyond conventional linear analysis, the authors applied Smooth Quantile Regression, scrutinizing weekly data from July 2022 to June 2023 for Bitcoin and Ethereum. The study focus was to determine if the FGI, an indicator of investor sentiment, predicts shifts in cryptocurrency returns.

The study findings underscore the profound psychological sway within cryptocurrency markets. The FGI notably predicts the returns of Bitcoin and Ethereum, underscoring the lasting connection between investor emotions and market behavior. An intriguing feedback loop between the FGI and cryptocurrency returns was identified, accentuating emotions' persistent role in shaping market dynamics. While associations between sentiment and returns were observed at specific lag periods, the nonlinear Granger causality test didn't statistically support nonlinear causality. This suggests linear interactions predominantly govern variable relationships. Cointegration tests highlighted a stable, enduring link between the returns of Bitcoin, Ethereum and the FGI over the long term.

Despite valuable insights, it's crucial to acknowledge our nonlinear analysis's sensitivity to methodological choices. Specifics of time series data and the chosen time frame may have influenced outcomes. Additionally, direct exploration of macroeconomic and geopolitical factors was absent, signaling opportunities for future research.

This study enriches theoretical understanding by illuminating causal dynamics between investor sentiment and cryptocurrency returns. Its significance lies in spotlighting the pivotal role of investor sentiment in shaping cryptocurrency market behavior. It emphasizes the importance of considering this factor when navigating investment decisions in a highly volatile, dynamic market environment.

Wheat, which is one of the major staple food grain crops in India, continues to depict occasional fluctuation in the prices though Union government has adopted administered price policy for wheat by intervening in its procurement at assured prices and distribution. Such fluctuations in prices are usually attributed to inefficient functioning of the agricultural markets. Since spatially separated markets also play an important role to determine efficiency of the agricultural markets, the study has used market integration as one of the tools to analyze the price transmission across the spatially separated markets to identify causes of price fluctuations and suggest ways to stabilize wheat prices.

The study utilizes monthly wholesale prices for January, 2006 to May, 2016 for dara wheat. First, the study employs augmented Dickey and Fuller (ADF), Phillips and Perron (PP) and Kwiatkowski, Phillips, Schmidt and Shin (KPSS) tests to check stationarity in wheat prices. Second, Johansen's cointegration test is applied to assess the integration of wholesale prices between selected pairs of wheat markets to determine long-run relationship among them. Third, Granger casualty test is used to find the direction of causality between the wheat market pairs. Finally, threshold vector error correction model (TVECM) and likelihood ratio (LR) tests are employed to examine long-run adjustment of prices towards the equilibrium in selected wheat markets.

Since wheat wholesale prices for the selected markets are found to be integrated of the order one, that is [I(1)], Johansen's test of cointegration is employed and its findings reveal that the selected wheat market pairs exhibit cointegration and show a long-run price association among themselves. There exists a bi-directional causality among the wheat market pairs. Since LR test is in favor of threshold model (except for Etawah–Delhi pair), one and two threshold models were also performed accordingly. Findings show that wholesale prices of wheat in Delhi markets remain higher than the prices of all other regional markets as regional markets are found to adjust their prices towards Delhi market. Distance of the wheat markets from each other is directly associated with threshold parameters, which are analogous to the transaction costs. Geographically dispersed wheat markets incorporate high transaction and vice versa.

The study argues that there is need to improve rural infrastructure and connectivity of the agricultural markets and remove market asymmetries through unified market regulating mechanisms across the states. This will enable price adjustment process from primary wholesale markets (in production regions) to the secondary wholesale markets (in scarcity regions) quickly.

The contribution of the study in the existing literature lies in the fact that there are no empirical evidences in the context of India that use price transmission as a tool of market integration among spatially separated wheat markets using TVCEM as this model examines role of transaction costs in efficient functioning of the agricultural markets.

The purpose of this research is to systematically scrutinize the influence of macroeconomic determinants on trade openness, through the lens of various trade theories, with a particular focus on the economies of the GIPSI countries – Greece, Ireland, Portugal, Spain and Italy.

This study investigates the macroeconomic factors influencing trade openness in the GIPSI economies from 1995 to 2020. Methods include stepwise regression (SR) for model selection, Pedroni panel cointegration test and panel regression results. The analysis uses advanced panel regressions, including FMOLS, Panel OLS and FEM. The long-term dynamics were tested using Pedroni cointegration, while Granger causality testing was used to examine the causal direction between the trade openness ratio and trade determinant.

The results show both long-term and short-term relationships between trade openness and (1) foreign direct investment, (2) labor force participation rate, (3) trade reserves and (4) trade balance. The researchers also detected unidirectional and bidirectional causality relationships between trade openness and these four factors. The study also revealed that trade reserves (TR) emerge as the most influential determinant of trade openness, and per capita income does not exhibit economic significance concerning the trade openness of GIPSI economies.

This research is conducted within the context of the GIPSI nations (Greece, Ireland, Portugal, Spain and Italy). As such, the outcomes may not be universally applicable to other economic systems due to the distinct institutional settings and governance structures across different economic groups. Future investigations may explore the relationship between trade openness and its determinants by incorporating different variables.

To the best of the authors' knowledge, this is the first study investigating the theory that suggested trade drivers drive the trade openness of GIPSI countries context. By focusing on GIPSI countries, the study offers a unique perspective on the dynamics of trade openness in economies that have experienced financial crises and stringent austerity measures.

The purpose of this paper is to investigate the short and long run causal relationship between stock market development and economic growth in sub-Saharan Africa within the period 1990 and 2020.

Using panel data from 1990–2020 obtained from the World Bank development indicators, the study makes use of the autoregressive distributed lag model and the Granger causality and cointegration to analyze the long and short run causal relationship between stock market development and economic growth in sub-Saharan Africa.

The findings unveiled that stock market capitalization had a positive and significant effect on economic growth in the long run and a negative insignificant effect in the short run within the period of 1990–2020 while stock market liquidity measured through total value of shares traded and turnover ratio had a negative and significant effect on economic growth in sub-Saharan Africa within the period of 1990–2020. The Granger causality test showed an inconclusive result between stock market development and economic growth; implying that the authors cannot say if it is stock market development that causes economic growth or it is economic growth that causes stock market development within the period of 1990–2020.

The findings suggest that governments of sub-Saharan African countries should encourage stock market development by implementing favorable rules for companies listing on their stock market, promote stock market integration with world markets to diversify risk, increase public awareness on stock markets, increase investors' confidence level and finally, remove stock market impediments like high taxes, legal and regulatory barriers to its development.

This study contributes to the existing literature by offering a whole new perspective on stock market development and economic growth since its conception in sub-Saharan Africa. Again, contrary to other papers, the study show how stock market development can contribute to the growth of sub-Saharan Africans’ economy.

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.

This paper explores whether data back the claim that imports of armaments are inherently bad for economic growth. Regardless of one's point of view, the production and trade of weaponry is a significant industry with serious economic implications that warrant investigation. The financial repercussions of military spending have been extensively studied, but the economic effects of arms importation remain unknown.

This study adopts a pooled mean group approach to investigate the nexus between arms imports, military expenditure and per capita GDP for a balanced panel of twenty-five of the top arms importers in the world from 2000 to 2021.

The authors find that arms imports and military spending negatively impact GDP per capita in the short run, but military spending is beneficial over the long run. The authors also used the Dumitrescu Hurlin Granger causality test, which revealed a unidirectional causation between per capita GDP and military expenditure, and a unidirectional causal relationship from military spending to arms imports.

This paper is deficient in a few aspects: first, it looks at only those countries comprising the top 70% of arms imports. Second, it omits many political, technological and legal factors that impact arms imports and military expenditures.

This paper looks into the impact of defense spending and arms imports on economic growth for twenty-five nations with the highest share of arms imports in recent times. It is a significant addition to the literature as it resolves the debate of whether or not the military expenditure is wasteful and whether arms imports significantly harm the nation's economic growth.

This paper examines the long-run and dynamic causal relationship among air pollution, health expenditure and economic growth in Mexico, Indonesia, Nigeria and Turkey (MINT countries).

The bounds test approach to cointegration and causality test was employed on data covering 1995–2018.

The study shows evidence of a long-run relationship among the variables in MINT countries and the causality test confirms the existence of a bidirectional causal nexus between health expenditure and economic growth in the four countries. It also confirms that there is a bidirectional causal relationship between carbon dioxide (CO₂) emission and economic growth, except in Nigeria where a unidirectional causal relationship was found running from CO₂ emissions to economic growth. In addition, a bidirectional causal relationship was found between air pollution and health expenditure in Turkey, while no causal relationship was found among these variables in Nigeria.

This study is limited by available data and it only focuses on four emerging economies. To address this, future studies can expand this scope to more emerging economies with severe air pollution and also extend the scope when more recent data becomes available.

This study suggests that pollution standards in MINT countries should be monitored and enforced with transparency so as to mitigate its health implications and ensure the sustainability of economic growth.

The study confirms the importance of keeping air pollution as low as possible because of its negative effect on health and economic output.

The study accounts for the complexity of each MINT country instead of providing a general discussion on the relationship between air pollution, health expenditure and economic growth in MINT countries.

The purpose of the paper is twofold: first, to observe the relationship between sale to list ratio (SLR)/ for-sale inventory (FSI)/ sale count nowcast (SCN) and real/nominal housing value, and second, to produce a handbook of empirical evidence that can serve as a foundation for future research on this topic.

This paper broadly compiles empirical evidence, using three of the most common causality tests in the field of housing economics. The analysis methods include lagged Pearson correlation test, Granger causality test and cointegration test.

Causal relationships were observed between SLR/FSI/SCN and both nominal and real housing values. SLR and SCN showed positive long-term correlations with housing value, whereas FSI had a negative correlation. Adjusting the housing value with the Consumer Price Index (CPI) to derive real housing values could potentially alter the direction of the causal relationships. It is crucial to distinguish the long-term relationship from temporal dynamics, as FSI displayed a positive immediate impulse–response relationship with nominal housing price despite the negative long-term correlation.

SLR/FSI/SCN are housing market parameters that have only recently begun to be documented and have seen little use in research. So far, housing market research has revolved around traditional macroeconomic indicators such as unemployment rate. To the best of the authors’ knowledge, this study is one of the first studies that introduce these three parameters into housing market research.

This study aims to explore the long- and short-run effects of daily confirmed cases of COVID-19 (C_t) on daily stock returns (R_t) for Kuwait. This is the first study that was applied to the case of Kuwait.

We employed the autoregressive distributed lag (ARDL) model of Pesaran et al. (2001) and the nonlinear autoregressive distributed lag (NARDL) model of Shin et al. (2001) for daily data over the period March 2020 to August 2021.

The findings first document the existence of a long-run relationship (cointegration). Second, the findings of the ARDL model show a significant positive long-run effect of daily confirmed cases of COVID-19 (C_t) on daily stock returns (R_t) but a significant negative short-run effect. As for the NARDL model, the findings showed that the increase and decrease of daily confirmed cases of COVID-19 (Ct−1+,Ct−1−) have symmetric long-run effects on daily stock returns but asymmetric short-run effects. Finally, the vector error correction model causality test shows significant long- and short-run unidirectional causality running from daily confirmed cases of COVID-19 (C_t) to daily stock returns (R_t).

To the best of the author’s knowledge, this is the first study that was applied to the case of Kuwait.

The main purpose of this paper is to examine the role of population aging in determining the health care expenditure (HCE) in India over the period 1981 to 2018.

While establishing the linkage between population aging and HCE, the study has used economic growth, urbanization and CO2 emissions as control variables and used the autoregressive distributed lag (ARDL) approach to cointegration and VECM based Granger causality approach to estimate both the long-run and short-run relationships among the variables.

The results of the ARDL bounds test showed that there is a stable and long-run relationship among the variables. The long-run and short-run coefficients reveal that population aging and income per capita exert a statistically significant and positive effect on per capita HCE in India. The VECM causality evidence shows that there is a presence of short-run causality from economic growth and population aging to per capita HCE, urbanization to environmental degradation and further from aging to urbanization. However, the long-run causality evidence confirms unidirectional causality from population aging to the per capita HCE.

The research findings could be improved by considering the changes in mortality rate over time because of other environmental factors such as air pollution, among others as control variables. Various other variables affecting the health of an aged person could be considered for better research outcome which is not included in the present study because of the paucity of data. However, the present research findings would certainly serve effective policy instrument aiming at maximizing health gains that are highly associated with the elderly population and economic growth towards achieving sustainable development in India.

The uniqueness of the present study lies in its estimation where the relationship between population aging and HCE is looked at while considering the impact of other environmental factors separately. The causal relationship is shown among the variables using updated econometrics time-series techniques. The study tried to resolve the ambiguity associated with the relationship between aging and HCE at a macro level.