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This study aims to assess the nexus among oil price (OP), renewable energy consumption (REC) and trade balance (TB) for India using annual time series data for the time period 1985–2019. In particular, the authors examine whether REC improves India's TB in the context of high oil import dependence.

The study uses autoregressive distributed lags (ARDL) bound testing approach that has the advantage of yielding estimates of long-run and short-run parameters simultaneously. Moreover, the small sample properties of this approach are superior to other multivariate cointegration techniques. Fully modified ordinary least square (FMOLS) and dynamic ordinary least squares (DOLS) are also applied to test the robustness of the results. The causality among the series is investigated through block exogeneity test based on vector error correction model.

The findings based on ARDL bounds testing approach indicate that OPs exert a negative impact on TB of India in both long run and short run, whereas REC has a favorable impact on the TB. In particular, 1% increase in OPs decreases TBs by 0.003% and a 1% increase in REC improves TB by 0.011%. The results of FMOLS and DOLS corroborate the findings from ARDL estimates. The results of block exogeneity test suggest unidirectional causation from OPs to TB; OPs to REC and REC to TB.

The study underscore the importance of renewable energy as a potential tool to curtail trade deficits in the context of Indian economy. Our results suggest that the policymakers must pay attention to the hindrances in augmentation of renewable energy usage and try to capitalize on the resulting gains for the TB.

Climate change is a major challenge for developing countries like India. Renewable energy sector is considered an important instrument toward attaining the twin objectives of environmental sustainability and employment generation. This study underscores another role of REC as a tool to achieve a sustainable trade position, which may help India save her valuable forex reserves for broader objectives of economic development.

To the best of the authors’ knowledge, this is the first study that probes the dynamic nexus among OPs, REC and TB in Indian context. From a policy standpoint, the study underscores the importance of renewable energy as a potential tool to curtail trade deficits in context of India. From a theoretical perspective, the study extends the literature on the determinants of TB by identifying the role of REC in shaping TB.

Despite the huge financial resources associated with oil, Nigeria has consistently recorded poor growth performance. Therefore, this study aims to examine how corruption and oil rent influence Nigeria’s economic performance during the 1996–2021 period.

Various estimation techniques were used. These include the bootstrap autoregressive distributed lag (ARDL) bounds-testing, dynamic ordinary least squares (DOLS), the fully modified OLS (FMOLS) and the canonical cointegration regression (CCR) estimators and the Toda–Yamamoto causality.

The bounds testing results provide evidence of a cointegrating relationship between the variables. In addition, the results of the ARDL, DOLS, CCR and FMOLS estimators demonstrate that oil rent and corruption have a significant positive impact on growth. Further, the results indicate that human capital and financial development enhance economic growth, whereas domestic investment and unemployment rates slow down long-term growth. Additionally, the causality test results illustrate the presence of a one-way causality from oil rent to economic growth and a bi-directional causal relationship between corruption and economic growth.

Existing studies focused on the effects of either oil rent or corruption on growth in Nigeria. Little attention has been paid to the exploration of how the rent from oil and the pervasiveness of corruption contribute to the performance of the Nigerian economy. Based on the outcome of this study, strategies and policies geared towards reducing oil dependence and the pervasiveness of corruption, enhancing human capital and financial development and reducing unemployment are recommended.

This study empirically aims to examine the relationship between Bangladesh’s pharmaceutical industry growth and macroeconomic indicators such as the inflation rate, gross domestic product (GDP) growth, foreign direct investment (FDI) inflows, exchange rate and export growth through the long- and short-run relationship.

Using the time series data from 1986 to 2020, this study was developed based on the autoregressive distributed lag (ARDL) framework for co-integration. In contrast, the Toda–Yamamoto Granger Causality approach was also used for finding the direction of causality.

This study used the ARDL bounds test, which found strong co-integration among the variables, indicating a long-term relationship between them. In the long run, inflation, exchange rate and export growth significantly positively influence the pharmaceutical industry’s growth. Surprisingly, an FDI inflow has a negative impact. In the short term, the exchange rate and GDP growth were found to influence the growth of the pharmaceutical industry positively. Bidirectional causality between the growth of the pharmaceutical industry and the exchange rate was also identified using the Granger causality approach.

This paper emphasizes developing the policy as well as making concrete decisions regarding the development of the pharmaceutical industry and economic development in Bangladesh. The results also highlight the necessity for strategic macroeconomic management to support this sector’s long-term development and global competitiveness.

To the best of the authors’ knowledge, this paper is conducted to identify the short- and long-run relationship of pharmaceutical industry development with the economic indicators and progress, where no study has been found on this dimension.

Despite the vulnerability of rapidly developing and emerging market economies, researchers have paid less attention to the determination of the size of money laundering (ML) in these economies, including the United Arab Emirates (the UAE). Therefore, this paper aims to estimate the magnitude of ML in the UAE between 1975 and 2020 based on the currency demand approach (CDA).

The study uses the Gregory–Hansen cointegration technique alongside the autoregressive distributed lag bounds testing procedure to estimate the CDA model.

The results illustrate that an amount equivalent to about 19.034% of the GDP is laundered in the UAE between 1975 and 2020, on average, with the value lying between 15.129% and 23.121%. In addition, the results demonstrate the importance of the real estate market, gold trade, remittance channels and the size of the underground economy in facilitating the laundering of illicit funds in the country.

To the best of the authors’ knowledge, the study is the pioneering attempt at estimating the amount of illicit funds laundered in the UAE. Besides, the adoption of a novel, yet robust, approach based on the modification of the CDA technique also sets the study apart as it ensures a correct, clear, unambiguous and indisputable estimate of the magnitude of ML is obtained. In addition, it is expected that the outcome of the study will expand the frontiers of knowledge among policy makers and relevant agencies and ensure the adoption of the most efficient and effective measures to curb the ML menace in the country.

This study aims to investigate the effect of the tourism sector on employment in 13 Mediterranean countries. In addition, the impact of economic growth and inflation rate, which are included in the analysis as control variables, on the employment rate are investigated.

For this study, data from 1995 to 2018 and the ratio of the employed population, the number of international tourist arrivals, the annual growth rate of real gross domestic product (GDP) and the annual percentage change in the Consumer Price Index (CPI) were used. First, the authors investigated the relationship between variables using the Autoregressive Distributed Lag (ARDL) Bounds Test with Sharp and Smooth Breaks. Then, after determining the significant cointegration relationship, the long-term and short-term coefficients were also estimated.

The results show a cointegration relationship for Cyprus, Greece, Israel, Malta and Tunisia. Tourism demand has a positive effect on all these countries and economic growth positively affects the employment rate only in Greece, Israel and Tunisia. Besides, the inflation rate has a negative effect in Israel and Tunisia and a positive effect in Malta. Overall, the authors' results provide important policy suggestions, such as the training of the employees in the tourism sector should be improved to keep up with the requirements of the times.

The impact of the tourism sector on total employment varies from country to country. In particular, the employment creation policies of the sector need to be changed by taking technological changes into consideration.

Since tourism is a labor-intensive sector, tourism's impact on employment is an important research topic. However, whether this effect applies to all countries is debatable. Furthermore, the development of technology can also reduce employment in labor-intensive sectors. Therefore, this research can be regarded as important as this research addresses such a critical current issue and suggests a novel econometric method such as the ARDL Bounds Test with Sharp and Smooth Breaks.

Although the effects of both news sentiment and expectations on price in financial markets have now been extensively demonstrated, the jointness that these predictors can have in their effects on price has not been well-defined. Investigating causal ordering in their effects on price can further our understanding of both direct and indirect effects in their relationship to market price.

We use autoregressive distributed lag (ARDL) methodology to examine the relationship between agent expectations and news sentiment in predicting price in a financial market. The ARDL estimation is supplemented by Grainger causality testing.

In the ARDL models we implement, measures of expectations and news sentiment and their lags were confirmed to be significantly related to market price in separate estimates. Our results further indicate that in models of relationships between these predictors, news sentiment is a significant predictor of agent expectations, but agent expectations are not significant predictors of news sentiment. Granger-causality estimates confirmed the causal inferences from ARDL results.

Taken together, the results extend our understanding of the dynamics of expectations and sentiment as exogenous information sources that relate to price in financial markets. They suggest that the extensively cited predictor of news sentiment can have both a direct effect on market price and an indirect effect on price through agent expectations.

Even traditional financial management firms now commonly track behavioral measures of expectations and market sentiment. More complete understanding of the relationship between these predictors of market price can further their representation in predictive models.

This article extends the frequently reported bivariate relationship of expectations and sentiment to market price to examine jointness in the relationship between these variables in predicting price. Inference from ARDL estimates is supported by Grainger-causality estimates.

Climate change-related weather events significantly affect rice production. In this paper, we investigate the impact of and interrelationships between agriculture inputs, climate change factors and financial variables on rice production in India from 1970–2021.

This study is based on the time series analysis; the unit root test has been employed to unveil the integration order. Further, the study used various econometric techniques, including vector autoregression estimates (VAR), cointegration test, autoregressive distributed lag (ARDL) model and diagnostic test for ARDL, fully modified least squares (FMOLS), canonical cointegrating regression (CCR), impulse response functions (IRF) and the variance decomposition method (VDM) to validate the long- and short-term impacts of climate change on rice production in India of the scrutinized variables.

The study's findings revealed that the rice area, precipitation and maximum temperature have a significant and positive impact on rice production in the short run. In the long run, rice area (ß = 1.162), pesticide consumption (ß = 0.089) and domestic credit to private sector (ß = 0.068) have a positive and significant impact on rice production. The results show that minimum temperature and direct institutional credit for agriculture have a significant but negative impact on rice production in the short run. Minimum temperature, pesticide consumption, domestic credit to the private sector and direct institutional credit for agriculture have a negative and significant impact on rice production in the long run.

The present study makes valuable and original contributions to the literature by examining the short- and long-term impacts of climate change on rice production in India over 1970–2021. To the best of the authors’ knowledge, The majority of the studies examined the impact of climate change on rice production with the consideration of only “mean temperature” as one of the climatic variables, while in the present study, the authors have considered both minimum as well as maximum temperature. Furthermore, the authors also considered the financial variables in the model.

This paper aims to examine the effects of investor sentiment on the development of the real estate corporate bond market in Vietnam.

The research uses an autoregressive distributed lag (ARDL) model with quarterly data. Additionally, the study employs Google Trends search data (GVSI) related to topics such as “Real Estate” and “Corporate Bond” to construct a sentiment index.

The empirical outcomes reveal that real estate market sentiment improves the growth of the real estate corporate bond market, while stock market sentiment reduces it. Also, there is evidence of a long-run negative effect of corporate bond market sentiment on the total value of real estate bond issuance. Further empirical research evidences the short-term effect of sentiment and economic factors on corporate bond development in the real estate industry.

Due to difficulties in collecting data, this paper has the limited sample of 54 valid quarterly observations. Moreover, the sentiment index based on Google search volume data only reflects the interest level of investors, not their attitudes.

These results yield important implications for policymakers in respect of strengthening the corporate bond market platform and maintaining stability in macroeconomic and monetary policies in order to promote efficient and sustainable market development.

The study offers some suggestions for regulators and governments to improve the real estate corporate bond market.

This is the first quantitative study to examine the effect of sentiment factors on real estate corporate bond development in Vietnam.

This study is intended to analyze the impact of information and communication technology (ICT) infrastructure, technological innovation, renewable energy consumption and financial development on carbon dioxide emissions in emerging economies.

The present study adopts the autoregressive distributed lag (ARDL) cointegration technique for the annual data collection of Vietnam from 1990 to 2020.

The results of the study unveil that renewable energy consumption, the interaction between renewable energy consumption and ICT infrastructure and financial development have significant predictive power for carbon dioxide emissions. In the long term, renewable energy consumption, export and population growth reduce CO2 emissions, whereas the interaction between renewable energy consumption and ICT infrastructure and financial development increases CO2 emissions, while ICT infrastructure does not affect emissions. In the short run, changes in ICT infrastructure contribute to carbon dioxide emissions in Vietnam. In addition, changes in renewable energy consumption, financial development, the interaction between ICT infrastructure and renewable energy consumption and population growth have a significant effect on CO2 emissions. Notably, technological innovation has no impact on CO2 emissions in both the short and long run.

The current study provides new insights into the environmental effects of ICT infrastructure, technological innovation, renewable energy consumption and financial development. The interaction between renewable energy consumption and ICT infrastructure has a significant effect on carbon dioxide emissions. The paper suggests important implications for setting long-run policies to boost the effects of financial development, renewable energy consumption and ICT infrastructure on environmental quality in emerging countries like Vietnam in the coming time.

In this paper we examine the validity of the J-curve hypothesis in four Southeast Asian economies (Indonesia, Malaysia, the Philippines and Thailand) over the 1980–2017 period.

We employ the linear autoregressive distributed lags (ARDL) model that captures the dynamic relationships between the variables and additionally use the nonlinear ARDL model that considers the asymmetric effects of the real exchange rate changes.

The estimated models were diagnostically sound, and the variables were found to be cointegrated. However, with the exception of Malaysia, the short- and long-run relationships did not attest to the presence of the J-curve effect. The trade flows were affected asymmetrically in Malaysia and the Philippines, suggesting the appropriateness of nonlinear ARDL in these countries.

The previous research tended to examine the effects of the real exchange rate changes on the agricultural trade balance and specifically the J-curve effect (deterioration of the trade balance followed by its improvement) in the developed economies and rarely in the developing ones. In this paper, we address this omission.