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This study aims to assess how real income, oil prices and gold prices affect housing prices in China from 2010 to 2021.

This study uses a novel bootstrap autoregressive distributed lag (ARDL) testing to empirically analyze the short and long links among the tested variables.

The ARDL estimations demonstrate a positive impact of oil price shocks and real income on housing market prices in both the phrases of the short and long run. Furthermore, the results reveal that gold price shocks negatively affect housing prices both in the short and long run. The result can be attributed to China’s housing market and advanced infrastructure, resulting in a drop in housing prices as gold prices increase. Additionally, the prediction of housing market prices will provide a base and direction for housing market investors to forecast housing prices and avoid losses.

To the best of the authors’ knowledge, this is the first attempt to analyze the effect of gold price shocks on housing market prices in China.

Personal remittance and ready-made garments (RMG) export incomes have emerged as the largest source of foreign income for Bangladesh's economy. The study investigates their impact on income inequality and gross domestic product (GDP) as a control variable, using time-series yearly data from 1983 to 2018.

It employs the Autoregressive Distributed Lag (ARDL) estimation and the Toda-Yamamoto (T-Y) causality approach. The ARDL estimation outcomes confirm a long-run association among the above variables and validate the autoregressive characteristic of the model.

Personal remittances positively contribute to reducing the income gap among the people of the society and declining income inequality. In contrast, RMG export income and economic growth contribute to further income inequality. The T-Y causality analysis follows the ARDL estimation outcomes and authenticates their robustness. It reveals a feedback relationship between remittance inflow and the Gini coefficient, unidirectional causalities from RMG export income to income inequality and economic growth to income inequality.

The finding has important policy implications to limit the income gaps between low and high-income groups by channeling incremental income to the lower-income group people. The policymakers may facilitate further international migration to attract further remittances and may upgrade the minimum wage of the RMG workers.

The study is original. As far as the authors' knowledge goes, this is a maiden attempt to investigate the impact of personal remittances and RMG export income on income disparity in the case of Bangladesh.

This study attempts to come in help for Morocco by investigating rigorously the linkage between environmental degradation, measured by ecological footprint (EF), and the gross domestic product growth (EG), the human capital (HC) index and the natural resources (NR) depletion over the period of 1980:Q1 to 2021:Q1. The paper examines the validity of environmental Kuznets curve (EKC) hypothesis in the Moroccan context.

Unlike previous studies, which are based only on the autoregressif dynamic linear (ARDL) model, this paper investigates two recent models: the novel DYNARDL simulation approach and the Kernel-based regularized least squares (KRLS) technics and uses in addition the frequency domain causality (FDC) test.

Models output say a significant and negative association between HC and the EF and a significant and positive interplay between economic growth and environmental quality in the long term. In the short term, findings reveal a significant and negative association between NR and the EF. Based on the FDC test, results conclude about a unidirectional causality from NR to the EF in short-, medium-, and long-term. Moreover, results validate the EKC hypothesis for the Moroccan environment sustainability.

In this study, the researchers use the “ecological footprint” as dependent variable to obtain more accurate and comprehensive assessment of environmental deterioration. Based on time series data investigations, this study is the first paper, which validates the EKC hypothesis and develops important policy implications for Morocco context to achieve sustainable development targets.

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 explore the efficacy of government policy directions in mitigating the effects of the COVID-19 pandemic by employing a panel of 22 countries throughout the 2020-second quarter of 2022.

The panel autoregressive distributed lag (ARDL) model is employed to examine this phenomenon and to investigate the long-run effects of government policy decisions on infection and mortality rates from the pandemic.

The study reveals the following key findings: (1) Income support and debt relief facilities and stringent standards of governments are associated with reduced infection and death rates. (2) The response of governments has resulted in decreased mortality rates while simultaneously leading to an unexpected increase in infection rates. (3) Containment and healthcare practices have led to a decrease in infection rates but an increase in mortality rates, presenting another counterintuitive outcome. Despite the expectation that robust government responses would decrease infection rates and that healthcare containment practices would reduce mortality, these results highlight a lack of health equity and the challenge of achieving high vaccination rates across countries.

To effectively combat the spread of COVID-19, it is crucial to implement containment health practices in conjunction with tracing and individual-level quarantine. Simply implementing containment health measures without these interconnected strategies would be ineffective. Therefore, policy implications derived from containment health measures should be accompanied by targeted, aggressive, and rapid containment strategies aimed at significantly reducing the number of individuals infected with COVID-19.

This study concludes by suggesting the importance of implementing economic support in terms of income, and debt relief has played a crucial role in mitigating the spread of COVID-19 infections and reducing fatality rates.

To effectively combat the spread of COVID-19, it is crucial to implement containment health practices in conjunction with tracing and individual-level quarantine. Simply implementing containment health measures without these interconnected strategies would be ineffective. Therefore, policy implications derived from containment health measures should be accompanied by targeted, aggressive, and rapid containment strategies aimed at significantly reducing the number of individuals infected with COVID-19.

This research makes a unique contribution to the existing literature by investigating the impact of government responses on reducing COVID-19 infections and fatalities, specifically focusing on the period before COVID-19 vaccinations became available.

This study examines the possibility of asymmetric impact of inflation on the financial development (FD) in the case of Indian economy from 1980 to 2020. Moreover, the finance–growth hypothesis is also tested.

The authors incorporated the “Nonlinear Autoregressive Distributed Lag” (NARDL) model due to Shin et al. (2014) to investigate the asymmetric impact of inflation on financial development. Asymmetric cumulative dynamic multipliers are also used to track the traverse of any short-run distortion towards the long-run cointegration.

The results revealed that inflation impacts the financial development negatively whereas the economic growth (EG) and trade openness have a positive effect. However, the effect of inflation on financial development is not symmetric. Moreover, the findings support the demand-led growth hypothesis.

To the best of the authors' knowledge, this is the first study examining the asymmetric effects of inflation on financial development in the Indian context. In addition, instead of using a single proxy to measure financial development, an index for financial development encompassing different aspects of the financial system has been incorporated.

The peer review history for this article is available at: https://publons.com/publon/10.1108/IJSE-02-2023-0094

Today, the increasing use of fossil fuels, energy security, concerns and the great importance of achieving sustainable economic growth underscore the urgent need to transition to a green energy system as soon as possible. To shed light on the relationship between the economy and renewable energy, this study assesses the nonlinear relationship between renewable energy consumption and economic growth for 24 OECD countries between 1990 and 2015.

The authors apply two nonlinear models: panel threshold regression (PTR) and panel smooth transition regression (PSTR).

The results show that the positive effect of renewable energy consumption on economic growth is conditional. On the one hand, the results of the nonlinear PTR model yielded a threshold value for renewable energy consumption of about 251.17. Below this threshold, the authors find a negative impact of renewable energy consumption on economic growth. However, above this threshold, renewable energy consumption becomes a favorable source of economic growth. Using the nonlinear PSTR model based on the gamma transition parameter of 2.014, the transition from low renewable energy consumption regime to higher is abrupt.

Referring to previous studies analyzing linear causality between renewable energy and economic growth, most of the results show various mixed and non-stable effects over the study period. The contributions of this study consist in conduct a series of empirical tests of the nonlinear effects of renewable energy use on economic growth using two nonlinear approaches such as the PTR and PSTR models. If the authors show that such a relationship is nonlinear, it is essential to check whether the transition from one weak regime to another strong regime is abrupt or smooth, using the PSTR approach.

The paper investigates the dynamic relationship between oil prices, the USA dollar exchange rate and the Saudi stock market index.

The authors perform a novel dynamic simulated the autoregressive distributed lag (ARDL) on weekly data from 2010 to 2021.

The authors' work reveals three main results: First, a cointegration relationship exists between oil prices and the Saudi stock market index. Second, the Saudi stock market is strongly affected by fluctuations in oil prices in both the short and long run. Third, the exchange rate of the USA dollar has a slight influence on the movements of the Saudi stock market. The simulations show that the Saudi stock market index has a long-run upward trend after an oil price shock, while the dollar index rises moderately after a similar shock. Moreover, the first months of the COVID-19 pandemic coincided with a significant decline in the Saudi stock market index, particularly the substantial drop in oil prices.

These findings encourage domestic and foreign investors to benefit from an upward trend in oil prices, especially after the opening of the Saudi market to foreign investment. On the other hand, it raises questions about the Saudi economy's dependence on oil as the sole vehicle for output growth. It highlights the urgent need for diversification and productivity growth in the non-oil sector and other renewable natural resources to increase Saudi competitiveness.

The novelty of the research lies in the following. First, the authors apply one of the latest developments in time-series modeling techniques. This dynamic ARDL simulation model provides a worthwhile alternative way to explore dynamic correlations in the short and long run and assess the choc effects. Secondly, the study would enable us to track the impact of the COVID-19 health crisis on the Saudi stock market.

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.

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.