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This study aims to examine the symmetric and asymmetric impact of external debt on inflation in Sudan from 1970 to 2020 within a multivariate framework by including money supply and the nominal effective exchange rate as additional inflation determinants.

The authors utilize an Auto Regressive Distributed Lag (ARDL) model to examine the symmetric impact of external debt on inflation, while the asymmetric impact is examined using a Nonlinear ARDL (NARDL) model. The existence of a long-run relationship between inflation and external debt is tested using the bounds-testing approach to cointegration, and a vector error-correction model is estimated to determine the short parameters of equilibrium dynamics.

The linear ARDL model results show that external debt has no statistically significant impact on inflation in the long run. On the contrary, the results of the NARDL model show that positive and negative external debt shocks statistically affect inflation in the long run. The estimated long-run elasticity coefficients of the linear and nonlinear ARDL models reveal that the domestic money supply has a statistically significant positive impact on inflation. In contrast, the nominal effective exchange rate has a statistically significant negative impact on inflation.

The reliance on symmetric analysis may not be sufficient to uncover the existence of a linkage between external debt and inflation. Proper external debt management is crucial to control inflation rates in Sudan.

To date, no empirical study has assessed the external debt-inflation nexus and its potential asymmetry in Sudan, and the current study aims to fill this gap in the literature.

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.

The purpose of this study is to determine the Turkish housing price and rent dynamics among seven big cities with a unique monthly data set over 2003–2019. The secondary purpose is to examine bubble dynamics within the price convergence framework through alternative tests.

The paper conducts two autoregressive distributed lag (ARDL) cointegration estimates for housing prices and rents and applies conditional error correction model to investigate the long-run drivers of the Turkish housing market. The authors compare ARDL cointegration in-sample forecasts and discounted cash flow (DCF) estimates with actual prices to determine the timing, magnitude and collapse period(s) of bubbles within the price convergence framework. In particular, the generalized sup augmented Dickey–Fuller (GSADF) approach time stamps multiple explosive price behaviors.

The ARDL results confirm the theory of investment value by addressing mortgage rates, the price-to-rent ratio and rents as the fundamental factors of house prices. The price-to-rent ratio offers a comparison mechanism among houses deciding to buy a new house in which rents increase monthly real estate investment returns, and mortgage rates act as the discount rate. One key finding is that these dynamics have a greater impact on house prices than mortgage rates. Furthermore, the ARDL, DCF and GSADF findings exhibit temporal overvaluations rather than bubble signals, implying that housing price appreciations, including explosive behaviors, are consistent with fundamental advances.

This paper is considered to be innovative in determining housing market dynamics through two different ARDL estimates for the Turkish housing price index and rents in real terms as dependent variables. The authors compare the boom and collapse periods of the real housing price index and its fundamentals via the GSADF test. A final key feature of this research is its extensive data set, with 11 different regressors between 2003 and 2019.

The purpose of this paper is to focus on modeling economy growth with indicators of knowledge-based economy (KBE) introduced by World Bank for a case study in Iran during 1993-2013.

First, for grouping and reducing the number of variables, Tukey method and the principal component analysis are used. Also for modeling, 67 per cent of data is used for training in the two approaches of ARDL bounds testing and gene expression programming (GEP) and 33 per cent of them for testing the models. Then, the result models are compared with fitness function and Akaike information criteria (AIC).

The GEP model with fitness 945.7461 for training data and 954.8403 for testing data from 1000 is better than ARDL bounds testing model with fitness 335.5479 from 1000. In addition, according to model comparison tools (AIC), the GEP model has an extremely larger weight in comparison with ARDL bounds model. Therefore, the GEP model is introduced for future use in academia.

Knowledge and information is one of the most basic sources of wealth in economists’ sight. Thus, using KBE indicators appears essential in economic growth regarding daily progress in knowledge processes and its different theories. It is also extremely important to determine an appropriate model for KBE indicators which play a highly important role in the allocation of the economic resources of the country in an optimal manner.

This paper introduced a novel expression for economy growth using KBE indicators. All the data and the indicators are extracted from Word Bank service between 1993 and 2013.

This paper aims to test whether a debt threshold of public debt has any effect on economic growth in Africa.

The authors applied the panel autoregressive distributed models on 38 African countries with annual data from 1970 to 2015. It was established that the threshold and the trajectory of debt has an impact on economic growth.

Specifically, the authors found that public debt hampers economic growth when the depth is in the region of 20 to 80 per cent of GDP. Based on debt trajectory, this study established that increasing public debt beyond 50 to 80 per cent of GDP adversely affects economic growth in Africa. The study also finds that the persistent rise in debt also has adverse effect on economic growth in the African countries in the sample. It must be known to policymakers that the threshold of debt in developing countries, and for that matter African countries, are less than that of developed countries.

This study suggests threshold effects between 20 and 50 per cent; this should be a guide for policymakers in the accumulation of debt stock. Interestingly, the findings suggest some debt trajectory effect, which policymakers might consider by increasing efforts to reduce debt levels when they fall between 50 to 80 per cent of GDP. This implies that reducing such debt levels can help African countries increase their economic growth.

The study is unique because it seeks to add new evidence on the relationship between public debt and growth in the African region, by considering the impact of the persistent growth of public debt on economic growth.

One of the most important ways to pay attention to sustainable economic development is to invest in green technology and alter the energy consumption structure (ECS) in countries. Changing the ECS can be important in two ways: first, it increases the diversity of energy consumption and reduces energy dependence on other countries. Second, the use of highly polluted nonrenewable energy sources (such as oil and coal) is reduced, leading to the transfer of energy to natural gas with less carbon emissions or renewable energy. To this end, the authors examined the asymmetric effects of eco-innovation on the US ECS from 1980 to 2019. This paper aims to address this issue.

In this research, the nonlinear autoregressive distributed lag (ARDL) (NARDL) model is used and the results are compared with the linear ARDL model.

The ARDL results also confirm the positive effects of oil prices and GDP per capita in the long run. On the other hand, short-term and long-term Wald test results confirm the nonlinear effects of eco-innovation (LPATENT) on US ECS. These results indicate that 1% positive shock in LPATENTˆ+ increases the ECS by 0.179, while 1% negative fluctuations (LPATENTˆ-) leads to a decrease (−0.085) in the ECS. However, the ARDL results, in general, show the positive effects of LPATENT on the ECS in long run. Evidence suggests that ignoring nonlinear effects can lead to inaccurate results. Policy suggestions for environmental technology innovation are presented in the results.

This research has innovations in various aspects so that the previous studies in this field have examined the effects of environmental innovation on renewable or nonrenewable energy consumption, and so far no study has been done on the ECS. In this research, the Shannon–Wiener index has been used to calculate the ECS.

The objective of this study is to explore the dynamic inter-linkage between foreign aid and economic growth in Sri Lanka over the period of 1960–2018.

Both exploratory and inferential data analysis tools have been employed to examine the objective of this study. The exploratory data analysis covered the scatter plots, confidence ellipse with kernel fit. The inferential data analysis included the augmented Dickey–Fuller (ADF) and Phillips–Perron (PP) unit root tests, the autoregressive distributed lag (ARDL) Bounds co-integration technique and the Granger causality test.

The test result of exploratory data analysis indicates that there is a positive relationship between foreign aid and economic growth. The ADF and PP unit root tests results indicate that the variables used in this study are stationary at their 1st difference. The co-integration test result confirms the presence of long-run relationship between foreign aid and economic growth in Sri Lanka. The estimated coefficient of foreign aid in the long-run and the short-run shows that foreign aid has a positive relationship with economic growth in Sri Lanka. The estimated coefficient of error correction term indicates that approximately 26.6% of errors are adjusted each year and further shows that the response variable of economic growth moves towards the long-run equilibrium path. The Granger causality test result shows that foreign aid in short-run Granger causes economic growth in Sri Lanka which means that one-way causality from foreign aid to economic growth is confirmed. Further, the estimated coefficient of error correction term confirms that there is the long-run Granger causal relationship between foreign aid and economic growth in Sri Lanka.

The findings of this study have some important policy implications for the design of efficient policy related to foreign aid and economic growth, the knowledge of which will help follow sustainable foreign aid and growth nexus.

This study contributes to the existing literature by using the newly introduced ARDL Bounds cointegration technique to investigate the dynamic inter-linkage between foreign aid and economic growth in Sri Lanka.

The purpose of this research is to examine the validity of the agriculture-induced environmental Kuznets curve (EKC) hypothesis with evidence from an autoregressive distributed lag (ARDL) approach with a structural break including real income and energy consumption in the model for Ghana over the period 1980–2014.

The ARDL approach with a structural break was used to analyze the agriculture-induced EKC model which has not been studied in Ghana. The dynamic ordinary least squares (DOLS), canonical cointegration regression (CCR) and fully modified ordinary least squares (FMOLS) econometric methods were further used to validate the robustness of the estimates, and the direction of the relationship between the study variables was also clarified using the Toda–Yamamoto Granger causality test.

The ARDL results revealed that GDP, energy consumption and agricultural value added have significant positive effects on CO₂ emissions, while GDP² reduces CO₂ emissions. The Toda-Yamamoto causality test results show a bidirectional causality running from GDP and energy consumption to CO₂ emissions whereas a unidirectional long-term causality runs from GDP² and agriculture value-added to CO₂ emissions.

This finding validated the presence of the agriculture-induced EKC hypothesis in Ghana in both the short run and long run, and the important role of agriculture and energy consumption in economic growth was confirmed by the respective bidirectional and unidirectional causal relationships between the two variables and GDP. Thus, a reduction in unsustainable agricultural practices is recommended through specific policies to strengthen institutional quality in Ghana for a paradigm shift from rudimentary technology to modern sustainable agrarian technologies.

This study is novel in the EKC literature in Ghana, as no study has yet been done on agriculture-induced EKC in Ghana, and the other EKC studies also failed to account for structural breaks which have been done by this study. This study further includes a causality analysis to examine the direction of the relationship which the few EKC studies in Ghana failed to address. Finally, dynamic ordinary least squares (DOLS), canonical cointegration regression (CCR) and fully modified ordinary least squares (FMOLS) methods are used for robustness check, unlike other studies with single methodologies.

The Global recession of 2008 was the worst financial crisis in the postworld war economic history that brought in severe disruptions in global investments and capital flows. Not surprisingly, research interest in the field of market integration has considerably increased over the last decade. This paper analyses the dynamics of price integration among Asian financial markets during the postfinancial crisis period.

We employ an Autoregressive Distributed Lag (ARDL) bounds testing approach to cointegration and a Granger Causality/Block Exogeniety test from a Vector Error Correction Model (VECM) on monthly stock index data of five leading Asian economies from April 2009 to March 2020.

The cointegration results could not produce any conclusive evidence of long-run relations between stock markets. There exists weak price convergence among markets, and financial integration is partial and in an imperfect form.

Stock price performance in China is closely “coupled” with that in India, but both markets appear to be the short-run predictors of Asian stock returns. The research uses only the benchmark stock indices of the selected economies. Consideration of mid-cap and small-cap segments where foreign investments are significant today can validate the findings further.

The asymmetric pattern of price behavior of Asian markets has important implications for the pricing efficiency of national markets and offers arbitrage potentials for global investors to optimize returns through market diversifications on a long-term perspective. The finding definitely will be a great help to investors who are potentially interested in a trading strategy that offers greater returns with limited exposure to market risks.

Compared with previous studies, the research uses the most recent data of leading Asian markets and applies the robust method of ARDL Bounds testing approach that allows us to understand better if the economic recoveries and advancement have had an effect on market coupling and stock price transmissions.

The purpose of this study is to analyze the determinants of changes in carbon dioxide (CO₂) emissions for Turkey by utilizing the autoregressive distributed lag approach to investigate the long-run equilibrium relationships of CO₂ emissions between foreign tourist arrivals (FTAs) and electricity consumption (ELC). The results reveal that foreign tourists and ELC are significant determinants of a long-run equilibrium relationship with CO₂ emissions from electricity and heat production and CO₂ emissions from transport for Turkey, respectively. The results of the conditional error correction models (CECM) confirm that there are long-run causal relationships from the growing number of foreign tourist arrivals and the increase of ELC toward the growth of CO₂ emissions during 1960-2010. The results of autoregressive distributed lag (ARDL) error correction models for CO₂ emissions also validate significant dynamic relationships between CO₂ emissions, ELC and tourist arrivals in the short run.

ARDL modeling and Bounds test approach were used in this study.

Rapid tourism development in Turkey has triggered CO₂ emissions. The growth of CO₂ emissions in Turkey threatens sustainability. The hypothesis of “The growth of CO₂ emissions in Turkey” is validated. Tourist arrivals, ELC and CO₂ emissions are co-integrated. CECMs confirm the growth of CO₂ emissions during 1960-2010. ARDL modeling shows significant relationships between CO₂ emissions and other variables.

Results of ARDL error correction models for CO₂ emissions validate the hypothesis that there are significant dynamic relationships between CO₂ emissions, ELC and tourist arrivals in Turkey for the short run.