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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 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.

This study takes a disaggregated approach to investigate the impacts of long-run GDP on changes in total government expenditure in the Eastern Caribbean Currency Union (ECCU) economies. An understanding of the relationship between changes in total government expenditure and GDP (by sector categories) is expected to provide a working tool to understand the growth debt nexus of Caribbean countries. The purpose of the paper is to use an auto regressive distributed lag (ARDL) and error correction model (ECM) to examine and analyse short- and long-run dynamics of disaggregated approach to both output and government expenditure in a dynamic model to identify the growth in the Eastern Caribbean Countries.

In an attempt to examine the long-run dynamics, data for the period 1970-2015 were used in an ARDL and ECM framework. The authors examine the long-run GDP impacts of changes in total government expenditure and in the shares of different spending categories for the ECCU countries to establish and analyse short and long-run dynamics.

The results suggest that total fiscal expenditure and disaggregated expenditure including debt services have both positively and negatively contributed to economic growth in the agriculture, manufacturing and mining sectors. Among others, the study found that high national debt in the region resulted primarily from increases in government expenses and diminishing income sources.

This paper is the first to take a disaggregated approach to investigate the relationship between economic growth and government expenditure in the Eastern Caribbean States. The authors’ empirical results suggest that debt servicing reduces economic growth both in the short and long run. The greatest impact being felt in the mining and manufacturing sectors, namely, 1 per cent increase in debt service will bring about 7.90 and 1.67 per cent decrease in economic growth. These results offer fairly strong support to the view that expenditure share variables can weaken sectoral growth, and hence force the overall growth to decline.

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 paper is to examine the relationship between financial inclusion (FI) and economic growth in India.

To measure FI, a multidimensional time-varying index is proposed following the Human Development Index method. The long-run relationship between FI and economic growth is examined by using the autoregressive distributed lag (ARDL) approach to cointegration and nonlinear ARDL approach. Further, the direction of causality is investigated by employing the Toda–Yamamoto Granger causality test.

The linear cointegration test confirms a long-run relationship between FI and economic growth for India. The improvement in both demand-side and supply-side financial services has a positive impact on economic growth. These results suggest that India can attain long-run economic growth by improving the coverage of FI. However, there is no evidence of nonlinear cointegration, indicating that there is no asymmetric effect of FI on economic growth. Further, the causality test shows that FI granger causes economic growth but not vice versa.

The major limitation of the study is the availability of time series data for all important variables. The index for both demand- and supply-side indicators can be extended with several other important variables in later date once the data are available for those variables.

As the study confirms that FI is one of the main drivers of economic growth, it is suggested that the policy maker emphasizing on financial sector reforms can enjoy economic growth in the long run, especially in developing countries. Therefore, the government and policy makers need to address the issues involved in access to financial services to spur economic growth.

The study examines the long-run relationship between FI and economic growth employing ARDL bound testing approach and nonlinear ARDL approach, separately for demand-side and supply-side indicators. Further, the study uses the Toda–Yamamoto granger causality to find the direction of causal flow between FI and economic growth.

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 purpose of this paper is to find out empirically the relationship between foreign direct investment (FDI) and economic growth and it will also highlight the relationship status between the variables included in the model, either long‐ or short‐run in case of China.

This study uses secondary data obtained from World Development Indicators over the period 1985‐2009, whose viability has also been checked through the World Bank and IFS. An Augmented Dickey‐Fuller (ADF) unit root test is used to estimate an autoregressive distributive lag (ARDL) approach to co‐integration as the variables in the model are in I(1) and I(0) form and the Schwarz Bayesian Criterion (SBC) is used in this study to find out the estimated lags of the model, which are ultimately used to find out the short‐ and long‐run relationship of the variables included in the model. The error correction model (ECM) was also applied which basically provides information about the causal factors that may affect the variables included in the model.

The results provide evidence that there is an empirical relationship among FDI and economic growth. The computed value of F‐statistics is greater than the upper bond value described by Pesaran, M.H. et al., which depicts evidence against the null hypothesis of no effect and hence long‐run relationship among the variables is concluded at bottom line. Empirical evidence reveals that FDI has a positive effect on economic growth. An error correction model (ECM) is applied and the error correction term was negative and significant. This indicates that there exists a relationship between the variables. Diagnostic tests showed a lack of heteroscedisticity, confirming the validity of the model; CUSUM and CUSUMSQ tests were used to reveal the model's stability.

The Government of China should keep keen emphasis on the ingredients of this study so that China could reap maximum share of FDI through the achievement of positive spillovers of foreign investment, which ultimately results in its economic growth. However, the ingredients of this study depict the expenditures on security status, growth options as well as on infrastructure. This study also gives better impending in decision making about FDI in case of China.

This study bridges the gap between theory and practice and proves empirically the relationship between FDI and economic growth through auto regressive distributive lag approach (ARDL) to co‐integration in case of China. This research includes most dominating factors in the model which differentiate it from all previous empirical researches related to FDI's relationship with economic growth. However, this study not only pin points the new dominating factors related to this kind of relationship, but also set up a new horizon in the field of research to get groundbreaking results – in case of other countries – by following the footings set by this research.

Net energy importing countries (NEICs) pursue strategic policies to reduce the consumption of energy from conventional sources and increase that of renewable energy to attain energy security and sustainable development. However, net energy exporting countries (NEECs) rely substantially on the proceeds realised from oil and gas exports to mainly NEICs to finance government activities. This paper aims to investigate the effect of increased consumption of renewable energy in developed NEICs on the Nigeria’s oil and gas exports.

The study was undertaken by analysing macro-economic annual time-series data set (1980-2014) using autoregressive distributed lag (ARDL) bounds testing approach.

Both the short-run and the long-run results of the ARDL modelling reveal that renewable energy consumption in developed NEICs is affecting Nigeria’s oil and gas exports negatively, thereby causing significant decrease in the amounts of revenue being generated therefrom.

Like most empirical studies, the conduct of this research has encountered some challenges. Thus, the use of rather small sample in terms of period covered (1980-2014), annual frequency of data and focus on one NEEC (Nigeria) are the key limitations of this paper. While the first two challenges were dealt with by using ARDL, future research can focus on other NEECs to extend the study.

The findings have several policy implications, including the need for Nigeria to focus on developing internal market trajectories to increase domestic utilisation of its conventional energy rather than depending on external markets. The results also suggest the need for public policymakers to develop a strategic plan that will effectively address the external economic threat arising from the influence of global energy transition.

To the best of the authors’ knowledge, this paper represents the first effort to empirically examine the effect of renewable energy consumption by developed NEICs on the Nigeria’s oil and gas exports. The paper contributes to the literature by providing insight into and documenting evidence that the world is taking transitioning to cleaner energy sources very seriously.

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.

The study aims to evaluate the long- vs short-run relationships between crops' production (output) and crops' significant inputs such as land use, agricultural water use (AWU) and gross irrigated area in India during the period 1981–2018.

The study applied the autoregressive distributed lag (ARDL) bounds testing approach to estimate the co-integration among the variables. The study uses the error correction model (ECM), which integrates the short-run dynamics with the long-run equilibrium.

The ARDL bounds test of co-integration confirms the strong evidence of the long-run relationship among the variables. Empirical results show the positive and significant relationship of crops' production with land use and gross irrigated area. The statistically significant error correction term (ECT) validates the speed of adjustment of the empirical models in the long-run.

The study suggests that the decision-makers must understand potential trade-offs between human needs and environmental impacts to ensure food for the growing population in India.

For a clear insight into the impact of climate change on crops' production, the current study incorporates the climate variables such as annual rainfall, maximum temperature and minimum temperature. Further, the study considered agro-chemicals, i.e. fertilizers and pesticides, concerning their negative impacts on increased agricultural production and the environment.