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This paper develops a cross-sectionally augmented distributed lag (CS-DL) approach to the estimation of long-run effects in large dynamic heterogeneous panel data models with cross-sectionally dependent errors. The asymptotic distribution of the CS-DL estimator is derived under coefficient heterogeneity in the case where the time dimension (T) and the cross-section dimension (N) are both large. The CS-DL approach is compared with more standard panel data estimators that are based on autoregressive distributed lag (ARDL) specifications. It is shown that unlike the ARDL-type estimator, the CS-DL estimator is robust to misspecification of dynamics and error serial correlation. The theoretical results are illustrated with small sample evidence obtained by means of Monte Carlo simulations, which suggest that the performance of the CS-DL approach is often superior to the alternative panel ARDL estimates, particularly when T is not too large and lies in the range of 30–50.

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.

This study aims to investigate the cointegration and causality relationships between Hong Kong’s residential property price and stock price, using quarterly data, from the 1st quarter of 1980 to the 3rd quarter of 2015.

In contrast to other studies, the cointegration test used is the autoregressive distributed lag (ARDL) cointegration (bounds testing) approach of Pesaran et al. (2001) that based on the estimation of an unrestricted error correction model and the causality test is based on non-causality test of Granger et al. (2000). Moreover, this research employs recursive least square procedures and Chow (1960) breakpoint test to detect unknown structural break and variation of relationships between residential property and stock price over the whole sample period.

The results of ARDL cointegration tests running from stock to residential property markets provide strong evidence to support the hypothesis that the stock and residential properties are cointegrated. The results of Granger et al. (2000) non-causality test support the view of wealth effect that stock price has an important causal effect on residential property price in Hong Kong but not vice versa. In addition, the results of recursive ordinary least squares coefficients estimates and Chow (1960) test (breakpoint test) for structural instability confirm the variation of the relationships between stock and residential property markets over the sample period.

The empirical results from cointegration and causality tests suggest that the residential asset returns are better predicted by including the lagged difference values of stock price.

This is the pioneering study to examine the cointegration and causality study of stock and residential property price in Hong Kong by employing Pesaran ARDL cointegration approach and Granger non-causality approach. Investors are able to perform an effective evaluation to assist in allocating investment funds, and the government bodies can implement supplement housing policy in response to the public needs.

The paper attempts to revisit the nexus between economic growth, carbon emissions, trade openness, financial effectiveness and FDI for a sample of seven developed and developing countries using curvilinear relationship as per environmental Kuznets curve (EKC) hypothesis over long term.

The authors determine the unit root properties of variables (using Clemente–Montañés–Reyes unit root test with double mean shifts and AO model and augmented Dickey–Fuller test) for structural breaks at different levels. Autoregressive distributed lag (ARDL) and error correction model (ECM) methodology was used to estimate long- and short-run parameters among the selected variables in sample countries from 1965 to 2016. Vector error correction (VEC) and Granger causality approach was used to determine the direction of causality.

The authors confirmed long-run relationship among the variables and highlighted high economic growth and energy consumption as the main causes of environmental degradation. While in India financial development and FDI inflows depict a negative association with environmental sustainability, however, such relationship was positive in the United Kingdom (UK), which is often considered as a benchmark for policymakers. The authors’ findings were in agreement with existing research insights in reporting FDI and financial development as the major contributors towards (unsustainable) sustainable environment through emissions in case of (developing country like India) developed country like UK. For other sample countries (China, Brazil, Japan, South Africa, United States of America (USA)), the authors’ model failed to capture financial development and FDI as significant contributors of carbon emissions. However, unidirectional causality running from energy to carbon emission was observed leading to the policy adoption of incentivizing alternative energy-based resources to increase energy efficiency across the energy value chain.

Manufacturing with renewable energy, in collaboration with private and foreign players, under an institutional framework is desirable. Policy instruments including mandatory administrative controls, economic incentives and voluntary schemes that promote energy efficiency building blocks need to be established. A sound legal system for implementing technological innovation, financial subsidy incentives, interest-free loan programmes and development of financial sector supports creation and thriving of energy efficient units, often a perquisite for accelerated development.

By undertaking a comparative analysis, the authors address the research gap through revisiting EKC hypothesis with different set of trade policy and financial development framework. To the best of the authors’ knowledge, earlier studies were limited to one-country data analysis and did not consider the comparative data set of developed and developing countries with reference to financial development and FDI components.

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.

The search for more effective policies, choice of optimal implementation strategies for achieving defined policy targets (e.g., cost-containment, improved access, and quality healthcare outcomes), and selection among the metrics relevant for assessing health system policy change performance simultaneously pose continuing healthcare sector challenges for many countries of the world. Meanwhile, research on the core drivers of healthcare costs across the health systems of the many countries continues to gain increased momentum as these countries learn among themselves. Consequently, cross-country comparison studies largely focus their interests on the relationship among health expenditures (HCE), GDP, aging demographics, and technology. Using more recent 1980–2014 annual data panel on 34 OECD countries and the panel ARDL (Autoregressive Distributed Lag) framework, this study investigates the long- and short-run relationships among aggregate healthcare expenditure, income (GDP per capita or per capita GDP^_HCE), age dependency ratio, and “international co-operation patents” (for capturing the technology effects). Results from the panel ARDL approach and Granger causality tests suggest a long-run relationship among healthcare expenditure and the three major determinants. Findings from the Westerlund test with bootstrapping further corroborate the existence of a long-run relationship among healthcare expenditure and the three core determinants. Interestingly, GDP less health expenditure (GDP^_HCE) is the only short-run driver of HCE. The income elasticity estimates, falling in the 1.16–1.46 range, suggest that the behavior of aggregate healthcare in the 34 OECD countries tends toward those for luxury goods. Finally, through cross-country technology spillover effects, these OECD countries benefit significantly from international investments through technology cooperations resulting in jointly owned patents.

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.

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.