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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.

Productivity growth in Italy has been persistently anemic and lagged that of the euro area over the period 1999–2015, while the indebtedness of its corporate sector increased. Using the ORBIS firm-level database, this chapter studies the long-term impact of persistent corporate-debt accumulation on the productivity growth of Italian firms, and investigates whether total factor productivity (TFP) growth varies with the level of corporate indebtedness. The authors employ a novel estimation technique proposed by Chudik, Mohaddes, Pesaran, & Raissi (2017) to account for dynamics, bi-directional feedback effects, cross-firm heterogeneity, and cross-sectional dependence arising from unobserved common factors (e.g., oil price shocks, labor and product market frictions, and the stance of the global financial cycle). Filtering out the effects of unobserved common factors and controlling for firm-specific characteristics, the authors find significant negative effects of persistent corporate-debt build-up on firms’ TFP growth on average, and weak evidence of a threshold level of corporate debt, beyond which productivity growth drops off significantly. The results have strong policy implications, for example the design of the tax system should discourage persistent corporate-debt accumulation, and effective and timely frameworks to reduce corporate-debt overhangs are essential.

The research aims to study the regression, cointegration and causality between the construction sector (CS) and the Gross Domestic Product (GDP), considering other variables in the study such as interest rate, taxation, industry sector, investment and Foreign Direct Investment (FDI), which are analyzed through unique panel models. The study was conducted in Turkey and the ten other countries of the European Union (EU) from 1988 to 2019.

Regression, cointegration and causality methods were used to investigate the different types of relationships between variables in the models. Data were obtained from official databases and the study contains four main stages, which are explained in detail in the methodology section.

The study used the analysis methods of regression, cointegration and causality tests and found that the CS and GDP have long-run estimates and the relationship between the two for different countries is negative in a two-way direction. Results are detailed in the analysis section.

No data were available for the variables before 1988 for most countries, which led to a limited number of observations and issues in statistical analysis methods.

Previously, only input and output tables were used in the analysis. The impact of interest rate, taxation, investment and FDI has not been analyzed. Key variables are very relevant for Turkey, which suffers from chronical inflation and taxation regimes. These show variability with the EU countries for comparative analysis and have not been explored to date, remaining as a major gap for the construction industry. No attempts were made to use regression, cointegration and causality methods with variables. These analysis methods enable an understanding of the differences in variance (heteroscedasticity) and the presence of cross-sectional dependence (CSD), both critical for the reliability of the comparison of data sets and analysis.

In the present study, the authors intended to investigate whether the economic growth drivers such as per capita income, financial development, nonrenewable energy solutions and trade expansion have invigorated the level of environmental pollution in the eight developing nations of South and Southeast Asia.

Considering the possibility of the cross-sectional dependency, the authors employed relatively new econometric approaches, that is, the Westerlund cointegration test and cross-sectional augmented distributed lag mean estimation (CS-DL) for the period of 1990–2015.

The simulation results of the study confirmed an N-shaped environmental Kuznets curve, which raised a question on the existing economic policies in these nations. Further, the study reported that the improvements in the financial sector, nonrenewable energy consumption and trade expansion contributed to increasing the level of CO₂ emissions in the long run.

Based on the results, the authors intended to provide a unique policy framework because the present policies are generating a trade-off between economic and environmental goals. If the suggested framework is employed across sectors, the given countries may likely achieve the sustainable development goals by 2030.

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.

This study aims to analyze the socio-ecological policy trade-off caused by technological innovations in the post-COVID-19 era. The study outcomes are utilized to design a comprehensive policy framework for attaining sustainable development goals (SDGs).

Study is done for 100 countries over 1991–2019. Second-generation estimation method is used. Innovation is measured by total factor productivity, environmental quality is measured by carbon dioxide (CO₂) emissions and social dimension is captured by unemployment.

Innovation–CO₂ emissions association is found to be inverted U-shaped and innovation–unemployment association is found to be U-shaped.

The study outcomes show the conflicting impact of technological innovation leading to policy trade-off. This dual impact of innovation is considered during policy recommendation.

The policy framework recommended in the study shows a way to address the objectives of SDG 8, 9 and 13 during post-COVID-19 period.

Policy recommendations in the study show a way to internalize the negative social externality exerted by innovation.

This study contributes to the literature by considering the policy trade-off caused by innovation and recommending an SDG-oriented policy framework for the post-COVID-19 era.

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.