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Standard economic theory predicts that any increase in public spending is accompanied by a rise in inflation in an economy. This paper presents empirical proof that prices do not always rise with an increase in public expenditure but only up to a certain threshold level. The primary aim of this paper is to unearth the government size-inflation nexus in India for the period from 1971 to 2019.

The logistic STAR (smooth transition autoregression) model is employed to unravel the government size-inflation nexus for the Indian economy from a non-linear perspective.

The finding of our study confirm the non-linear relationship between the size of the government and inflation in India. The estimated threshold level for government size is precisely found to be 9.27%. The size of the government exerts a negative influence on inflation until it reaches the optimal or threshold level. Any further increase in the size of government beyond this threshold level would result in a rise in inflation.

The findings have implications for the conduct of fiscal policy. Policymakers can increase government spending in a regime of small government size without having any inflationary impacts by generating revenues from taxes and other sources instead of relying much on the central bank. In the regime of a large-sized government, adhering strictly to the discipline in the conduct of fiscal and monetary policies would help curb inflation and enhance growth synchronously, hence alleviating any loss of welfare.

To the best of the authors’ knowledge, this study is an attempt to revisit the government size-inflation nexus in India from a non-linear perspective using the Smooth Transition Autoregression (STAR) model for the first time.

The purpose of this study is to investigate the nonlinear effects of renewable energy (RE) consumption and economic growth on per capita CO₂ emissions during the time span from 1980 to 2020.

The study uses the logistic smooth transition autoregression (STAR) model to decipher the nonlinear relationship between RE consumption, economic growth and CO₂ emissions in the Indian economy.

The estimated results confirm a nonlinear relationship between India’s economic growth, RE consumption and CO₂ emissions. The authors found that economic growth positively impacts CO₂ emissions until it reaches a specific threshold of 1.81 (per capita growth). Beyond this point, further economic growth leads to a reduction in CO₂ emissions. Similarly, RE consumption positively affects CO₂ emissions until economic growth reaches the same threshold level, after which an increase in RE consumption negatively impacts CO₂ emissions.

The study suggests that India should optimize the balance between economic growth and RE consumption to mitigate CO₂ emissions. Policymakers should prioritize the adoption of RE during the early stages of economic growth. As economic growth reaches the specific threshold of 1.81 per capita, the economy should shift to more sustainable and energy-efficient practices to limit the effect of further CO₂ emissions on further economic growth.

To the best of the authors’ knowledge, this study represents the first-ever endeavor to reexamine the nonlinear relationship between RE consumption, economic growth and CO₂ emissions in India, using the STAR model.

The purpose of this paper is to explore the dynamic interdependence structure and risk spillover effect between the Chinese stock market and the US stock market.

This paper mainly uses the multivariate R-vine copula-complex network analysis and the multivariate R-vine copula-CoVaR model and selects stock price indices and their subsector indices as samples.

The empirical results indicate that the Energy, Materials and Financials sectors have leading roles in the interdependent structure of the Chinese and US stock markets, while the Utilities and Real Estate sectors have the least important positions. The comprehensive influence of the Chinese stock market is similar to that of the US stock market but with smaller differences in the influence of different sectors of the US stock market on the overall interdependent structure system. Over time, the interdependent structure of both stock markets changed; the sector status gradually equalized; the contribution of the same sector in different countries to the interdependent structure converged; and the degree of interaction between the two stock markets was positively correlated with the degree of market volatility.

This paper employs the methods of nonlinear cointegration and the R-vine copula function to explore the interactive relationship and risk spillover effect between the Chinese stock market and the US stock market. This paper proposes the R-vine copula-complex network analysis method to creatively construct the interdependent network structure of the two stock markets. This paper combines the generalized CoVaR method with the R-vine copula function, introduces the stock market decline and rise risk and further discusses the risk spillover effect between the two stock markets.

India’s historical fiscal performance has featured elevated deficit levels. Driven by the imperative need for fiscal stimulus measures in response to the crisis, efforts toward fiscal consolidation from 2003 to 2008 were reversed in 2008–2009 due to the financial crisis. These stimulus actions are believed to have wielded a notable influence on inflation dynamics. Presumably, a high inflation rate hinders growth and inflicts severe welfare costs. Accordingly, the principal objective of this paper is to scrutinise the threshold effects of fiscal deficit on inflation within the context of the Indian economy.

We employed the Smooth Transition Autoregressive (STAR) Model, a robust tool for capturing non-linear relationships, to discern the specific threshold level of fiscal deficit. Our analysis encompasses annual data spanning from 1971 to 2020. Additionally, we have leveraged the Toda-Yamamoto causality test to establish the existence and direction of a causal connection between fiscal deficit and inflation in the Indian economy.

Our analysis pinpointed a critical threshold level of 3.40% for fiscal deficit, a value beyond which inflation dynamics in India undergo a marked transition, signifying the presence of significant non-linear effects. Moreover, the results derived from the Toda-Yamamoto causality test offer substantiating evidence of a causal relationship originating from the fiscal deficit and leading to inflation within the Indian economic framework.

The findings of our study carry significant implications, particularly for the formulation and execution of both fiscal and monetary policies. Understanding the threshold effects of fiscal deficit on inflation in India provides policymakers with valuable insights into achieving a harmonious balance between these two critical economic variables.

To the best of our knowledge, this study is the first of its kind to empirically investigate threshold effects of fiscal deficit on inflation in India from a non-linear perspective using the Smooth Transition Autoregression (STAR) model.

This study examines the role of financial development (FD) in enhancing the growth effect of foreign direct investment (FDI) in emerging and developing Asia from 1996 to 2019.

The study exploits the new broad-based Financial Development Index of the International Monetary Fund (IMF) and adopts panel smooth transition regression (PSTR) to perform alternative empirical models for a multidimensional analysis of the FD threshold effect in the growth–FDI nexus.

The results show two thresholds of FD mediating the nonlinear effect of FDI on growth. FD beyond a certain level will enhance the growth effect of FDI, but very high levels of FD will not induce foreign investment to benefit economic growth in emerging and developing Asian economies. The impact of financial institutions on the FDI–growth link is stronger than that of financial markets. Besides, FDI’s effect on growth has an inverted-U shape conditional on financial depth, whereas it is positively associated with the accessibility and efficiency of the financial system.

These results suggest policy implications for emerging and developing Asian countries, emphasizing the other side of “too much finance” and the potential for improvement in the access to and efficiency of the financial system to boost the effects of FDI and FD in the growth of these economies.

The study is the first multifaceted investigation into the influence of FD on the growth effect of FDI. Beyond the previous empirical evidence showing only the impact of credit from banking sector, this study shows different mediating effects of different financial sectors and three dimensions of financing (depth, access and efficiency). The study suggests essential implications for the region in adjusting long-run policies to enhance the FDI–FD–growth link.

The recent Russia–Ukraine conflict highlights the geopolitical importance of natural gas, especially in Europe. In this light, this study examines the impact of the Russia–Ukraine conflict on the spread of price bubbles from European natural gas to international energy prices.

The Generalized Supremum Augmented Dickey-Fuller (GSADF) test is employed to detect the occurrence of price bubble episodes while the Dynamic Logit Model is used to examine price bubble contagion between the two markets. Further, a tri-variate VAR model is used to examine the determinants of the price bubble.

The findings reveal multiple bubble episodes in both European natural gas and international energy prices. Further, evidence of bilateral contagion between European natural gas and the international energy market is found. In addition, the Russia–Ukraine conflict triggers price bubble episodes in both markets. Finally, a counterfactual analysis suggests that the conflict increases the bubble contagion from the European natural gas market to the international energy market by about 40%. These findings imply that the Russia–Ukraine conflict is a significant driver of high upside risks to bubble occurrence and subsequent contagion to both European natural gas and international energy prices.

To the best of our knowledge, this study contributes new empirical evidence that the Russian–Ukrainian conflict significantly impacts the spread of price bubbles from the European natural gas market to international energy markets.

This paper aims to construct positivity-preserving finite volume schemes for the three-dimensional convection–diffusion equation that are applicable to arbitrary polyhedral grids.

The cell vertices are used to define the auxiliary unknowns, and the primary unknowns are defined at cell centers. The diffusion flux is discretized by the classical nonlinear two-point flux approximation. To ensure the fully discrete scheme has positivity-preserving property, an improved discretization method for the convection flux was presented. Besides, a new positivity-preserving vertex interpolation method is derived from the linear reconstruction in the discretization of convection flux. Moreover, the Picard iteration method may have slow convergence in solving the nonlinear system. Thus, the Anderson acceleration of Picard iteration method is used to solve the nonlinear system. A condition number monitor of matrix is employed in the Anderson acceleration method to achieve better robustness.

The new scheme is applicable to arbitrary polyhedral grids and has a second-order accuracy. The results of numerical experiments also confirm the positivity-preserving of the discretization scheme.

1. This article presents a new positivity-preserving finite volume scheme for the 3D convection–diffusion equation. 2. The new discretization scheme of convection flux is constructed. 3. A new second-order interpolation algorithm is given to eliminate the auxiliary unknowns in flux expressions. 4. An improved Anderson acceleration method is applied to accelerate the convergence of Picard iterations. 5. This scheme can solve the convection–diffusion equation on the distorted meshes with second-order accuracy.

It is generally believed that business spending on capital expenditure tends to decrease as interest rates rise, and vice versa, this is not always the case. The previous literature produces inconclusive results vis-à-vis the interest rate and investment nexus. This study analyzes the responsiveness of investment to changes in high and low levels of interest rates in India through a quantile-based, non-parametric method utilizing annual data from 1980 to 2022.

This study uses Quantile-on-quantile (QQ) technique proposed by Sim and Zhou (2015) to examine the impact of interest rate quantiles on quantiles of investment. In addition, long-term association and the direction of causality are estimated through the Cho et al. (2015) test of quantile cointegration and the Jeong et al. (2012) Granger causality in quantile (GCQ) test, respectively.

The empirical evidence validates that the linkage between investments and interest rate is not consistently negative and varies from quantile to quantile. The study finds a negative impact at median quantiles and a positive impact at extreme higher quantiles. Conversely, the impact at lower quantiles is negligible, which is also observed from quantile cointegration, indicating the presence of a statistically significant association above the median quantiles. Additionally, the study finds one interesting finding that there exists unidirectional causality from investment to interest rates in India rather than other way around.

The study provides significant implications for policymakers as it suggests that during extreme economic conditions, the effectiveness of traditional monetary policy tools to boost capital formation is restricted. Policymakers may consider alternative measures to stimulate investment during these time periods. The study additionally posits that the neoclassical theory of investment may not be readily applicable in emerging economies in its unaltered state, mostly due to the lack of well-developed financial markets.

There is a limited literature available on non-linear linkage between interest rates and investment. The present study adds to the existing knowledge by investigating how investment responds differently to fluctuations in interest rates, while incorporating the complete distribution of both the variables.

In order to improve the efficiency and reliability of simulation analysis for composite riveting structures in engineering products, a comparative study was conducted on different forms of riveting simulation methods.

Five different rivent simulation models were established using the finite element method, including rigid element CE, flexible element Rbe3 and beam element, and their results were future compared and analyzed.

Under the given technical parameters, the simulation method of Rbe3 (with holes) + beam can meet the analysis requirements of complex engineering products in terms of the rationality of rivet load distribution, calculation error and relatively efficient modeling.

This study proposes a simulation method for the riveting structure of carbon fiber composite materials for engineering applications. This method can satisfy the simulation analysis requirements of transportation vehicles in terms of modeling time, computational efficiency and accuracy. The research can provide technical support for the riveting process and mechanical analysis between carbon fiber composite components in transportation products.

This study aims to test the validity of the Rajan theory in South Africa and other selected emerging markets (Chile, Peru and Brazil) during the period 1975–2019.

In this study, the researchers used time-series data to estimate a Bayesian Vector Autoregression (BVAR) model with hierarchical priors. The BVAR technique has the advantage of being able to accommodate a wide cross-section of variables without running out of degrees of freedom. It is also able to deal with dense parameterization by imposing structure on model coefficients via prior information and optimal choice of the degree of formativeness.

The results for all countries except Peru confirmed the Rajan hypotheses, indicating that inequality contributes to high indebtedness, resulting in financial fragility. However, for Peru, this study finds it contradicts the theory. This study controlled for monetary policy shock and found the results differing country-specific.

The findings suggest that an escalating level of inequality leads to financial fragility, which implies that policymakers ought to be cautious of excessive inequality when endeavouring to contain the risk of financial fragility, by implementing sound structural reform policies that aim to attract investments consistent with job creation, development and growth in these countries. Policymakers should also be cautious when implementing policy tools (redistributive policies, a sound monetary policy), as they seem to increase the risk of excessive credit growth and financial fragility, and they need to treat income inequality as an important factor relevant to macroeconomic aggregates and financial fragility.