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The upper reaches of the Yangtze River observed record‐breaking droughts, heatwaves, and forest fires in rapid sequence during the 2022 summer, challenging the established mechanistic understanding. We here explained the compound event through a trans‐seasonal vegetation‐land‐atmosphere interacting perspective. The wetter spring‐sunnier summer pattern resulted in record high loads of vegetation and enhanced transpiration. This led to progressive depletion of soil moisture to a critical threshold that shifted the originally weak response of air temperature into hypersensitive mode. The resulting rapid rise of air temperature amplified atmospheric evaporative demand to an unprecedentedly high level, which in turn exacerbated the drying‐out of soil and vegetation. These favorable weather and fuel factors combined to cause unseasonal forest fires of unprecedented burning intensity. Our results remind of preparedness against drought‐heat‐fire compounding hazards even in humid regions under opportune configurations between ecological and meteorological conditions. Plain Language Summary In humid regions, solar energy absorbed by the surface is largely used for evaporation, acting to suppress atmospheric warming. So the chance of droughts and heatwaves co‐occurring there tends to be small. Contrary to this expectation, the upper reaches of the Yangtze River suffered from concurrent droughts and heatwaves during the 2022 summer with both events breaking previous records by large margins. The resulting impacts were further exacerbated by unprecedentedly severe summertime forest fires. We here attempt to figure out the mechanism for the cascading extremes. Interestingly, we find booming vegetation and abundance of soil moisture through spring to early summer instead of signs of droughts. As large‐scale circulations became unfavorable to precipitation, the excessive biomass continuously consumed soil moisture to a quite low level. Beyond that, the response of local air temperature to declining soil moisture temporarily entered into a hypersensitive state, creating several new heat records within a few days. Under joint pressures from the high water demand of a hotter atmosphere and diminished moisture supply from the drying soil, the originally green vegetation got increasingly flammable, and finally fed the hot‐dry fire weather with excessive amounts of fuels. Key Points Record‐breaking droughts, hot extremes, and forest fires cascaded in the upper reaches of the Yangtze River during the 2022 summer The drop of soil moisture below a critical threshold activated strong land‐air coupling atypical to the humid region The enhanced spring vegetation greenness proves to be conducive to the extreme dryness and forest fires' frequency and intensity

Understanding the irrational sentiments of the market participants is necessary for making good investment decisions. Despite the recent academic effort to examine the role of investors' sentiments in market dynamics, there is a lack of consensus in delineating the structural aspect of market sentiments. This research is an attempt to address this gap. The study explores the role of irrational investors' sentiments in determining stock market volatility. By employing monthly data on market-related implicit indices, we constructed an irrational sentiment index using principal component analysis. This sentiment index was modelled in the GARCH and Granger causality framework to analyse its contribution to volatility. The results showed that irrational sentiment significantly causes excess market volatility. Moreover, the study indicates that the asymmetrical aspects of an inefficient market contribute to excess volatility and returns. The findings are crucial for retail investors as well as portfolio managers seeking to make an optimum portfolio to maximise profits.

Twenty years ago, the discovery of the blood oxygen level dependent (BOLD) contrast and invention of functional magnetic resonance imaging (MRI) not only allowed for enhanced analyses of regional brain activity, but also laid the foundation for novel approaches to studying effective connectivity, which is essential for mechanistically interpretable accounts of neuronal systems. Dynamic causal modeling (DCM) and Granger causality (G-causality) modeling have since become the most frequently used techniques for inferring effective connectivity from fMRI data. In this paper, we provide a short historical overview of these approaches, describing milestones of their development from our subjective perspectives.

The main objective of this work is to analyze and configure appropriately the input vectors to enhance the performance of NARX models to forecast solar radiation one hour ahead. For this study, Engle–Granger causality tests were implemented. Additionally, collinearity among the meteorological variables of the databases was examined. Different databases were used to test the contribution of these analyses in the improvement of the input vectors. For that, databases from three cities of Mexico with different climates were obtained, namely: Chihuahua, Temixco, and Zacatecas. These databases consisted of hourly measurements of the following variables: solar radiation (SR), wind speed (WS), relative humidity (RH), pressure (P), and temperature (T). Results showed that, in all three cases, proper NARX models were produced even when using input vectors formed only with solar radiation and temperature data. Consequently, it was inferred that pressure, wind speed, and relative humidity could be excluded from the input vectors of the forecasting models since, according to the causality tests, they did not provide relevant information to improve the solar radiation forecast in the studied cases. Conversely, these variables could generate spurious results. Forecasting results obtained with the NARX model were compared to the smart persistence model, commonly used to validate SR prediction. Error measures, such as mean absolute error (MAE) and root mean squared error (RMSE), were used to compare prediction results obtained from different models. In all cases, results obtained from the enhanced NARX model surpassed the results of the smart persistence, namely: in Chihuahua up to 11.5 % , in Temixco up to 15.7 % , and in Zacatecas up to 27.2 % .

Brain function arises from networks of distributed brain areas whose directed interactions vary at subsecond time scales. To investigate such interactions, functional directed connectivity methods based on nonparametric spectral factorization are promising tools, because they can be straightforwardly extended to the nonstationary case using wavelet transforms or multitapers on sliding time window, and allow estimating time-varying spectral measures of Granger–Geweke causality (GGC) from multivariate data. Here we systematically assess the performance of various nonparametric GGC methods in real EEG data recorded over rat cortex during unilateral whisker stimulations, where somatosensory evoked potentials (SEPs) propagate over known areas at known latencies and therefore allow defining fixed criteria to measure the performance of time-varying directed connectivity measures. In doing so, we provide a comprehensive benchmark evaluation of the spectral decomposition parameters that might influence the performance of wavelet and multitaper approaches. Our results show that, under the majority of parameter settings, nonparametric methods can correctly identify the contralateral primary sensory cortex (cS1) as the principal driver of the cortical network. Furthermore, we observe that, when properly optimized, the approach based on Morlet wavelet provided the best detection of the preferential functional targets of cS1; while, the best temporal characterization of whisker-evoked interactions was obtained with a sliding-window multitaper. In addition, we find that nonparametric methods provide GGC estimates that are robust against signal downsampling. Taken together our results provide a range of plausible application values for the spectral decomposition parameters of nonparametric methods, and show that they are well suited to characterize time-varying directed causal influences between neural systems with good temporal resolution. •A systematic evaluation of dynamic nonparametric GGC algorithms in an animal model.•Nonparametric GGC is robust against signal downsampling.•Spectral decomposition parameters can influence GGC.•Conditional GGC outdoes pairwise GGC and provides physiologically plausible results.

Differing from previous studies ignoring the nonlinear features, this study employs both the linear and nonlinear Granger causality tests to examine the complex causal relationship between health care expenditure and economic growth among 15 Organisation for Economic Co-operation and Development (OECD) and 5 major developing countries. Some interesting findings can be obtained as follows: (1) For Australia, Austria, and UK, linear and nonlinear Granger causality does not exist between them. A unidirectional linear or nonlinear causality running from economic growth to health care expenditure can be found for Ireland, Korea, Portugal, and India. For these seven countries, health or fiscal policy related to health spending will not have an impact on economic growth; (2) For Belgium, Norway, and Mexico, only a unidirectional linear causality runs from health care expenditure to economic growth, while bidirectional linear causality can be found for Canada, Finland, Iceland, New Zealand, Spain, Brazil, and South Africa. Especially for the US, China, and Japan, a unidirectional nonlinear causality exists from health spending to economic growth. To improve the quality of national health, life quality and happiness, these 13 countries should actively look to optimise policy related to health care expenditure, such as by enhancing the efficiency of health costs to promote sustainable economic development.

This study examines the Granger-causal relationships between oil price movements and global stock returns by using time-varying Granger-causality tests in mean and in variance. We use the daily returns from Morgan Stanley Capital International (MSCI) G7 and the MSCI Emerging Stock Market Indexes to distinguish between the effects of daily oil price movements on G7 countries’ and emerging market countries’ stock markets. We further divide the emerging markets into two groups as oil-exporting and oil-importing countries. For the oil market, we use both the West Texas Intermediate (WTI) and Brent oil daily price movements. While the Granger-causality-in-mean tests indicate a causal link from WTI oil prices and G7 countries’ stock returns to MSCI emerging countries’ stock returns, the Granger-causality-in-variance tests suggest no causal link from global oil market prices to stock market returns. Nonetheless, a causal link from the G7 countries’ stock returns to the MSCI emerging countries’ stock returns is detected. In addition, G7 countries’ stock market volatility is found to Granger-cause Brent oil price volatility. The time-varying Granger-causality-in-mean and Granger-causality-in-variance tests present new and further insights. A causal relationship between oil price changes and G7 countries’ stock returns is found for some periods during and after the global financial crisis. Time-varying Granger-causality-in-variance test results indicate evidence of causal linkages among oil prices and global stock market returns that are specific only to certain time periods. We also find that there might be a difference between the movements in Brent and WTI oil prices with respect to their Granger-causal effects on oil-importing emerging markets’ stock returns—especially after the global financial crisis. Our results provide further evidence that the effects of oil price movements on stock returns might be different depending on the volatility in the stock markets.

In recent years, affective computing based on electroencephalogram (EEG) data has attracted increased attention. As a classic EEG feature extraction model, Granger causality analysis has been widely used in emotion classification models, which construct a brain network by calculating the causal relationships between EEG sensors and select the key EEG features. Traditional EEG Granger causality analysis uses the L 2 norm to extract features from the data, and so the results are susceptible to EEG artifacts. Recently, several researchers have proposed Granger causality analysis models based on the least absolute shrinkage and selection operator (LASSO) and the L 1/2 norm to solve this problem. However, the conventional sparse Granger causality analysis model assumes that the connections between each sensor have the same prior probability. This paper shows that if the correlation between the EEG data from each sensor can be added to the Granger causality network as prior knowledge, the EEG feature selection ability and emotional classification ability of the sparse Granger causality model can be enhanced. Based on this idea, we propose a new emotional computing model, named the sparse Granger causality analysis model based on sensor correlation (SC-SGA). SC-SGA integrates the correlation between sensors as prior knowledge into the Granger causality analysis based on the L 1/2 norm framework for feature extraction, and uses L 2 norm logistic regression as the emotional classification algorithm. We report the results of experiments using two real EEG emotion datasets. These results demonstrate that the emotion classification accuracy of the SC-SGA model is better than that of existing models by 2.46–21.81%.

This study explores the complex relationship between banking sector performance and economic growth in Central and Eastern European (CEE) countries. Given the banking sector’s prominent role within the CEE financial system, our research examines its potential as a driver of economic growth using a fixed-effects panel regression model, focusing on four key variables: non-performing loans, total capital ratio, return on assets, and the ratio of bank assets to GDP. Granger causality tests further assess the directional nature of this relationship. Contrary to prevailing assumptions, the findings reveal no significant direct impact of banking sector performance on economic growth across the CEE region. Instead, the results of the Granger causality indicate that economic growth significantly bolsters the banking sector’s develop¬ment, suggesting an inverse causality. These results offer valuable insights for policymakers, indicating that efforts to stimulate banking sector growth may benefit from prioritising economic development. The study contributes to a nuanced understanding of the CEE context, emphasizing the unique interplay between economic growth and banking sector development in post-transition economies.