No full-text available
To read the full-text of this research,
you can request a copy directly from the author.
Investigating Causal Relations by Econometric Models and Cross-Spectral Methods
Abstract
There occurs on some occasions a difficulty in deciding the direction of causality between two related variables and also whether or not feedback is occurring. Testable definitions of causality and feedback are proposed and illustrated by use of simple two-variable models. The important problem of apparent instantaneous causality is discussed and it is suggested that the problem often arises due to slowness in recordhag information or because a sufficiently wide class of possible causal variables has not been used. It can be shown that the cross spectrum between two variables can be decomposed into two parts, each relating to a single causal arm of a feedback situation. Measures of causal lag and causal strength can then be constructed. A generalization of this result with the partial cross spectrum is suggested.The object of this paper is to throw light on the relationships between certain classes of econometric models involving feedback and the functions arising in spectral analysis, particularly the cross spectrum and the partial cross spectrum. Causality and feedback are here defined in an explicit and testable fashion. It is shown that in the two-variable case the feedback mechanism can be broken down into two causal relations and that the cross spectrum can be considered as the sum of two cross spectra, each closely connected with one of the causations. The next three sections of the paper briefly introduce those aspects of spectral methods, model building, and causality which are required later. Section IV presents the results for the two-variable case and Section V generalizes these results for three variables.
... At the same time, it has also been noticed that when there exists instantaneous and/or strong nonlinear interactions between two regions, GC analysis may lead to invalid results [9,15]. Moreover, GC may not be able to detect the causation in deterministic settings [10,16]. ...
... The first practical causal analysis framework is Granger Causality (GC), which was proposed by Granger in 1969 [10]. GC is a statistical approach that relies on a multi-step linear prediction model and aims to determine whether the values of one time series are useful in predicting the future values of the other. ...
... First, it is a universal method that does not have any modeling constraints on the sequences to be evaluated [29,30]. Second, DI serves as the pivot that links existing causality models GC [10,18], transfer entropy (TE) [9,31,32], and dynamic causal modeling (DCM) [33,34] through conditional equivalence between them. ...
Rooted in dynamic systems theory, convergent cross mapping (CCM) has attracted increased attention recently due to its capability in detecting linear and nonlinear causal coupling in both random and deterministic settings. One limitation with CCM is that it uses both past and future values to predict the current value, which is inconsistent with the widely accepted definition of causality, where it is assumed that the future values of one process cannot influence the past of another. To overcome this obstacle, in our previous research, we introduced the concept of causalized convergent cross mapping (cCCM), where future values are no longer used to predict the current value. In this paper, we focus on the implementation of cCCM in causality analysis. More specifically, we demonstrate the effectiveness of cCCM in identifying both linear and nonlinear causal coupling in various settings through a large number of examples, including Gaussian random variables with additive noise, sinusoidal waveforms, autoregressive models, stochastic processes with a dominant spectral component embedded in noise, deterministic chaotic maps, and systems with memory, as well as experimental fMRI data. In particular, we analyze the impact of shadow manifold construction on the performance of cCCM and provide detailed guidelines on how to configure the key parameters of cCCM in different applications. Overall, our analysis indicates that cCCM is a promising and easy-to-implement tool for causality analysis in a wide spectrum of applications.
... To analyze longitudinal networks, our approach aims to utilize various statistical methods, including autoregressive models (e.g., Bringmann et al., 2013;Epskamp, 2020), Group Iterative Multiple Model Estimation (GIMME; Gates & Molenaar, 2012), multivariate Granger causality (Granger, 1969), and Bayesian networks (e.g., McNally et al., 2017;Pearl & Mackenzie, 2019), and possibly advanced machine learning techniques such as deep neural networks (e.g., Goodfellow et al., 2016;Okuno & Woodward, 2021). These methods facilitate the identification of stable network connections (i.e., strength of connections) and contribute to improving treatment outcomes (McElroy et al., 2019). ...
... Due to the presence of heterogeneity both between individuals and within individuals (Molenaar, 2008(Molenaar, , 2013, and the limitations in identifying distinct symptom patterns within diagnostic labels (e.g., Contractor et al., 2017), we propose the utilization of dynamical network analyses which consider individual variability and aim to minimize the impact of aggregated group patterns (Fisher et al., 2018;Molenaar, 2013;. Dynamic networks visualize the direction of relationships between nodes and provide insights into Granger causality (Granger, 1969), which assumes that knowledge of a system's status at earlier time points aids in predicting its future behavior (Epskamp et al., 2018b;Granger, 1969). Granger causality can be instrumental in prioritizing specific symptoms within interventions by identifying their role in driving network dynamics (e.g., Goldin et al., 2014;Hamilton et al., 2011;Zuidersma et al., 2022). ...
... Due to the presence of heterogeneity both between individuals and within individuals (Molenaar, 2008(Molenaar, , 2013, and the limitations in identifying distinct symptom patterns within diagnostic labels (e.g., Contractor et al., 2017), we propose the utilization of dynamical network analyses which consider individual variability and aim to minimize the impact of aggregated group patterns (Fisher et al., 2018;Molenaar, 2013;. Dynamic networks visualize the direction of relationships between nodes and provide insights into Granger causality (Granger, 1969), which assumes that knowledge of a system's status at earlier time points aids in predicting its future behavior (Epskamp et al., 2018b;Granger, 1969). Granger causality can be instrumental in prioritizing specific symptoms within interventions by identifying their role in driving network dynamics (e.g., Goldin et al., 2014;Hamilton et al., 2011;Zuidersma et al., 2022). ...
Background
Despite impressive dissemination programs of best-practice therapies, clinical psychology faces obstacles in developing more efficacious treatments for mental disorders. In contrast to other medical disciplines, psychotherapy has made only slow progress in improving treatment outcomes. Improvements in the classification of mental disorders could enhance the tailoring of treatments to improve effectiveness. We introduce a multimodal dynamical network approach, to address some of the challenges faced by clinical research. These challenges include the absence of a comprehensive meta-theory, comorbidity, substantial diagnostic heterogeneity, violations of ergodicity assumptions, and a limited understanding of causal processes.
Methods
Through the application of multimodal dynamical network analysis, we describe how to advance clinical research by addressing central problems in the field. By utilizing dynamic network analysis techniques (e.g., Group Iterative Multiple Model Estimation, multivariate Granger causality), multimodal measurements (i.e., psychological, psychopathological, and neurobiological data), intensive longitudinal data collection (e.g., Ecological Momentary Assessment), and causal inference methods (e.g., GIMME), our approach could improve the comprehension and treatment of mental disorders. Under the umbrella of the systems approach and utilizing e.g., graph theory and control theory, we aim to integrate data from longitudinal, multimodal measurements.
Results
The multimodal dynamical network approach enables a comprehensive understanding of mental disorders as dynamic networks of interconnected symptoms. It dismantles artificial diagnostic boundaries, facilitating a transdiagnostic view of psychopathology. The integration of longitudinal data and causal inference techniques enhances our ability to identify influential nodes, prioritize interventions, and predict the impact of therapeutic strategies.
Conclusion
The proposed approach could improve psychological treatment by providing individualized models of psychopathology and by suggesting individual treatment angles.
... In econometric analyses, in cases where the direction of the relationship between variables cannot be determined by economic theory, the existence and direction of the relationship between variables can be determined by the [47] test. In this test, variables are not separated as dependent and independent. ...
... Ref. [50] defended this exclusion by mentioning differences in trading timing, but we do not agree with this reasoning since the indexes' prices we are examining are from US-based exchanges and can be considered synchronous. Therefore, we exclude the contemporaneous causality of [47] for that reason. ...
... Ref. [50] defended this exclusion by mentioning differences in trading timing, but we do not agree with this reasoning since the indexes' prices we are examining are from US-based exchanges and can be considered synchronous. Therefore, we exclude the contemporaneous causality of [47] for that reason. Table 2 displays the rejection rates of the null hypothesis of no tail causality using the DCC-MGARCH Hong test with both normal and simulated critical values. ...
This work provides an in-depth investigation of the dynamic interaction patterns between water stocks and renewable energy markets through the application of continuous wavelet analysis, dynamic correlation analysis, and time-varying Granger causality analysis. In addition, this study utilizes daily pricing indices, namely the S&P Global Water Index, Solactive Global Wind Energy Index, and Solactive Global Solar Energy Index, spanning from 18 May 2011 to 23 June 2022. The results show significant correlation patterns between the indices, ranging from moderate to high. Notably, robust correlations have been detected starting from 2015. The research also discovered a varied and inconsistent relationship between frequency and causation throughout different time periods. Moreover, the results reveal an asymmetry in the causal effects and a symmetry correlation at tail quantile ranges. Policymakers and market participants must consider these insights to make wise financial and strategic decisions.
... Statistical approaches to modeling effective connectivity among brain regions include dynamic causal modeling (DCM, Friston et al., 2003), structural equation modeling (SEM, Mclntosh & Gonzalez-Lima, 1994), Bayesian networks (BNs, Li et al., 2008;Rajapakse & Zhou, 2007), and Granger causality (CG) modeling via vector autoregressive (VAR) models (Granger, 1969;Roebroeck et al., 2005). DCM and SEM are typically used as confirmatory techniques to test predefined hypotheses about neural activity (Friston, 2011). ...
... For one approach, at the first stage, that is, group-level edge selection, we considered a traditional GC model. GC(Granger, 1969) is a statistical hypothesis test to assess whether one time series can predict another time series. Here, it estimates subject-level VAR coefficients via ordinary least squares and then performs group-level inference through one-sample t tests. ...
In this article, we develop an analytical approach for estimating brain connectivity networks that accounts for subject heterogeneity. More specifically, we consider a novel extension of a multi‐subject Bayesian vector autoregressive model that estimates group‐specific directed brain connectivity networks and accounts for the effects of covariates on the network edges. We adopt a flexible approach, allowing for (possibly) nonlinear effects of the covariates on edge strength via a novel Bayesian nonparametric prior that employs a weighted mixture of Gaussian processes. For posterior inference, we achieve computational scalability by implementing a variational Bayes scheme. Our approach enables simultaneous estimation of group‐specific networks and selection of relevant covariate effects. We show improved performance over competing two‐stage approaches on simulated data. We apply our method on resting‐state functional magnetic resonance imaging data from children with a history of traumatic brain injury (TBI) and healthy controls to estimate the effects of age and sex on the group‐level connectivities. Our results highlight differences in the distribution of parent nodes. They also suggest alteration in the relation of age, with peak edge strength in children with TBI, and differences in effective connectivity strength between males and females.
... The Dumitrescu and Hurlin (2012) test is an extension of the Granger causality test. Granger (1969) developed a causality test for time series data. Consider and as two stationary series. ...
... The basic idea is that if past values of x are significant predictors of the current value of y even when past values of y have been included in the model, then x exerts a causal influence on y (Lopez and Weber 2017). Similar to Granger's (1969) causality test in a panel environment, Dumitrescu and Hurlin (2012) developed a bivariate testing method. The underlying regression is written as follows: ...
The study sought to examine the relationship between financial development and economic growth in low-income nations in the SADC region. Motivated by the observation that numerous states in the SADC region lack adequately developed financial systems, this investigation was undertaken. Many SADC states are low-income countries, and they remain financially underdeveloped, which could compromise their growth prospects. The analysis was quantitative in nature, and used panel data to achieve its objectives. The data period spanned from 2000 to 2022. The dynamic common correlated effects (DCCE) technique was used for estimation purposes. Results showed that there is a positive relationship between financial development and economic growth. The relationship was also found to be causal: financial development is not only a result of economic growth; it also influences growth. The evidence from the findings supports the notion that financial development is needed to increase the effectiveness of resource allocation and consequently promote growth. This calls on the governments in the countries under investigation to create environments that foster financial development.
... Granger causality test is a statistical method used to determine the causal relationship between two time series variables. It was developed by Nobel Prize-winning economist Clive Granger in the 1960s and is widely used in economics, finance, and social sciences to understand the direction and strength of the relationship between two variables (Granger, 1969). In the context of population and emissions, Granger causality test can be used to determine whether population growth causes an increase in emissions or vice versa. ...
... Overall, China has the highest total energy consumption and CO2 emissions from transport, followed by India and Japan. The four aspects discussed in Table 5 (Granger, 1969). The null hypothesis for Granger's test of causality: The population of the country is not a direct determinant that impacts CO2 emissions from transport (% of total fuel combustion). ...
Transportation sustainability is critical in Asia, given the region's population and economic growth. This research paper investigates the relationship between population and fuel consumption in Asia's ten most populous countries. Using the Granger Causality test, this study examines if a country's population significantly predicts fuel consumption. The findings suggest that no significant relationship exists between the population and fuel consumption, indicating that population growth does not necessarily lead to increased fuel consumption. These results have important implications for policymakers and researchers seeking to understand the factors driving fuel consumption in Asia. To improve transportation sustainability in these countries, there is a need for policies and strategies that focus on reducing transportation emissions and improving energy efficiency. Therefore, there is a need for an integrated approach that considers the development of sustainable transportation systems. This study can inform policymakers and stakeholders in developing sustainable transportation systems.
... The goal of the Granger Causality [9] is to detect a causal effect of a time series on another time series. The Granger causality measures correlations between the effect series and shifted cause series, thus detecting a lag that represents the time needed for the cause to take shape. ...
... The method uses various statistical tests to detect whether adding a cause into a predictive model significantly improves the prediction capabilities of the model. The original paper [9] used linear regression as the testing predictor. Further modifications of the original paper followed and included non-linearity [32], learning from multiple time series [4], applications on spectral data, i.e., in the frequency domain [11], model-free modifications [6], and nonstationarity [27]. ...
We present a benchmark for methods in causal learning. Specifically, we consider training a rich class of causal models from time-series data, and we suggest the use of the Krebs cycle and models of metabolism more broadly.
... To model two or more time series that are hypothesized to be related to each other, an SDE can be linked to other SDEs, with the connection characterized using coefficients determining link strength [14,18]. Links between two measured or observed time series can be modelled as correlations (via the stochastic term) or Granger causal relationships (where the 'driver' time series is 'embedded' in the deterministic term of the 'driven' time series, i.e. the mean of the driven series is 'controlled' by the 'driver' [32]). Granger causality [32] is a definition of causality that rests on information-transfer. Time series X is said to Granger cause time series Y if the present state of Y can better be predicted by the past of both X and Y than by only the past of Y. ...
... Links between two measured or observed time series can be modelled as correlations (via the stochastic term) or Granger causal relationships (where the 'driver' time series is 'embedded' in the deterministic term of the 'driven' time series, i.e. the mean of the driven series is 'controlled' by the 'driver' [32]). Granger causality [32] is a definition of causality that rests on information-transfer. Time series X is said to Granger cause time series Y if the present state of Y can better be predicted by the past of both X and Y than by only the past of Y. Such links can also be estimated between a measured and an unmeasured time series (i.e. a hidden layer, figure 1). ...
In investigating global patterns of biodiversity through deep time, many large-scale drivers of diversification have been proposed, both biotic and abiotic. However, few robust conclusions about these hypothesized effectors or their roles have been drawn. Here, we use a linear stochastic differential equation (SDE) framework to test for the presence of underlying drivers of diversification patterns before examining specific hypothesized drivers. Using a global dataset of observations of skeletonized marine fossils, we infer origination, extinction and sampling rates (collectively called fossil time series) throughout the Phanerozoic using a capture–mark–recapture approach. Using linear SDEs, we then compare models including and excluding hidden (i.e. unmeasured) drivers of these fossil time series. We find evidence of large-scale underlying drivers of marine Phanerozoic diversification rates and present quantitative characterizations of these. We then test whether changing global temperature, sea-level, marine sediment area or continental fragmentation could act as drivers of the fossil time series. We show that it is unlikely any of these four abiotic factors are the hidden drivers we identified, though there is evidence for correlative links between sediment area and origination/extinction rates. Our characterization of the hidden drivers of Phanerozoic diversification and sampling will aid in the search for their ultimate identities.
... Having ascertained the cointegrating relationship among the variables, the study next investigated the causal link among them. Granger (1969) suggested that causality can be split into short-run and long-run causality. In ...
... Variables can be used at their level without taking differences. Traditional Granger (1969) causality tests use stationary variables. In particular, time series such as growth rates, inflation, and unemployment rates often become stationary by taking differences. ...
There is no doubt that migration is a complex phenomenon with varied economic, political, and cultural impacts on both the countries of origin as well as the countries of destination. In particular, migration fears and policy uncertainties may have positive or negative consequences on economic activities such as the labor market, price stability, and economic activity in economies either directly or indirectly. For this reason, this study analyzes the causality effect of migration fear and policy uncertainty indices on macroeconomic variables in the most immigrant-receiving countries, the US, the UK, France, and Germany, using both panel and time-varying causality tests. In the study, no causality relationship was found from migration fear and policy uncertainty indices to macroeconomic variables in panel data. However, country-specific time-varying causality relationships were detected in the time series dimension. According to the findings, it can be stated that policymakers and researchers should consider migration fear and policy uncertainty when determining policies to ensure macroeconomic stability in these countries.
... In line with Granger and Wiener's description of causal relation between time series (Granger, 1969), we utilized multivariate autoregressive (MVAR) models to analyze the causal connections between the single EEG channels. A causal connection may accordingly be assumed if knowledge of past values of one time series improves predictions of another time series. ...
Accident analyses repeatedly reported the considerable contribution of runoff road incidents to fatalities in road traffic, and despite considerable advances in assistive technologies to mitigate devastating consequences, little insight into the drivers' brain response during such accident scenarios has been gained. While various literature documents neural correlates to steering motion, the driver's mental state, and the impact of distraction and fatigue on driving performance, the cortical substrate of continuous deviations of a car from the road-i.e., how the brain represents a varying discrepancy between the intended and observed car position and subsequently assigns customized levels of corrective measures-remains unclear. Furthermore, the superposition of multiple subprocesses, such as visual and erroneous feedback processing, performance monitoring, or motor control, complicates a clear interpretation of engaged brain regions within car driving tasks. In the present study, we thus attempted to disentangle these subprocesses, employing passive and active steering conditions within both error-free and error-prone vehicle operation conditions. We recorded EEG signals of 26 participants in 13 sessions, simultaneously measuring pairs of Executors (actively steering) and Observers (strictly observing) during a car driving task. We observed common brain patterns in the Executors regardless of error-free or error-prone vehicle operation, albeit with a shift in spectral activity from motor beta to occipital alpha oscillations within erroneous conditions. Further, significant frontocentral differences between Observers and Executors, tracing back to the caudal anterior cingulate cortex, arose during active steering conditions, indicating increased levels of motor-behavioral cognitive control. Finally, we present regression results of both the steering signal and the car position, indicating that a regression of continuous deviations from the road utilizing the EEG might be feasible.
... Methods based on pairwise association, such as correlation-based measures, are common tools for analyzing the relationships among the variables in the climate system, but the statistical-association-based methods are often inadequate to infer causality (Gao et al., 2022;Runge et al., 2019b). To detect causality from time series, a celebrated framework is the Granger causality (GC) method (Granger, 1969). In climate sciences, the GC method has been widely used in applications such as identifying the temperature and wind patterns (McGraw & Barnes, 2018) or climate teleconnections (Silva et al., 2021). ...
Identifying and understanding various causal relations are fundamental to climate dynamics for improving the predictive capacity of Earth system modeling. In particular, causality in Earth systems has manifest temporal periodicities, like physical climate variabilities. To unravel the characteristic frequency of causality in climate dynamics, we develop a data‐analytic framework based on a combination of causality detection and Hilbert spectral analysis, using a long‐term temperature and precipitation dataset in the contiguous United States. Using the Huang–Hilbert transform, we identify the intrinsic frequencies of cross‐regional causality for precipitation and temperature, ranging from interannual to interdecadal time scales. In addition, we analyze the spectra of the physical climate variabilities, including El Niño‐Southern Oscillation and Pacific Decadal Oscillation. It is found that the intrinsic causal frequencies are positively associated with the physics of the oscillations in the global climate system. The proposed methodology provides fresh insights into the causal connectivity in Earth's hydroclimatic system and its underlying mechanism as regulated by the characteristic low‐frequency variability associated with various climatic dynamics.
... Granger causality test: Granger causality is a statistical concept used to determine if one time series can help predict another (Stokes and Purdon, 2017). The test is based on the principles of temporal precedence and predictability (Granger, 1969). That is, if one time series causes another, then past values of the causing series should contain information that can be used to improve the prediction of the second series (Stokes and Purdon, 2017 series. ...
This study explores the causal relationships between catchment water availability, vapor pressure deficit, and gross primary productivity across 341 catchments in the contiguous US. Seasonal climatic, hydrological, and vegetation characteristics were represented using the Horton index, ecological aridity index, evaporative fraction index, and carbon uptake efficiency. Statistical methods, including circularity statistics, correlation analysis, and causality tests, were employed to determine the complex interactions between catchment wetness, atmospheric dryness, and vegetation carbon uptake. The results revealed a maximum lag of two months in the intra-annual variability of catchment water supply-productivity and atmospheric water demand-productivity relationships, with hysteresis patterns varying with the catchment’s hydrological characteristics. In catchments not permanently under water-limited or energy-limited conditions, vegetation experiences hydrological stress during the peak growing period, coinciding with the highest gross primary productivity and carbon uptake efficiency being out of phase with Horton index and in phase with evaporative fraction index. Causality analysis highlights strong temporal continuity in GPP seasonal characteristics, with a cause-effect relationship between catchment water supply, atmospheric demand, and vegetation productivity spanning a maximum of two months. These findings underscore the need for a comprehensive functional framework that integrates catchment water supply, atmospheric demand, and vegetation productivity to enhance our understanding and predictive capabilities of ecosystem responses to climate change.
... Traditionally, causal relationships between variables of the same system, assuming X and Y variables, are measured by the amount of information of past X that is encoded into future Y (Lucas, 2020;Moraffah et al., 2020;Zhao & Hastie, 2021). Granger Causality (GC) is used to identify and measure causality in time series (Granger, 1969). According to GC, X causes Y if the predictability of Y decreases when X is removed from the system. ...
Cyanobacterial blooms in freshwater sources are a global concern, and gaining insight into their causes is crucial for effective resource management and control. In this study, we present a novel computational framework for the causal analysis of cyanobacterial harmful algal blooms (cyanoHABs) in Lake Kinneret. Our framework integrates Convergent Cross Mapping (CCM) and Extended CCM (ECCM) causal networks with Bayesian Network (BN) models. The constructed CCM–ECCM causal networks and BN models unveil significant interactions among factors influencing cyanoHAB formation. These interactions have been validated by domain experts and supported by evidence from peer‐reviewed publications. Our findings suggest that Microcystis flos‐aquae levels are influenced not only by community structure but also by ammonium, phosphate, oxygen, and temperature levels in the weeks preceding bloom occurrences. We demonstrated a non‐parametric computational framework for causal analysis of a multivariate ecosystem. Our framework offers a more comprehensive understanding of the underlying mechanisms driving M. flos‐aquae blooms in Lake Kinneret. It captures complex interactions and provides an explainable prediction model. By considering causal relationships, temporal dynamics, and joint probabilities of environmental factors, the proposed framework enhances our understanding of cyanoHABs in Lake Kinneret.
... The Granger causality test, first proposed in Granger (1969) is a statistical test used to determine whether a time series can linearly forecast another. It is important to note that this does not imply causation in the strict sense. ...
We present an Natural Language Processing based analysis on the phenomenon of “Meme Stocks”, which has emerged as a result of the proliferation of neo-brokers like Robinhood and the massive increase in the number of small-scale stock investors. Such investors often use specific Social Media channels to share short-term investment decisions and strategies, resulting in partial collusion and planning of investment decisions. The impact of online communities on the stock prices of affected companies has been considerable in the short term. This paper has two objectives. Firstly, we chronologically model the discourse on the most prominent platforms. Secondly, we examine the potential for using collaboratively made investment decisions as a means to assist in the selection of potential investments.. To understand the investment decision-making processes of small-scale investors, we analyze data from Social Media platforms like Reddit, Stocktwits and Seeking Alpha. Our methodology combines Sentiment Analysis and Topic Modelling. Sentiment Analysis is conducted using VADER and a fine-tuned BERT model. For Topic Modelling, we utilize LDA, NMF and the state-of-the-art BERTopic. We identify the topics and shapes of discussions over time and evaluate the potential for leveraging information of the decision-making process of investors for trading choices. We utilize Random Forest and Neural Network Models to show that latent information in discussions can be exploited for trend prediction of stocks affected by Social Network driven herd behavior. Our findings provide valuable insights into content and sentiment of discussions and are a vehicle to improve efficient trading decisions for stocks affected from short-term herd behavior.
... The corresponding process graph, on the contrast, is a finite graph, so its complexity is limited by the number of processes to be modelled. From a combinatorial point of view, it would therefore be attractive to use only the process graph instead of the time series graph when analysing the causal structure [25,23,24,15,14]. ...
A structural vector autoregressive (SVAR) process is a linear causal model for variables that evolve over a discrete set of time points and between which there may be lagged and instantaneous effects. The qualitative causal structure of an SVAR process can be represented by its finite and directed process graph, in which a directed link connects two processes whenever there is a lagged or instantaneous effect between them. At the process graph level, the causal structure of SVAR processes is compactly parameterised in the frequency domain. In this paper, we consider the problem of causal discovery and causal effect estimation from the spectral density, the frequency domain analogue of the auto covariance, of the SVAR process. Causal discovery concerns the recovery of the process graph and causal effect estimation concerns the identification and estimation of causal effects in the frequency domain. We show that information about the process graph, in terms of $d$- and $t$-separation statements, can be identified by verifying algebraic constraints on the spectral density. Furthermore, we introduce a notion of rational identifiability for frequency causal effects that may be confounded by exogenous latent processes, and show that the recent graphical latent factor half-trek criterion can be used on the process graph to assess whether a given (confounded) effect can be identified by rational operations on the entries of the spectral density.
... During the construction of the aforementioned adjacency matrix, the consideration of directionality in information transmission was omitted. Hence, this paper proposes a data enhancement method based on the Granger causality test to construct an asymmetric adjacency matrix that accurately reflects the directional relationships between EEG channels and improves emotion recognition accuracy [21,29]. For two EEG channels i and j, with their respective time series data denoted as ...
EEG signals capture information through multi-channel electrodes and hold promising prospects for human emotion recognition. However, the presence of high levels of noise and the diverse nature of EEG signals pose significant challenges, leading to potential overfitting issues that further complicate the extraction of meaningful information. To address this issue, we propose a Granger causal-based spatial–temporal contrastive learning framework, which significantly enhances the ability to capture EEG signal information by modeling rich spatial–temporal relationships. Specifically, in the spatial dimension, we employ a sampling strategy to select positive sample pairs from individuals watching the same video. Subsequently, a Granger causality test is utilized to enhance graph data and construct potential causality for each channel. Finally, a residual graph convolutional neural network is employed to extract features from EEG signals and compute spatial contrast loss. In the temporal dimension, we first apply a frequency domain noise reduction module for data enhancement on each time series. Then, we introduce the Granger–Former model to capture time domain representation and calculate the time contrast loss. We conduct extensive experiments on two publicly available sentiment recognition datasets (DEAP and SEED), achieving 1.65% improvement of the DEAP dataset and 1.55% improvement of the SEED dataset compared to state-of-the-art unsupervised models. Our method outperforms benchmark methods in terms of prediction accuracy as well as interpretability.
... The Granger's linear test (Granger, 1969) is one of the most popular methods to check the noncausality between the variables. As the linear assumption of the test causes a problem with low power to detect non-linear relationships, non-linear tests are also required. ...
Predictability of the various financial instruments can lead to more trust and investment. The study examines the long-term and causal relationship between various Nifty indices and the Ethereum cryptocurrency. This study considers the data from April 2015 to December 2022 in two phases, pre-covid and post-covid. Johansen’s cointegration test was used to determine if the vectors in the data set are cointegrated, using the Max-Eigen and Trace tests for evaluation. The Granger causality test was also used to explore the short-term causal relationship between Ethereum and the five Nifty indices. The study found that post-pandemic daily returns of stock market indices have developed a significant cointegration with the cryptocurrency over time. The Granger causality test results showed bi-directional relationships of Nifty 50, Nifty 200 and Nifty Next 50 with Ethereum and a unidirectional relationship between Nifty Auto and Ethereum. The non-linear results reveal a one-way relationship pre-covid and a bi-directional relationship post-covid except for Nifty Banks. Johansen’s cointegration test, both in the pre-and post-covid-era, indicated that these indices had a substantial long-term cointegration with cryptocurrencies. This study also offers guidance to investors in making long-term investment decisions and to regulatory authorities. This implied that the investing decisions resulted in developing a causal relationship between the equity market and cryptocurrencies, which seemed very unlikely before 2020. This indicates that a new and young investor also considered cryptocurrencies a viable alternate investment option compared to traditional options such as fixed deposits, gold, and other fixed-income instruments.
... Certain years, like 2010, 2012, and 2024, exhibit spikes in activity, indicating potential influencing factors or events. Further investigation could delve into possible correlations with geopolitical events, economic circumstances, or changes in maritime security protocols during these peak period (Anele, 2023;Granger, 1969)s. ...
This research delves into global patterns and trends in maritime piracy and anti-shipping activities from 2008 to 2024, utilizing data from Anti-Shipping Activity Messages (ASAM). Through the use of advanced geospatial analysis techniques in QGIS, the study seeks to pinpoint piracy hotspots, temporal fluctuations, and evolving patterns. The examination demonstrates that piracy continues to pose a persistent threat, with notable incidents occurring in areas like Somalia, the Gulf of Guinea, and Southeast Asia. The results highlight the variety of targeted vessels and forms of hostility, underscoring the necessity for customized security measures and international collaboration. This thorough analysis contributes to the enhancement of maritime security strategies and risk assessment for global shipping.
... In the first instance, a correlation matrix was created that allowed determining the relationship between the independent and dependent variables. In addition, causalities were calculated in the sense of Granger (1969) which made it possible to determine if the independent variables cause variations in the MAI remunerations; In order to determine the causalities, regressions were carried out with the vector autoregressive (VAR) method, and to determine the lag of the variable that provides optimal results, the Akaike Information Criterion (1974) was applied. ...
The United States, Mexico, and Canada (USMCA) seek to promote fair wages and adequate working conditions, especially in Mexico, by strengthening labor rights and freedom of association. The objective of this research is to determine the factors that influence salary levels in the Mexican Automotive Industry (MAI), through a causality analysis in the Granger sense, to generate a panorama that allows a decision-making process in the Mexican salary policy. With data from the National Institute of Statistics and Geography, the Bank of Mexico and Statista, autoregressive vector models were estimated to determine causalities in the Granger sense. It was proven that minimum wage, employed personnel, production, total sales, and exports are some causes of remuneration in the sector, with the minimum wage being the most significant. The above suggests that the salary increase involves several actors, such as the government (minimum wage), the organization (production, sales and exports) and the market (employed personnel), therefore, the design of appropriate labor policies will contribute to the dignification of salaries inside the MAI.
... This ensures that the tidal spectrum of the noise surrogates has the same spectral power as the input signals but removes any linear dependencies on the test time series. While the WTC method does not examine any direct measure of causality (such as Granger causality; Granger, 1969), it has some advantages over the kind of strict predictive causality implied in Granger tests and the more ill-defined general concepts of casualty. WTC has been used very widely (Ng and Chan, 2012;Bi et al., 2018;Yadav et al., 2022) because it is useful in describing correlations at multiple periodicities and quantifies phase relationships that can be statistically tested with appropriate noise models. ...
The mass loss from the neighboring Totten and Moscow University ice shelves is accelerating and may raise global sea levels in coming centuries. Totten Glacier is mostly based on bedrock below sea level, and so is vulnerable to warm water intrusion reducing its ice shelf buttressing. The mechanisms driving the ocean forced sub-ice-shelf melting remains to be further explored. In this study, we simulate oceanic-driven ice shelf melting of the Totten (TIS) and Moscow University ice shelves (MUIS) using a high spatiotemporal resolution model that resolves both eddy and tidal processes. We selected the year 2014 as representative of the period 1992 to 2017 to investigate how basal melting varies on spatial and temporal scales. We apply the wavelet coherence method to investigate the interactions between the two ice shelves in time-frequency space and hence estimate the contributions from tidal (<1.5 days) and eddy (2-35 days) components of the ocean heat transport to the basal melting of each ice shelf. In our simulation, the 2014 mean basal melt rate for TIS is 6.7 m yr⁻¹ (42 Gt yr⁻¹) and 9.7 m yr⁻¹ (52 Gt yr⁻¹) for MUIS. We find high wavelet coherence in the eddy dominated frequency band between the two ice shelves over almost the whole year. The wavelet coherence along five transects across the ice shelves suggests that TIS basal melting is dominated by eddy processes, while MUIS basal melting is dominated by tidal processes. The eddy-dominated basal melt for TIS is probably due to the large and convoluted bathymetric gradients beneath the ice shelf, weakening higher frequency tidal mode transport. This illustrates the key role of accurate bathymetric data plays in simulating on-going and future evolution of these important ice shelves.
... Yanlış korelasyonlardan kaçınmanın anahtarı ise nedensellik ilişkisinde bulunmaktadır. Granger (1969), nedensellik kavramını tanımlayarak, bir değişkenin diğerinin nedeni olduğunu belirtir: eğer bir değişkenin mevcut değerleri, diğer değişkenin gecikmiş değerleri tarafından açıklanabiliyorsa, bu değişken diğerinin Granger nedeni olmaktadır. Sims (1980) (Karacan, 2022). ...
Bu çalışmada, Azerbaycan'da ithalat, ihracat, yenilenebilir ve yenilenemeyen
enerji kullanımının GSYİH’a olan etkisini incelemektedir. Araştırma, 1990-2022
dönemine ait yıllık veriler kullanılarak gerçekleştirilmiştir. Çalışmada ilk kez,
Azerbaycan özelinde değişkenlerin durağanlıkları, Enders ve Lee (2012) ile Omay (2015) tarafından önerilen Fourier yaklaşımıyla araştırılmıştır. Ayrıca, eş-bütünleşme ilişkisi için Sam vd. (2019) tarafından önerilen Genişletilmiş ARDL sınır testi kullanılarak literatüre katkı sağlanması hedeflenmektedir. Durağanlık analizi sonucunda, yenilenemeyen enerji tüketimi dışındaki tüm değişkenlerin seviyede durağan olduğu bulgusuna ulaşılmıştır. Ardından, Genişletilmiş ARDL sınır testi kullanılarak değişkenler arasında eşbütünleşme ilişkisi tespit edilmiştir. Genişletilmiş ARDL uzun dönem tahmin sonuçlarına göre, Azerbaycan'da ithalat, ihracat, yenilenebilir ve yenilenemez enerji kullanımının GSYİH’yı artan yönde etkilediği görülmektedir. İthalat ve ihracatta %1'lik bir artışın, Azerbaycan’ın GSYİH ‘da sırasıyla %0,12 ve %0,33 oranında artışa sebep olduğu bulunmuştur. Öte yandan, yenilenemeyen ve yenilenebilir enerji tüketiminde %1'lik bir artışın, ortalama olarak Azerbaycan'ın ekonomik büyümesinde sırasıyla %0,37 ve %0,17 oranında bir artışa neden olduğu tespit edilmiştir. Ayrıca, değişkenler arasındaki nedensonuç ilişkisinin belirlenmesi için Nazlioglu vd. (2016) tarafından önerilen Fourier TodaYamamoto nedensellik testi kullanılmış ve Azerbaycan'da ithalat ve ihracat değişkenlerinden ekonomik büyümeye doğru tek yönlü nedensellik belirlenmiştir. Çalışma, Azerbaycan için çeşitli politika önerileri sunarak tamamlanmaktadır.
... We manipulate the influence using the Transfer Entropy, T Y →X , between the time series of state and action pairs. Transfer entropy is closely related to Wiener-Granger causality [30], and computes the conditional mutual information between two variables X t and Y t [31], ...
Communication is essential for successful interaction. In human-robot interaction, implicit communication enhances robots' understanding of human needs, emotions, and intentions. This paper introduces a method to foster implicit communication in HRI without explicitly modeling human intentions or relying on pre-existing knowledge. Leveraging Transfer Entropy, we modulate influence between agents in social interactions in scenarios involving either collaboration or competition. By integrating influence into agents' rewards within a partially observable Markov decision process, we demonstrate that boosting influence enhances collaboration or competition performance, while resisting influence diminishes performance. Our findings are validated through simulations and real-world experiments with human participants.
... Motivated by the now celebrated Granger causality test, cf. [19], in linear time series, [13] introduced the notion of transfer entropy to determine the causal relationship between two nonlinear time series. The transfer entropy from X j to Y j , say T X→Y (j) is defined in [13] to be ...
In this work, we present a method which determines optimal multi-step dynamic mode decomposition (DMD) models via entropic regression, which is a nonlinear information flow detection algorithm. Motivated by the higher-order DMD (HODMD) method of \cite{clainche}, and the entropic regression (ER) technique for network detection and model construction found in \cite{bollt, bollt2}, we develop a method that we call ERDMD that produces high fidelity time-delay DMD models that allow for nonuniform time space, and the time spacing is discovered by consider most informativity based on ER. These models are shown to be highly efficient and robust. We test our method over several data sets generated by chaotic attractors and show that we are able to build excellent reconstructions using relatively minimal models. We likewise are able to better identify multiscale features via our models which enhances the utility of dynamic mode decomposition.
... The application of causal discovery algorithms can resolve these issues (e.g. Granger (1969); Papagiannopoulou et al. (2017); Runge et al. (2019a); Runge et al. (2019b)). The causal discovery algorithm Peter and Clark Momentary Conditional Independence (PCMCI) enables the analysis of a high dimensional feature space and serially correlated time series to infer direct and indirect links between a set of univariate time series (Runge, Bathiany, et al. 2019;Runge, Nowack, et al. 2019). ...
With temperatures in Central Asia (CA) increasing more than the global average, this region is one of the global hotspots affected by climate change. CA is mostly characterized by arid climate, which is why available water resources are of paramount importance for the societies, economies, and the environment. In this regard, quantifying changes on the land surface and controlling factors that influence land surface dynamics are of great interest to improve our understanding of climate change impacts in this region. Hence, this study analyzes multivariate time series covering climatic, hydrological and Earth observation (EO)-based land surface variables. The used EO time series characterize the land surface and include data on the normalized difference vegetation index (NDVI), surface water area (SWA), and snow cover area (SCA) between December 2002 to November 2021. To analyze these time series, we employ trend analyses and a causal discovery algorithm. Both analyses were carried out at multiple spatial and temporal scales. The results show that NDVI trends were mostly significantly negative in the Northwest and positive in the Northeast of CA in summer. In summer and autumn, the percentage of significant negative NDVI trends outweighed the positive trends. For SWA, the detected trends were mostly significant negative throughout all scales. Significant negative trends were retrieved for SCA across all seasons, except for autumn regionally. Particularly the Tian Shan and Pamir mountains show significant declines of SCA in winter and spring. The causal analyses revealed that the NDVI is mostly controlled by water availability in summer. In spring and autumn, temperature is the leading driver on the NDVI. Likewise, temperature is found to largely control SWA in spring and autumn. SCA is mostly negatively coupled to temperature during spring and autumn. A positive coupling between SCA and precipitation is identified in winter.
... La relación entre la tasa de operaciones de subastas de reportos, con las tasas activas de corto plazo de los bancos comerciales, es consistente con lo que sugiere la prueba de precedencia temporal o causalidad en el sentido deGranger (1969), la cual es presentada en la Tabla A3. ...
Luego del choque adverso que atravesó Nicaragua en 2018, se introdujeron operaciones activas de reportos por parte del Banco Central, mismas que buscaban proveer liquidez en un momento de estrés económico y financiero. Esta provisión de liquidez se ha otorgado con una tasa de interés definida por la Autoridad Monetaria, o por condiciones de competencia, afectando directamente el costo de financiamiento de la banca a través del mecanismo de reportos. Este estudio pretende estimar la relación entre la tasa de interés de esas operaciones de reportos y las tasas activas de los bancos comerciales, entre 2018 y 2023. Los resultados indican que la tasa de operaciones activas del Banco Central ha presentado una relación positiva y significativa con la tasa activa de los bancos comerciales, siendo una relación más fuerte que la observada entre la tasa activa y la tasa de operaciones interbancarias.
... In this study, the Toda-Yamamoto causality test was applied. The traditional Granger test [79] allows for determining causality between variables when the series are stationary and contain a cointegration relationship. However, the Toda-Yamamoto test does not consider whether the series is stationary or contains a cointegration relationship. ...
Nuclear energy, along with renewable and alternative energy sources, is a crucial green energy source. However, the existing literature often overlooks the role of nuclear energy in achieving sustainable development goals. This study analyzes the impact of green technological innovation, nuclear energy consumption, and trade openness on environmental quality in the US, which consumed the most nuclear energy from 1990 to 2019. The ARDL bounds testing approach was applied for its effectiveness in smaller samples, suitable for the data set used in this study, to determine cointegration relationships. Additionally, the Toda-Yamamoto causality test was employed to explore causal links without requiring series stationarity or cointegration. The ARDL cointegration results indicate a significant long-term relationship between CO2 emissions, green technological innovation, nuclear energy consumption, and trade openness. The results suggest that promoting green technological innovation and nuclear energy (although this effect is less certain) can be effective strategies for reducing CO2 emissions, while the impact of trade openness requires careful consideration due to its potential to increase emissions. Green technological innovation has a significant unidirectional causal effect on CO2 emissions. These results will help policymakers design policies to achieve sustainable environmental goals in the US economy.
Purpose-The objective of this study is to reevaluate the correlation among pharmaceutical consumption, per capita income, and life expectancy across different age groups (at birth, middle age, and advanced age) within the OECD countries between 1998 and 2018. Design/methodology/approach-We employ a two-step methodology, utilizing two independent approaches. Firstly, we conduct the Dumitrescu-Hurlin pairwise panel causality test, followed by Machine Learning (ML) experiments employing the Causal Direction from Dependency (D2C) Prediction algorithm and a DeepNet process, thought to deliver robust inferences with respect to the nature, sign, direction, and significance of the causal relationships revealed in the econometric procedure. Findings-Our findings reveal a two-way positive bidirectional causal relationship between GDP and total pharmaceutical sales per capita. This contradicts the conventional notion that health expenditures decrease with economic development due to general health improvements. Furthermore, we observe that GDP per capita positively correlates with life expectancy at birth, 40, and 60, consistently generating positive and statistically significant predictive values. Nonetheless, the value generated by the input life expectancy at 60 on the target income per capita is negative (À61.89%), shedding light on the asymmetric and nonlinear nature of this nexus.
In this paper, we present an alternative approach to predictive modeling for future energy demands. It is based on the application of causal detection models to create specifications of how this demand might be caused by different environmental and social factors. We proceed by using a dataset generated by the wholesale electricity company of Argentina (CAMMESA) and selecting four prominent causal detection methods identified in the literature. These methods were selected based on their demonstrated effectiveness and widespread adoption. Since these causal detection methods yield different causal graphs, we were able to construct an ensemble model that achieved better performance for recovering the true causal structure when applied to the full dataset. Also, we show that the variables in the causal model can be used to yield more accurate forecasts of future demands, improving over the informal models used by staff in electricity utilities.
O objetivo principal deste trabalho é desenvolver uma investigação empírica sobre a ocorrência de uma crise sanitária com dados de morte e casos de COVID e seus possíveis impactos na volatilidade cambial para os BRICS com dados diários de 20 de fevereiro de 2020 até 28 de fevereiro de 2022 utilizando modelos ARDL, MQO modificado e MQO dinâmico. Os resultados apontam que o aumento no número de casos e mortes confirmados está associado a menores níveis de volatilidade cambial indicando que houve ineficiência das políticas monetárias e fiscais no cenário de crise sanitária generalizada em direcionar fluxos de capitais, e como a crise da COVID é global, durante o período analisado ocorreu uma maior estabilidade cambial, ou seja, uma menor volatilidade cambial.
Purpose
The purpose of this study is to evaluate the linkage between stock markets in Middle Eastern countries before and during the COVID-19 pandemic by using daily and monthly data sets for the period from 2011 to 2021.
Design/methodology/approach
The multivariate BEKK-GARCH model was computed to evaluate the existence of non-linear linkage among Middle Eastern stock markets. A correlation approach was used in this study to determine the type of linear connectivity between Middle Eastern stock markets. The study used monthly and daily data sets covering the years 2011 to 2021 to investigate the linkage between stock returns and the volatility spillover between the stock markets in Palestine, Jordan, Syria and Lebanon, both before and during COVID-19. To understand the types of relationships between markets before and during COVID-19, the daily data set was split into two periods.
Findings
Results from the pre-COVID-19 suggest that the Syria stock market is not related to any stock market in the Middle East markets; the Palestine and Lebanon stock markets exhibit a weak relationship, but Jordan and Palestine stock markets are strongly linked. Conversely, results from COVID-19 evince a very strong bidirectional volatility spillover between Middle East stock markets. Overall, the results indicate the existence of increased linkage during the COVID-19.
Research limitations/implications
The data collection on a daily and monthly basis, both before and during COVID-19, presents certain limitations for the paper. Another limitation is that the data cannot be generalized to all other Middle Eastern countries; rather, the conclusions drawn can only be applied to these four countries. This is especially true if the scholars collected most of the necessary data but were unable to obtain certain data for various reasons.
Practical implications
These findings have implications for risk management, market regulation and the growth of local stock markets. Facilitating the growth of smaller, more specialized markets to improve integration with other Middle Eastern markets is one of the goals of the domestic stock market development policy. To ensure financial stability, Middle Eastern stock market linking policies should consider spillover risk and take steps to minimize it. Enhancing the range of investment opportunities accessible to shareholders and functioning as confidential risk-sharing mechanisms to facilitate improved risk management in Middle Eastern stock markets will not only significantly influence the mobilization of private capital to promote investment and local economic growth but also lay groundwork for integrated market platforms.
Originality/value
This paper adds to the body of literature by demonstrating the nature of the connections between these small markets and the larger markets in the Middle East region. Information from the smaller markets provides institutional insights that enhance the body of existing research, guide the formulation of evidence-based policies and advance financial literacy in these markets. This study contributes by comparing data from different stock markets to better understand the type and strength of the link and relationship between Middle Eastern stock markets, as well as any underlying or reinforcing factors that might have contributed to the relationship and the specific types of links that these markets shared prior and during COVID-19.
This study highlights that green finance plays an important role in the field of sustainability and serves as a critical tool in achieving sustainability goals, especially in sectors such as food production, health, nutrition and ecological agriculture. The main purpose of the study is to examine how green financing plays a role in achieving sustainability goals and to analyze the impact of green bond issuances on global stock returns.
This study investigates the impact of green bonds issued worldwide between 2010 and 2023 on stock returns. Our study uses a large dataset containing stock indices of different countries using panel data analysis. Main concentration of this research is investigating a relationship between green bond issuances and stock returns and impact of economic growth rate on relationship between these two variables. Amprical evidence shows that there is a significant relationship between green bond issuances and global stock returns. This result reflects that green financing is an important tool in achieving sustainability goals and that green bond issuances increase investors' interest in environmentally responsible investments. The increasing trend observed in green bond issuances shows that financial markets are used effectively in combating environmental problems. it has been also observed that the economic growth rate does not affect the relationship between green bond issuances and stock returns. This shows that the relationship between green bond issuances and stock returns is independent of economic growth dynamics. The green financing effect occurs independently of economic growth dynamics.
The results of this study show that green finance plays an important role in the field of sustainability and that financial markets have the potential to provide solutions to environmental problems. Increasing green bond issuances show that investments sensitive to environmental issues are encouraged and that the financial sector plays a critical role in achieving sustainability goals. Highlighting the contributions of green finance to a sustainable economy highlights the need for policies and practices to support broader sustainability efforts in the future.
This article covers the topic of the relationship between the growth of companies and their financial condition when different business profiles are surveyed, such as high-tech, growth potential companies included in the NASDAQ Composite Index and traditional, matured companies included in the Dow Jones Industrial Average (DJIA), commonly known as blue-chip stocks. The hypothesis that the relationship between the Altman Z -score and the growth of enterprises as measured by assets, equity, sales, and earnings per share is positive was tested with Granger and VAR models. The main difference was found to be related to the size of the companies size and dynamics of growth. It was also shown that between NASDAQ enterprises there was no relationship between their growth and Altman Z -score, whereas when the DJIA blue chips were taken into account, a positive relationship was identified. It can be concluded that high-tech enterprises grow in a less predictable way not related to their economic condition, but mature enterprises focused on the growth and their condition. The value added of the article is finding that high-tech companies with growth potential and blue chips are managed in a different way due to their strategies of development.
Öz: Ekonomik büyümenin ham maddelerinden biri olan enerji, sürdürülebilir büyüme hedefinin sağlanması noktasında dikkate alınması gereken bir konu olmuştur. Ekonomik büyüme gerçekleştirilirken ekolojik çevreye verilen zararın en aza indirilmesi önem arz etmektedir. Bu sebeple sürdürülebilir büyüme için yenilenebilir enerji kaynaklarının etkin ve verimli kullanımına ihtiyaç duyulmaktadır. Bu çalışmada Türkiye’de 1995-2018 yılları arasında yenilenebilir enerji tüketimi ile büyüme arasındaki nedensellik analizi incelenmiştir. Çalışmada ilk olarak serilerin durağanlığını test etmek amacıyla bütün değişkenlere ait birim kök sınaması yapılmıştır. Optimal gecikme uzunluğunu belirlemek için bilgi kriteri kullanılmıştır. Vector AutoRegresif (VAR) modelinin kullanılıp kullanılmayacağı varsayım testleri uygulanmıştır. Bu çerçevede ilk varsayım olan otokorelasyon sorununun olmamasının tespiti için Otokorelasyon LM testi uygulanmıştır. İkinci varsayım olan değişen varyansın olmamasının analizi için White Heteroskedasticity testi kullanılmıştır. Varsayım testleri için son olarak parametre istikrar testi AR Roots testi ile sınanmıştır. VAR Granger Nedensellik testiyle de analize konu olan değişkenler arasındaki nedensellik sınaması yapılmıştır. VAR Granger Nedensellik testiyle, kısa dönemde, yenilenebilir enerji tüketiminden ekonomik büyümeye doğru tek yönlü bir nedensellik tespit edilmiştir. Elde edilen bulgular sonucunda Türkiye’de 1995-2018 yılları arasında büyüme hipotezinin geçerliliği söz konusudur. Dolayısıyla Türkiye’de geleneksel enerji kaynaklarının yenilenebilir enerji kaynaklarıyla değiştirilmesine yönelik politikaların desteklenmesi önem arz etmektedir.
Innovative applications of human movement analysis, for example, for mitigating/slowing down certain pathological conditions, have recently emerged from the modeling and automated measurement of full-body expressive midlevel individual and group movement qualities, at a higher complexity level than movement qualities derived directly from physical signals, still not characterizing any gesture in a specific way. More in general, the availability of automated analysis techniques of midlevel expressive movement qualities can contribute to interaction design incorporating body-based performance practices inspired by artistic theories in dance and music. This work investigates how such practices and techniques can support embodied interaction design by enabling automated measuring of cues of leadership, cohesion, and fluidity in full-body movement in group settings. In particular, the dance-inspired scientific approach, the data collection protocol, and the analysis techniques adopted for assessing movement qualities connected to leadership and cohesion within the group and fluidity of the dancers’ full-body movement are described. Finally, future developments of this research are outlined.
In real-life scenarios, joint consumption is common, particularly influenced by social relationships such as romantic ones. However, how romantic relationships affect consumption decisions and determine dominance remains unclear. This study employs electroencephalography hyperscanning to examine the neural dynamics of couples during joint-consumption decisions. Results show that couples, compared to friends and strangers, prefer healthier foods, while friends have significantly faster reaction times when selecting food. Time–frequency analysis indicates that couples exhibit significantly higher theta power, reflecting deeper emotional and cognitive involvement. Strangers show greater beta1 power, indicating increased cognitive effort and alertness due to unfamiliarity. Friends demonstrate higher alpha2 power when choosing unhealthy foods, suggesting increased cognitive inhibition. Inter-brain phase synchrony analysis reveals that couples display significantly higher inter-brain phase synchrony in the beta1 and theta bands across the frontal–central, parietal, and occipital regions, indicating more coordinated cognitive processing and stronger emotional bonds. Females in couples may be more influenced by emotions during consumption decisions, with detailed sensory information processing, while males exhibit higher cognitive control and spatial integration. Granger-causality analysis shows a pattern of male dominance and female dependence in joint consumption within romantic relationships. This study highlights gender-related neural synchronous patterns during joint consumption among couples, providing insights for further research in consumer decision-making.
The purpose of this work is development and research of an algorithm for determining the structure of couplings of an ensemble of chaotic self-oscillating systems. The method is based on the determination of causality by Granger and the use of direct propagation artificial neural networks trained with regularization. Results. We have considered a method for recognition structure of couplings of a network of chaotic maps based on the Granger causality principle and artificial neural networks approach. The algorithm demonstrates its efficiency on the example of small ensembles of maps with diffusion couplings. In addition to determining the network topology, it can be used to estimate the magnitue of the couplings. Accuracy of the method essencially depends on the observed oscillatory regime. It effectively works only in the case of homogeneous space-time chaos. Discussion. Although the method has shown its effectiveness for simple mathematical models, its applicability for real systems depends on a number of factors, such as sensitivity to noise, to possible distortion of the waveforms, the presence of crosstalks and external noise etc. These questions require additional research.
Business confidence refers to the level of optimism or pessimism that business owners have about the prospects of their companies and the overall economy. Thus, the focus of this study is to examine the long-term impact of various macroeconomic factors—economic growth, government expenditure, interest rates, inflation, exchange rates, and the composite stock price index—on the business confidence index in Indonesia by utilizing monthly data from January 2009 to December 2022. We employ Dynamic Ordinary Least Squares (DOLS) and Fully-Modified Ordinary Least Squares (FMOLS) as the main methods, with Canonical Cointegrating Regressions (CCR) as a robustness check method. The study also utilizes pairwise Granger causality tests for a comprehensive analysis. The findings indicate that all macroeconomic factors significantly impact the business confidence index in the long term across all methodologies. Specifically, economic growth, inflation, and the composite stock price index exert a positive impact, while government expenditure, interest rates, and exchange rates indicate a negative impact on the business confidence index. This evidence emphasizes the importance for businesses to diligently monitor macroeconomic trends and understand the patterns in these indicators so that companies can better anticipate changes in business sentiment. Taking a long-term perspective when making strategic decisions and investments is also advisable, recognizing that the influence of macroeconomic factors on business confidence may be more pronounced over time.
Objective: To demonstrate the capability of utilizing graph feature-based supervised machine learning (ML) algorithm on intracranial
electroencephalogram (iEEG) recordings for the identification of seizure onset zones (SOZs) in individuals with drug-resistant epilepsy. Approach: Utilizing three model-free measures of effective connectivity (EC) – directed information (DI), mutual information-guided Granger causality index (MI-GCI), and frequency-domain convergent cross-mapping (FD-CCM) – directed graphs are
generated. Graph centrality measures at different sparsity are used as the classifier’s features. Main results: The centrality features achieve high accuracies exceeding 90% in distinguishing SOZ electrodes from non-SOZ electrodes. Notably, a sparse graph representation with just ten features and simple machine learning models effectively achieves such performance. The study identifies
FD-CCM centrality measures as particularly significant, with a mean AUC of 0.93, outperforming prior literature. The FD-CCM-based graph modeling also highlights elevated centrality measures among SOZ electrodes, emphasizing heightened activity relative to non-SOZ electrodes during ictogenesis.
Significance: This research not only underscores the efficacy of automated SOZ identification but also illuminates the potential of specific EC measures in enhancing discriminative power within the context of epilepsy research.
In a study of size constancy, monkeys were trained with extended practice to choose the larger of two projected stimulus discs independently of distance. When surgical brain lesions were made following pre-operative training, it was found that posterior parietal lesions caused no deficit on this task while inferotemporal lesions caused a severe breakdown from which three out of four animals showed almost no recovery. Mathematical analysis showed that the pattern of performance after inferotemporal lesions could be described as due to a tendency to oscillate between a “correct” and two “incorrect strategies” and that this tendency was also apparent to at least some extent in the unoperated animals. It is argued that the two incorrect strategies might have resulted from a disturbance of size constancy, such that the animal became unable to use both retinal size and distance information in computing physical size and instead used either retinal size or distance information alone. Parallels are drawn between such an hypothetical perceptual disorder and certain clinical disturbances in man.The Institute of Experimental Psychology, 1 South Parks Road, Oxford, England
The important data of economics are in the form of time series; therefore, the statistical methods used will have to be those designed for time series data. New methods for analyzing series containing no trends have been developed by communication engineering, and much recent research has been devoted to adapting and extending these methods so that they will be suitable for use with economic series. This book presents the important results of this research and further advances the application of the recently developed Theory of Spectra to economics. In particular, Professor Hatanaka demonstrates the new technique in treating two problems-business cycle indicators, and the acceleration principle existing in department store data.
In this paper is presented a simultaneous equation model that is non-triangular, its disturbances having a non-diagonal covariance matrix, and yet is causal and recursive, and whose underlying structural relations are also causal. The model is put forth as a counter-example to the Wold-Strotz claim that only triangular systems can be recursive and causal.
This paper discusses one of the uses to which two powerful techniques of modern time series analysis may be put in economics: namely, the study of the precise effects of seasonal adjustment procedures on the characteristics of the series to which they are applied. Since most economic data appearing at intervals of less than a year are to a greater or lesser extent "manufactured" from more basic time series, the problem of assessing the effects of the "manufacturing" processes upon the essential characteristics of the raw material to which they are applied is not unimportant. Perhaps the most common type of adjustment applied to raw economic time series is that designed to eliminate so-called seasonal fluctuations. The precise nature of seasonality is not easy to define, but an attempt is made in Section 2.1 below. The techniques employed to study the effects of seasonal adjustment procedures are those of spectral and cross-spectral analysis. In somewhat oversimplified terms the basic idea behind these types of analysis is that a stochastic time series may be decomposed into an infinite number of sine and cosine waves with infinitesimal random amplitudes. Spectral analysis deals with a single time series in terms of its frequency "content": cross-spectral analysis deals with the relation between two time series in terms of their respective frequency "contents." The two techniques are discussed in both theoretical and practical terms. Spectral analyses have been made for about seventy-five time series of United States employment, unemployment, labor force, and various categories thereof. Cross-spectral analyses have been made of the relations between these series and the corresponding series as seasonally adjustment by the procedures used by the Bureau of Labor Statistics. Two major conclusions regarding the effects of the BLS seasonal adjustment procedures emerge from these emerge from these analyses. First, these procedures remove far more from the series to which they are applied than can properly be considered as seasonal. Second, if the relation between two seasonally adjusted series in time is compared with the corresponding relation between the original series in time, it is found that there is a distortion due to the process of seasonal adjustment itself. Both defects impair the usefulness of the seasonally adjusted series as indicators of economic conditions, but, of the two, temporal distortion is the more serious defect. Examples of some of these results are discussed below in Section 3.3.
Causal Ordering and Identifiability Studies in Econometric Method
- H A Simon
SIMON, H. A.: "Causal Ordering and Identifiability," Studies in Econometric Method (edited by W. C. Hood and T. C. Koopmans), Cowles Commission Monograph 14, New York, 1953.