Search results for " causality"

showing 10 items of 109 documents

Causal flows between oil and forex markets using high-frequency data: Asymmetries from good and bad volatility

2019

The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link. This paper investigates the causal linkages in volatility between crude oil prices and six major bilateral exchange rates against the U.S. dollar in the time-frequency space using high-frequency intraday data. Special attention is paid to the potential asymmetries in the causal effects between oil and forex markets. The wavelet-based Granger causality method proposed by Olayeni (2016) is applied to quantify the causal relations in the time and frequency domains simultaneously. Moreover, the realized semivariance approach of Barndoff-Nielsen et a…

Economics and EconometricsRealized variance020209 energycrude oil prices02 engineering and technologyMonetary economicsexchange ratesrealized volatilityGranger causality0502 economics and business0202 electrical engineering electronic engineering information engineeringEconomics050207 economics05 social scienceswavelet analysisgood and bad volatilityhigh-frequency dataGeneral EnergyCurrencyFinancial crisisLiberian dollarGranger causalityFinancializationVolatility (finance)Foreign exchange marketasymmetry
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Aggregate uncertainty and sectoral productivity growth: The role of credit constraints

2016

Abstract We show that an increase in aggregate uncertainty—measured by stock market volatility—reduces productivity growth more in industries that depend heavily on external finance. The mechanism at play is that during periods of high uncertainty, firms that are credit constrained switch the composition of investment by reducing productivity-enhancing investment—such as on ICT capital—which is more subject to liquidity risks (Aghion et al., 2010). The effect is larger during recessions, when financing constraints are more likely to be binding, than during expansions. Our statistical method—a difference-in-difference approach using productivity growth of 25 industries from 18 advanced econo…

Economics and Econometricsmedia_common.quotation_subjectMonetary economicsRecession0502 economics and businessEconomicsEconometrics050207 economicsTotal factor productivityProductivityGeneral Environmental Sciencemedia_commonInformation and communication technology investmentReverse causality050208 finance05 social sciencesInstrumental variableAggregate (data warehouse)UncertaintySettore SECS-P/02 Politica EconomicaOmitted-variable biasInvestment (macroeconomics)Fiscal policyMarket liquidityEconometric modelFinancial dependenceProductivity growthOutput gapGeneral Earth and Planetary SciencesStock marketFinance
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The Influence of Oil Price on Renewable Energy Stock Prices: An Analysis for Entrepreneurs

2020

Abstract This study investigates the relationship between oil price fluctuations and renewable energy stock returns using daily data on Brent crude oil prices and global renewable energy stock market indices between 29 November 2010 and 18 February 2020. The investigation is based on the existing evidence on positive correlations between stock prices and oil prices, but it also considers the shift from non-renewable to renewable sources of energy. A two-stage GARCH(1,1) model and a Granger causality test were applied. Our results show that volatility clustering is present in the renewable energy companies‘ stock prices, but, oil price volatility does not seem to induce any significant effec…

Economics and Econometricsoil price020209 energyStrategy and ManagementAutoregressive conditional heteroskedasticity02 engineering and technologyMonetary economicssymbols.namesakeRegional economics. Space in economicsgranger causalityGranger causalitygarch0502 economics and business0202 electrical engineering electronic engineering information engineeringEconomics050207 economicsBusiness and International ManagementHB71-74Stock (geology)Volatility clusteringglobal renewable energy indicesbusiness.industry05 social sciencesStock market indexRenewable energyBrent CrudeEconomics as a scienceHT388symbolsOil pricebusinessFinanceStudia Universitatis Vasile Goldis Arad, Seria Stiinte Economice
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Spectral decomposition of cerebrovascular and cardiovascular interactions in patients prone to postural syncope and healthy controls.

2022

We present a framework for the linear parametric analysis of pairwise interactions in bivariate time series in the time and frequency domains, which allows the evaluation of total, causal and instantaneous interactions and connects time- and frequency-domain measures. The framework is applied to physiological time series to investigate the cerebrovascular regulation from the variability of mean cerebral blood flow velocity (CBFV) and mean arterial pressure (MAP), and the cardiovascular regulation from the variability of heart period (HP) and systolic arterial pressure (SAP). We analyze time series acquired at rest and during the early and late phase of head-up tilt in subjects developing or…

Endocrine and Autonomic SystemsTime series analysisBlood PressureHeartBaroreflexCardiovascular SystemSyncopeCerebral autoregulationCellular and Molecular NeuroscienceHeart RateAutoregressive modelsCardiovascular controlCerebrovascular CirculationGranger causalitySettore ING-INF/06 - Bioingegneria Elettronica e InformaticaHumansNeurology (clinical)Spectral decompositionAutoregressive models; Cardiovascular control; Cerebral autoregulation; Granger causality; Spectral decomposition; Time series analysis;Autonomic neuroscience : basicclinical
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Stock Earnings and Bond Yields in the US 1871 - 2016: The Story of a Changing Relationship

2018

Using historical data that spans almost 150 years, we examine whether there is a long-run equilibrium relationship between the stock's earnings and bond yields. The novelty of our econometric methodology consists in using a vector error correction model where we allow multiple structural breaks in the equilibrium relationship. The results of our analysis suggest the existence of equilibrium relationship over 1871-1929 and 1958-2017. On the two historical segments, our analysis finds that the stock's earnings yield followed the bond yield in both the short- and long-run, but not the other way around. Perhaps the most important and surprising finding of our empirical study is that, after the …

Error correction modelFed modelEarnings yieldEarningsGranger causalityBondStock valuationEconometricsStock (geology)SSRN Electronic Journal
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Demography and Economic Growth in Spain: A Time Series Analysis

2003

In this paper, advanced time series econometric tools are employed to test the existence of relationships among demographic and macroeconomic variables in Spain along the 1960-2000 period. Annual data for the total fertility rate, infant mortality rate, per capita gross domestic product and wages are used in the empirical analysis. We first examine the bivariate Granger causality to look for short run relations. Then, a multivariate cointegration analysis is carry out, showing that two long run relationships among the variables exist with statistically significant coefficients. From these cointegration vectors, the vector error correction model is estimated to test the endogenous or exogeno…

Error correction modelShort runCointegrationGranger causalityTotal fertility ratemedia_common.quotation_subjectEconomicsEconometricsFertilityBivariate analysisGross domestic productmedia_commonSSRN Electronic Journal
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A cross-country study of skills and unemployment flows

2021

AbstractUsing an international survey that directly assesses the cognitive skills of the adult population, I study the relation between skills and unemployment flows across 37 countries. Depending on the specifically assessed domain, I document that skills have an unconditional correlation with the log-risk-ratio of exiting to entering unemployment of 0.65–0.68 across the advanced and skill-abundant countries in the sample. The relation is remarkably robust and it is unlikely to be due to reverse causality. I do not find evidence that this positive relation extends to the seven relatively less advanced and less skill-abundant countries in the sample: Peru, Ecuador, Indonesia, Mexico, Chile,…

ErziehungswissenschaftEconomicsJ24Adult populationArbeitslosigkeitinternationaler Vergleich20100Gross worker flowscognitive abilityPIAACEconomicsLabor Market Research050207 economicsBildung und Erziehung050205 econometrics media_commonReverse causalitySkillsSurvey of Adult Skills05 social sciencesWirtschaftEducation and PedagogicsI20BildungsniveauunemploymentInternational comparisonsmedia_common.quotation_subjecteducationSample (statistics)level of educationEducationGross worker flows; Skills; Survey of Adult Skills PIAACddc:370Humankapitalparasitic diseases0502 economics and businessddc:330human capitalCognitive skillCross countrykognitive FaktorenArbeitsmarktforschungInternational surveyinternational comparisonUnemployment10600Demographic economicsJ20J64Bildungcognitive factorskognitive FähigkeitJ60Journal for Labour Market Research
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On the interpretability and computational reliability of frequency-domain Granger causality

2017

This Correspondence article is a comment which directly relates to the paper “A study of problems encountered in Granger causality analysis from a neuroscience perspective” (Stokes and Purdon, 2017). We agree that interpretation issues of Granger causality (GC) in neuroscience exist, partially due to the historically unfortunate use of the name “causality”, as described in previous literature. On the other hand, we think that Stokes and Purdon use a formulation of GC which is outdated (albeit still used) and do not fully account for the potential of the different frequency-domain versions of GC; in doing so, their paper dismisses GC measures based on a suboptimal use of them. Furthermore, s…

FOS: Computer and information sciences0301 basic medicineTheoretical computer scienceImmunology and Microbiology (all)Computer scienceTime series analysiMathematics - Statistics TheoryStatistics Theory (math.ST)Statistics - ApplicationsGeneral Biochemistry Genetics and Molecular BiologyMethodology (stat.ME)Causality (physics)03 medical and health sciences0302 clinical medicinegranger causalityGranger causalityCorrespondenceFOS: MathematicsApplications (stat.AP)Physiological oscillationGeneral Pharmacology Toxicology and PharmaceuticsTime seriessignal processingStatistical Methodologies & Health Informaticsfrequency-domain connectivityReliability (statistics)Statistics - MethodologyInterpretabilityGranger-Geweke causalityBiochemistry Genetics and Molecular Biology (all)Interpretation (logic)General Immunology and Microbiologybrain connectivityGeneral MedicineArticlesvector autoregressive models030104 developmental biologyMathematics and StatisticsWildcardVector autoregressive modelPharmacology Toxicology and Pharmaceutics (all)Frequency domaintime series analysisspectral decompositionSettore ING-INF/06 - Bioingegneria Elettronica E InformaticaBrain connectivity; Directed coherence; Frequency-domain connectivity; Granger-Geweke causality; Physiological oscillations; Spectral decomposition; Time series analysis; Vector autoregressive models; Biochemistry Genetics and Molecular Biology (all); Immunology and Microbiology (all); Pharmacology Toxicology and Pharmaceutics (all)directed coherence030217 neurology & neurosurgeryphysiological oscillations
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Critical comments on EEG sensor space dynamical connectivity analysis

2019

Many different analysis techniques have been developed and applied to EEG recordings that allow one to investigate how different brain areas interact. One particular class of methods, based on the linear parametric representation of multiple interacting time series, is widely used to study causal connectivity in the brain. However, the results obtained by these methods should be interpreted with great care. The goal of this paper is to show, both theoretically and using simulations, that results obtained by applying causal connectivity measures on the sensor (scalp) time series do not allow interpretation in terms of interacting brain sources. This is because (1) the channel locations canno…

FOS: Computer and information sciencesComputer scienceSocial SciencesTransfer functionStatistics - Applications050105 experimental psychology03 medical and health sciences0302 clinical medicinegranger causalityMVARHumansApplications (stat.AP)Computer Simulation0501 psychology and cognitive sciencesRadiology Nuclear Medicine and imagingBrain connectivityEEGTime domainSpurious relationshipRepresentation (mathematics)Mixing (physics)Parametric statisticsBrain MappingRadiological and Ultrasound TechnologySeries (mathematics)05 social sciencesbrain connectivitysource modellingElectroencephalographyNeurologyFOS: Biological sciencesFrequency domainQuantitative Biology - Neurons and CognitionSettore ING-INF/06 - Bioingegneria Elettronica E InformaticaGranger causalityDirected transfer functionNeurons and Cognition (q-bio.NC)Neurology (clinical)AnatomyAlgorithm030217 neurology & neurosurgery
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Local Granger causality

2021

Granger causality is a statistical notion of causal influence based on prediction via vector autoregression. For Gaussian variables it is equivalent to transfer entropy, an information-theoretic measure of time-directed information transfer between jointly dependent processes. We exploit such equivalence and calculate exactly the 'local Granger causality', i.e. the profile of the information transfer at each discrete time point in Gaussian processes; in this frame Granger causality is the average of its local version. Our approach offers a robust and computationally fast method to follow the information transfer along the time history of linear stochastic processes, as well as of nonlinear …

FOS: Computer and information sciencesInformation transferGaussianFOS: Physical sciencestechniques; information theory; granger causalityMachine Learning (stat.ML)Quantitative Biology - Quantitative Methods01 natural sciences010305 fluids & plasmasVector autoregressionsymbols.namesakegranger causalityGranger causalityStatistics - Machine Learning0103 physical sciencesApplied mathematicstime serie010306 general physicsQuantitative Methods (q-bio.QM)Mathematicsinformation theoryStochastic processDisordered Systems and Neural Networks (cond-mat.dis-nn)Condensed Matter - Disordered Systems and Neural NetworksComputational Physics (physics.comp-ph)Discrete time and continuous timeAutoregressive modelFOS: Biological sciencesSettore ING-INF/06 - Bioingegneria Elettronica E InformaticasymbolsTransfer entropytechniquesPhysics - Computational Physics
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