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- Springer Netherlands
- Hindawi Publishing Corporation
- Mary Ann Liebert, Inc.
- Public Library of Science
- Academic Press
- Universidade Nacional da Austrália
- Instituto Superior de Economia e Gestão
- Universidade Cornell
- Universidade de São Paulo. Faculdade de Economia, Administração e Contabilidade de RP
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## More discussions for granger causality and new causality measures

Fonte: Springer Netherlands
Publicador: Springer Netherlands

Tipo: Artigo de Revista Científica

Português

Relevância na Pesquisa

47.11%

Granger causality (GC) has been widely applied in economics and neuroscience to reveal causality influence of time series. In our previous paper (Hu et al., in IEEE Trans on Neural Netw, 22(6), pp. 829–844, 2011), we proposed new causalities in time and frequency domains and particularly focused on new causality in frequency domain by pointing out the shortcomings/limitations of GC or Granger-alike causality metrics and the advantages of new causality. In this paper we continue our previous discussions and focus on new causality and GC or Granger-alike causality metrics in time domain. Although one strong motivation was introduced in our previous paper (Hu et al., in IEEE Trans on Neural Netw, 22(6), pp. 829–844, 2011) we here present additional motivation for the proposed new causality metric and restate the previous motivation for completeness. We point out one property of conditional GC in time domain and the shortcomings/limitations of conditional GC which cannot reveal the real strength of the directional causality among three time series. We also show the shortcomings/limitations of directed causality (DC) or normalize DC for multivariate time series and demonstrate it cannot reveal real causality at all. By calculating GC and new causality values for an example we demonstrate the influence of one of the time series on the other is linearly increased as the coupling strength is linearly increased. This fact further supports reasonability of new causality metric. We point out that larger instantaneous correlation does not necessarily mean larger true causality (e.g....

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## Statistical Analysis of Single-Trial Granger Causality Spectra

Fonte: Hindawi Publishing Corporation
Publicador: Hindawi Publishing Corporation

Tipo: Artigo de Revista Científica

Português

Relevância na Pesquisa

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Granger causality analysis is becoming central for the analysis of interactions between neural populations and oscillatory networks. However, it is currently unclear whether single-trial estimates of Granger causality spectra can be used reliably to assess directional influence. We addressed this issue by combining single-trial Granger causality spectra with statistical inference based on general linear models. The approach was assessed on synthetic and neurophysiological data. Synthetic bivariate data was generated using two autoregressive processes with unidirectional coupling. We simulated two hypothetical experimental conditions: the first mimicked a constant and unidirectional coupling, whereas the second modelled a linear increase in coupling across trials. The statistical analysis of single-trial Granger causality spectra, based on t-tests and linear regression, successfully recovered the underlying pattern of directional influence. In addition, we characterised the minimum number of trials and coupling strengths required for significant detection of directionality. Finally, we demonstrated the relevance for neurophysiology by analysing two local field potentials (LFPs) simultaneously recorded from the prefrontal and premotor cortices of a macaque monkey performing a conditional visuomotor task. Our results suggest that the combination of single-trial Granger causality spectra and statistical inference provides a valuable tool for the analysis of large-scale cortical networks and brain connectivity.

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## Investigating Effective Brain Connectivity from fMRI Data: Past Findings and Current Issues with Reference to Granger Causality Analysis

Fonte: Mary Ann Liebert, Inc.
Publicador: Mary Ann Liebert, Inc.

Tipo: Artigo de Revista Científica

Publicado em /10/2012
Português

Relevância na Pesquisa

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Interactions between brain regions have been recognized as a critical ingredient required to understand brain function. Two modes of interactions have held prominence—synchronization and causal influence. Efforts to ascertain causal influence from functional magnetic resonance imaging (fMRI) data have relied primarily on confirmatory model-driven approaches, such as dynamic causal modeling and structural equation modeling, and exploratory data-driven approaches such as Granger causality analysis. A slew of recent articles have focused on the relative merits and caveats of these approaches. The relevant studies can be classified into simulations, theoretical developments, and experimental results. In the first part of this review, we will consider each of these themes and critically evaluate their arguments, with regard to Granger causality analysis. Specifically, we argue that simulations are bounded by the assumptions and simplifications made by the simulator, and hence must be regarded only as a guide to experimental design and should not be viewed as the final word. On the theoretical front, we reason that each of the improvements to existing, yet disparate, methods brings them closer to each other with the hope of eventually leading to a unified framework specifically designed for fMRI. We then review latest experimental results that demonstrate the utility and validity of Granger causality analysis under certain experimental conditions. In the second part...

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## BOLD Granger Causality Reflects Vascular Anatomy

Fonte: Public Library of Science
Publicador: Public Library of Science

Tipo: Artigo de Revista Científica

Publicado em 13/12/2013
Português

Relevância na Pesquisa

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A number of studies have tried to exploit subtle phase differences in BOLD time series to resolve the order of sequential activation of brain regions, or more generally the ability of signal in one region to predict subsequent signal in another region. More recently, such lag-based measures have been applied to investigate directed functional connectivity, although this application has been controversial. We attempted to use large publicly available datasets (FCON 1000, ADHD 200, Human Connectome Project) to determine whether consistent spatial patterns of Granger Causality are observed in typical fMRI data. For BOLD datasets from 1,240 typically developing subjects ages 7–40, we measured Granger causality between time series for every pair of 7,266 spherical ROIs covering the gray matter and 264 seed ROIs at hubs of the brain’s functional network architecture. Granger causality estimates were strongly reproducible for connections in a test and replication sample (n=620 subjects for each group), as well as in data from a single subject scanned repeatedly, both during resting and passive video viewing. The same effect was even stronger in high temporal resolution fMRI data from the Human Connectome Project, and was observed independently in data collected during performance of 7 task paradigms. The spatial distribution of Granger causality reflected vascular anatomy with a progression from Granger causality sources...

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## Canonical Granger Causality between Regions of Interest

Fonte: PubMed
Publicador: PubMed

Tipo: Artigo de Revista Científica

Português

Relevância na Pesquisa

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Estimating and modeling functional connectivity in the brain is a challenging problem with potential applications in the understanding of brain organization and various neurological and neuropsychological conditions. An important objective in connectivity analysis is to determine the connections between regions of interest in the brain. However, traditional functional connectivity analyses have frequently focused on modeling interactions between time series recordings at individual sensors, voxels, or vertices despite the fact that a single region of interest will often include multiple such recordings. In this paper, we present a novel measure of interaction between regions of interest rather than individual signals. The proposed measure, termed canonical Granger causality, combines ideas from canonical correlation and Granger causality analysis to yield a measure that reflects directed causality between two regions of interest. In particular, canonical Granger causality uses optimized linear combinations of signals from each region of interest to enable accurate causality measurements from substantially less data compared to alternative multivariate methods that have previously been proposed for this scenario. The optimized linear combinations are obtained using a variation of a technique developed for optimization on the Steifel manifold. We demonstrate the advantages of canonical Granger causality in comparison to alternative causality measures for a range of different simulated datasets. We also apply the proposed measure to local field potential data recorded in a macaque brain during a visuomotor task. Results demonstrate that canonical Granger causality can be used to identify causal relationships between striate and prestriate cortex in cases where standard Granger causality is unable to identify statistically significant interactions.

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## Granger causality revisited

Fonte: Academic Press
Publicador: Academic Press

Tipo: Artigo de Revista Científica

Publicado em 01/11/2014
Português

Relevância na Pesquisa

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This technical paper offers a critical re-evaluation of (spectral) Granger causality measures in the analysis of biological timeseries. Using realistic (neural mass) models of coupled neuronal dynamics, we evaluate the robustness of parametric and nonparametric Granger causality. Starting from a broad class of generative (state-space) models of neuronal dynamics, we show how their Volterra kernels prescribe the second-order statistics of their response to random fluctuations; characterised in terms of cross-spectral density, cross-covariance, autoregressive coefficients and directed transfer functions. These quantities in turn specify Granger causality — providing a direct (analytic) link between the parameters of a generative model and the expected Granger causality. We use this link to show that Granger causality measures based upon autoregressive models can become unreliable when the underlying dynamics is dominated by slow (unstable) modes — as quantified by the principal Lyapunov exponent. However, nonparametric measures based on causal spectral factors are robust to dynamical instability. We then demonstrate how both parametric and nonparametric spectral causality measures can become unreliable in the presence of measurement noise. Finally...

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## Spatio-temporal Granger causality: a new framework

Fonte: PubMed
Publicador: PubMed

Tipo: Artigo de Revista Científica

Português

Relevância na Pesquisa

47.16%

That physiological oscillations of various frequencies are present in fMRI
signals is the rule, not the exception. Herein, we propose a novel theoretical framework,
spatio-temporal Granger causality, which allows us to more reliably and precisely estimate
the Granger causality from experimental datasets possessing time-varying properties caused
by physiological oscillations. Within this framework, Granger causality is redefined as a
global index measuring the directed information flow between two time series with
time-varying properties. Both theoretical analyses and numerical examples demonstrate that
Granger causality is a monotonically increasing function of the temporal resolution used
in the estimation. This is consistent with the general principle of coarse graining, which
causes information loss by smoothing out very fine-scale details in time and space. Our
results confirm that the Granger causality at the finer spatio-temporal scales
considerably outperforms the traditional approach in terms of an improved consistency
between two resting-state scans of the same subject. To optimally estimate the Granger
causality, the proposed theoretical framework is implemented through a combination of
several approaches, such as dividing the optimal time window and estimating the parameters
at the fine temporal and spatial scales. Taken together...

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## Dynamic Granger causality based on Kalman filter for evaluation of functional network connectivity in fMRI data

Fonte: PubMed
Publicador: PubMed

Tipo: Artigo de Revista Científica

Português

Relevância na Pesquisa

47.01%

Increasing interest in understanding dynamic interactions of brain neural networks leads to formulation of sophisticated connectivity analysis methods. Recent studies have applied Granger causality based on standard multivariate autoregressive (MAR) modeling to assess the brain connectivity. Nevertheless, one important flaw of this commonly proposed method is that it requires the analyzed time series to be stationary, whereas such assumption is mostly violated due to the weakly nonstationary nature of functional magnetic resonance imaging (fMRI) time series. Therefore, we propose an approach to dynamic Granger causality in the frequency domain for evaluating functional network connectivity in fMRI data. The effectiveness and robustness of the dynamic approach was significantly improved by combining a forward and backward Kalman filter that improved estimates compared to the standard time-invariant MAR modeling. In our method, the functional networks were first detected by independent component analysis (ICA), a computational method for separating a multivariate signal into maximally independent components. Then the measure of Granger causality was evaluated using generalized partial directed coherence that is suitable for bivariate as well as multivariate data. Moreover...

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## The FDI-led growth hypothesis: further econometric evidence from China

Fonte: Universidade Nacional da Austrália
Publicador: Universidade Nacional da Austrália

Tipo: Working/Technical Paper
Formato: 76073 bytes; application/pdf

Português

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#China#foreign direct investment#FDI#vector autoregression#VAR model#economic growth#real output growth#two-way Granger causality

Despite a large volume of econometric literature on the impacts of foreign direct investment (FDI) on economic growth in developing countries, the question of causality linkage between them has only been investigated very recently. This paper re-examines the FDI-led growth hypothesis in the case of China, a country which has become one of the major FDI recipient countries in the world. The study is based upon quarterly time series data and a vector autoregresion (VAR) model applying the Granger no-causality procedure, developed by Toda and Yamamoto (1995), to test the causal link between the inflow of FDI and real output growth. Four distinct features in this paper stand out as follows: first, the FDI-led growth study on China applying Granger no-causality testing procedure is the first attempt in the literature and hence is attractive; second, we have gone beyond the traditional two-variable relationship by building a six-variable VAR model in the production function context to avoid the possible specification bias; third, we follow Riezman, Whiteman and Summers (1996) to test the hypothesis while controlling for the growth of imports to avoid producing a spurious causality result; and finally, the methodology by Toda and Yamamoto is expected to improve the standard F-statistics in the causality test process. The results emerging from our research indicate a two-way Granger causality running between output growth and FDI inflows.

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## Impacto do preço do petróleo no mercado accionista dos Estados Unidos, França, Holanda, Bélgica e Portugal : análise por sector de actividade

Fonte: Instituto Superior de Economia e Gestão
Publicador: Instituto Superior de Economia e Gestão

Tipo: Dissertação de Mestrado

Publicado em //2011
Português

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#Preço do petróleo#Mercado accionista#Sectores de actividade#Gestão de portfólios#Efeitos assimétricos#Modelo de valorização de dois factores#mercado e petróleo#Causalidade de Granger#Oil price#Stock market#USA

Mestrado em Finanças; A presente investigação que contribui para o conhecimento da relação entre o preço do petróleo e o retorno do mercado accionista norte-americano estruturando-se em torno das seguintes questões: (1) As variações do preço do petróleo têm impacto no retorno do mercado accionista?; (2) As variações do preço do petróleo têm impacto no retorno dos sectores?; (3) Quando existem efeitos estes são assimétricos?; e (4) Existe causalidade de Granger? Usando dados do DataSream Advance para o período de 1 de Janeiro de 2000 a 31 de Março de 2011 de dez sectores de actividade do índice S&P 500 e do preço do Petróleo (WTI) foi estimado o modelo de mercado (por sectores) e o modelo de preços de dois factores (mercado e petróleo). Testou-se a assimetria entre as variáveis, assim como a Causalidade de Granger. Conclui-se através do modelo de mercado que o índice S&P 500 explica as variações dos sectores. Quando introduzida a variável designada por a variação inesperada no preço do petróleo e aplicado o modelo de dois factores verifica-se a existência de relação entre as variações do preço do petróleo e alguns sectores. Os sectores que apresentam uma associação positiva são o da Energia e o dos Materiais e uma associação negativa os sectores da Saúde...

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## A Granger Causality Measure for Point Process Models of Ensemble Neural Spiking Activity

Fonte: Public Library of Science
Publicador: Public Library of Science

Tipo: Artigo de Revista Científica

Português

Relevância na Pesquisa

47.01%

The ability to identify directional interactions that occur among multiple neurons in the brain is crucial to an understanding of how groups of neurons cooperate in order to generate specific brain functions. However, an optimal method of assessing these interactions has not been established. Granger causality has proven to be an effective method for the analysis of the directional interactions between multiple sets of continuous-valued data, but cannot be applied to neural spike train recordings due to their discrete nature. This paper proposes a point process framework that enables Granger causality to be applied to point process data such as neural spike trains. The proposed framework uses the point process likelihood function to relate a neuron's spiking probability to possible covariates, such as its own spiking history and the concurrent activity of simultaneously recorded neurons. Granger causality is assessed based on the relative reduction of the point process likelihood of one neuron obtained excluding one of its covariates compared to the likelihood obtained using all of its covariates. The method was tested on simulated data, and then applied to neural activity recorded from the primary motor cortex (MI) of a Felis catus subject. The interactions present in the simulated data were predicted with a high degree of accuracy...

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## Identification and quantification of Granger causality between gene sets

Fonte: Universidade Cornell
Publicador: Universidade Cornell

Tipo: Artigo de Revista Científica

Publicado em 05/11/2009
Português

Relevância na Pesquisa

47.21%

Wiener and Granger have introduced an intuitive concept of causality between
two variables which is based on the idea that an effect never occurs before its
cause. Later, Geweke has generalized this concept to a multivariate Granger
causality, i.e., n variables Granger-cause another variable. Although Granger
causality is not "effective causality", this concept is useful to infer
directionality and information flow in observational data. Granger causality is
usually identified by using VAR models due to their simplicity. In the last few
years, several VAR-based models were presented in order to model gene
regulatory networks. Here, we generalize the multivariate Granger causality
concept in order to identify Granger causalities between sets of gene
expressions, i.e., whether a set of n genes Granger-causes another set of m
genes, aiming at identifying and quantifying the flow of information between
gene networks (or pathways). The concept of Granger causality for sets of
variables is presented. Moreover, a method for its identification with a
bootstrap test is proposed. This method is applied in simulated and also in
actual biological gene expression data in order to model regulatory networks.
This concept may be useful to understand the complete information flow from one
network or pathway to the other...

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## The relation between Granger causality and directed information theory: a review

Fonte: Universidade Cornell
Publicador: Universidade Cornell

Tipo: Artigo de Revista Científica

Publicado em 13/11/2012
Português

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This report reviews the conceptual and theoretical links between Granger
causality and directed information theory. We begin with a short historical
tour of Granger causality, concentrating on its closeness to information
theory. The definitions of Granger causality based on prediction are recalled,
and the importance of the observation set is discussed. We present the
definitions based on conditional independence. The notion of instantaneous
coupling is included in the definitions. The concept of Granger causality
graphs is discussed. We present directed information theory from the
perspective of studies of causal influences between stochastic processes.
Causal conditioning appears to be the cornerstone for the relation between
information theory and Granger causality. In the bivariate case, the
fundamental measure is the directed information, which decomposes as the sum of
the transfer entropies and a term quantifying instantaneous coupling. We show
the decomposition of the mutual information into the sums of the transfer
entropies and the instantaneous coupling measure, a relation known for the
linear Gaussian case. We study the multivariate case, showing that the useful
decomposition is blurred by instantaneous coupling. The links are further
developed by studying how measures based on directed information theory
naturally emerge from Granger causality inference frameworks as hypothesis
testing.

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## Spatio-temporal Granger causality: a new framework

Fonte: Universidade Cornell
Publicador: Universidade Cornell

Tipo: Artigo de Revista Científica

Português

Relevância na Pesquisa

47.16%

That physiological oscillations of various frequencies are present in fMRI
signals is the rule, not the exception. Herein, we propose a novel theoretical
framework, spatio-temporal Granger causality, which allows us to more reliably
and precisely estimate the Granger causality from experimental datasets
possessing time-varying properties caused by physiological oscillations. Within
this framework, Granger causality is redefined as a global index measuring the
directed information flow between two time series with time-varying properties.
Both theoretical analyses and numerical examples demonstrate that Granger
causality is a monotonically increasing function of the temporal resolution
used in the estimation. This is consistent with the general principle of coarse
graining, which causes information loss by smoothing out very fine-scale
details in time and space. Our results confirm that the Granger causality at
the finer spatio-temporal scales considerably outperforms the traditional
approach in terms of an improved consistency between two resting-state scans of
the same subject. To optimally estimate the Granger causality, the proposed
theoretical framework is implemented through a combination of several
approaches, such as dividing the optimal time window and estimating the
parameters at the fine temporal and spatial scales. Taken together...

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## Analyzing Multiple Nonlinear Time Series with Extended Granger Causality

Fonte: Universidade Cornell
Publicador: Universidade Cornell

Tipo: Artigo de Revista Científica

Publicado em 08/05/2004
Português

Relevância na Pesquisa

47.04%

#Nonlinear Sciences - Chaotic Dynamics#Mathematics - Statistics Theory#Physics - Data Analysis, Statistics and Probability

Identifying causal relations among simultaneously acquired signals is an
important problem in multivariate time series analysis. For linear stochastic
systems Granger proposed a simple procedure called the Granger causality to
detect such relations. In this work we consider nonlinear extensions of
Granger's idea and refer to the result as Extended Granger Causality. A simple
approach implementing the Extended Granger Causality is presented and applied
to multiple chaotic time series and other types of nonlinear signals. In
addition, for situations with three or more time series we propose a
conditional Extended Granger Causality measure that enables us to determine
whether the causal relation between two signals is direct or mediated by
another process.; Comment: 16 pages, 6 figures

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## Multivariate Granger Causality and Generalized Variance

Fonte: Universidade Cornell
Publicador: Universidade Cornell

Tipo: Artigo de Revista Científica

Português

Relevância na Pesquisa

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#Quantitative Biology - Neurons and Cognition#Physics - Data Analysis, Statistics and Probability#Quantitative Biology - Quantitative Methods#Statistics - Methodology

Granger causality analysis is a popular method for inference on directed
interactions in complex systems of many variables. A shortcoming of the
standard framework for Granger causality is that it only allows for examination
of interactions between single (univariate) variables within a system, perhaps
conditioned on other variables. However, interactions do not necessarily take
place between single variables, but may occur among groups, or "ensembles", of
variables. In this study we establish a principled framework for Granger
causality in the context of causal interactions among two or more multivariate
sets of variables. Building on Geweke's seminal 1982 work, we offer new
justifications for one particular form of multivariate Granger causality based
on the generalized variances of residual errors. Taken together, our results
support a comprehensive and theoretically consistent extension of Granger
causality to the multivariate case. Treated individually, they highlight
several specific advantages of the generalized variance measure, which we
illustrate using applications in neuroscience as an example. We further show
how the measure can be used to define "partial" Granger causality in the
multivariate context and we also motivate reformulations of "causal density"
and "Granger autonomy". Our results are directly applicable to experimental
data and promise to reveal new types of functional relations in complex
systems...

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## Granger causality for state space models

Fonte: Universidade Cornell
Publicador: Universidade Cornell

Tipo: Artigo de Revista Científica

Português

Relevância na Pesquisa

47.04%

Granger causality, a popular method for determining causal influence between
stochastic processes, is most commonly estimated via linear autoregressive
modeling. However, this approach has a serious drawback: if the process being
modeled has a moving average component, then the autoregressive model order is
theoretically infinite, and in finite sample large empirical model orders may
be necessary, resulting in weak Granger-causal inference. This is particularly
relevant when the process has been filtered, downsampled, or observed with
(additive) noise - all of which induce a moving average component and are
commonplace in application domains as diverse as econometrics and the
neurosciences. By contrast, the class of autoregressive moving average models -
or, equivalently, linear state space models - is closed under digital
filtering, downsampling (and other forms of aggregation) as well as additive
observational noise. Here, we show how Granger causality, conditional and
unconditional, in both time and frequency domains, may be calculated simply and
directly from state space model parameters, via solution of a discrete
algebraic Riccati equation. Numerical simulations demonstrate that Granger
causality estimators thus derived have greater statistical power and smaller
bias than pure autoregressive estimators. We conclude that the state space
approach should be the default for (linear) Granger causality estimation.; Comment: 13 pages...

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## Relating Granger causality to directed information theory for networks of stochastic processes

Fonte: Universidade Cornell
Publicador: Universidade Cornell

Tipo: Artigo de Revista Científica

Português

Relevância na Pesquisa

47.06%

This paper addresses the problem of inferring circulation of information
between multiple stochastic processes. We discuss two possible frameworks in
which the problem can be studied: directed information theory and Granger
causality. The main goal of the paper is to study the connection between these
two frameworks. In the case of directed information theory, we stress the
importance of Kramer's causal conditioning. This type of conditioning is
necessary not only in the definition of the directed information but also for
handling causal side information. We also show how directed information
decomposes into the sum of two measures, the first one related to Schreiber's
transfer entropy quantifies the dynamical aspects of causality, whereas the
second one, termed instantaneous information exchange, quantifies the
instantaneous aspect of causality. After having recalled the definition of
Granger causality, we establish its connection with directed information
theory. The connection is particularly studied in the Gaussian case, showing
that Geweke's measures of Granger causality correspond to the transfer entropy
and the instantaneous information exchange. This allows to propose an
information theoretic formulation of Granger causality.; Comment: submitted...

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## Retornos das ações e o lucro: Avaliação da relevância da informação contábil; STOCK RETURNS AND PROFIT: ASSESS THE RELEVANCE OF ACCOUNTING INFORMATION

Fonte: Universidade de São Paulo. Faculdade de Economia, Administração e Contabilidade de RP
Publicador: Universidade de São Paulo. Faculdade de Economia, Administração e Contabilidade de RP

Tipo: info:eu-repo/semantics/article; info:eu-repo/semantics/publishedVersion; ; Pesquisa Empirica; ; ; ; ; ;
Formato: application/pdf

Publicado em 31/12/2012
Português

Relevância na Pesquisa

47.07%

#Contabilidade e Finanças#Retorno das ações#Informação contábil#Causalidade de Granger.##Stock returns#Accounting information#Granger Causality.

Este artigo objetiva verificar, através do teste de causalidade de Granger, o relacionamento entre as séries trimestrais dos lucros contábeis e os retornos de mercado (RET) de empresas brasileiras com ações em bolsa que apresentam níveis diferentes de exigências de divulgação. A amostra analisada foi composta por 75 empresas listadas na BM&FBOVESPA, durante o período de 1995 à 2010. As variáveis analisadas foram os retornos contábeis (ROE) e os retornos de mercado (RET). Na análise dos resultados, verificou-se, ao nível de 5% de significância estatística, que boa parte das empresas apresentou pelo menos algum sentido de causalidade, ou do ROE para o RET, ou do RET para o ROE. Foi averiguado também, pela análise em nível agregado dos P-valores do teste de Granger, que há bicausalidade entre ROE e RET, concluindo-se que o mercado brasileiro de ações não possui eficiência de mercado na amostra analisada. Destaca-se, dentre os principais resultados desta pesquisa, que não foi constatado que a causalidade entre ROE e RET é maior para empresas que possuem maior nível de exigência de divulgações das informações contábeis, permitindo inferir que os lucros líquidos das empresas que divulgam mais informações contábeis não possuem maior causalidade com o retorno de mercado do que as demais empresas. Com isso...

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## Relationship between investment in intangibles and total factor productivity: a study of the brazilian industrial sector; Relación entre inversiones en intangibles y productividad total de factores: un estudio del sector industrial brasileño; Relação entre investimentos em intangíveis e produtividade total de fatores: um estudo do setor industrial brasileiro

Fonte: UFSC
Publicador: UFSC

Tipo: info:eu-repo/semantics/article; info:eu-repo/semantics/publishedVersion;
Formato: application/pdf

Publicado em 11/12/2014
Português

Relevância na Pesquisa

47.2%

##Intangibility. TFP. Industrial production. Granger causality.##Intangibilidad. Productividad total de factores. Producción industrial. Causalidad de Granger.#Contabilidade#Organizações Industriais#Intangibilidade. Produtividade total de fatores. Produção industrial. Causalidade de Granger.

The present study aimed to investigate the intangibility and total factor productivity (TFP) and verify causality relationship between these two variables in the Brazilian industrial sector. To that end, the variables were analyzed using the Granger causality test. The results of Granger causality tests showed that two of the twelve sectors analyzed showed causality of GI in the sense of Granger for TFP. On the other hand, two sectors showed causality of TFP in the sense of Granger for GI. These results concluded that the intangibility of the Brazilian industrial sector, despite having growth, has not yet reached the levels of intangibility of the companies belonging to the most developed countries.; El presente estudio tuvo como objetivo investigar la intangibilidad y la productividad total de factores (PTF) y verificar la relación de causalidad entre esas dos variables en el sector industrial brasileño. Para eso, las variables fueron analizadas por medio del test de causalidad de Granger. Los resultados de los tests de causalidad de Granger mostraron que de los doce sectores analizados, dos presentaban relación de causalidad del GI en el sentido de Granger para la PTF. Por otro lado, dos sectores presentaron relación de causalidad de la PTF en el sentido de Granger para el GI. Esos resultados permitieron concluir que la intangibilidad del sector industrial brasileño...

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