T his empirical analysis endeavors to trace out the causal nexus between core inflation and economic growth from the perspective of twenty worlds’ leading economy with the help of the nonlinear Granger causality approach by using time series data from 1981 to 2016. Based on nonlinear Granger causality results, it has been found that there is unidirectional casualty running from core ...

Identifying dynamic causal relationships between financial time series may help explain market dynamics. The Granger causality (G-causality) test is a method to detect linear causal relationships between time series. However, there exists significant evidence for nonlinear causality between financial time series. Hence, several nonlinear extensions of G-causality (NLG-causality) were proposed. ...

1 Nonlinear Causality, with Applications to Liquidity and Stochastic Volatility Abstract The conditional Laplace transform is often easier to use in financial data analysis than the conditional density. This paper characterizes nonlinear causality hypotheses for models based on the conditional Laplace transform and provides interpretations of the linear and quadratic causality in this framework...

The traditional linear Granger test has been widely used to examine the linear causality among several time series in bivariate settings as well as multivariate settings. Hiemstra and Jones [19] develop a nonlinear Granger causality test in bivariate settings to investigate the nonlinear causality between stock prices and trading volume. This paper extends their work by developing a non-linear ...

the relationship between financial development and economic growth in order to give priority to policies that will lead to financial development or addressing other economic priorities to achieve economic growth has attracted the attention of many economics. nonlinear models of causality and causal relationships between variables in the modification of variables in different regimes exist, why ...

The concepts of weak, strong and strict Granger causality are introduced for nonlinear time series models. 1-step ahead predictions are formed using the conditional expectation. The weak form is related to Granger's original deenition for linear predictors in that it is based on the forecast error variance, whereas the strong form concerns the conditional variance, and the strict form the condi...

Article history: Received 10 April 2006 Received in revised form 4 August 2009 Accepted 20 August 2009 Available online 28 August 2009 This paper uses both linear and nonlinear causality tests to reexamine the causal relationship between the returns on large and small firms. Consistent with previous results, we find that large firms linearly lead small firms. We also find a significant linear c...

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 implementi...

Causality analytic techniques based on conditional mutual information are described. Causality analysis may be used to infer linear and nonlinear causal relations between selected brain regions, and can account for identified non-causal confounds. The analysis results in a directed graph whose nodes are brain regions, and whose edges represent information flow. This causal information measure i...

We analyze a set of complex time series from the view point of nonlinear causality. The mathematical background for analyzing time series is an extension of embedding theories of autonomous systems to an input{output system. We consider that the existence of nonlinear causality can be detected by nonlinear predictability of input and output sequences. Several numerical examples are given for co...