This paper examines the relationship between the female labour force participation rate and total fertility rate for the G7 countries over the period 1960 to 2004 using panel unit root, panel cointegration, Granger causality and long-run structural estimation. The paper’s main findings are that the female labour force participation rate and total fertility rate are cointegrated for the panel of...

In this paper, we consider the role of “leads” of the first difference of integrated variables in the dynamic OLS estimation of cointegrating regression models. Specifically, we investigate Stock and Watson’s (1993) claim that the role of leads is related to the concept of Granger causality by a Monte Carlo simulation. From the simulation results, we find that the dynamic OLS estimator without ...

This paper analyzes empirically the effect of crude oil price change on the economic growth of Indian-Subcontinent (India, Pakistan and Bangladesh). We use a multivariate Vector Autoregressive analysis followed by Wald Granger causality test and Impulse Response Function (IRF). Wald Granger causality test results show that only India’s economic growth is significantly affected when crude oil pr...

This paper estimates the causal relationships between energy consumption and income for India, Indonesia, the Philippines and Thailand, using cointegration and error-correction modelling techniques. The results indicate that, in the short-run, unidirectional Granger causality runs from energy to income for India and Indonesia, while bidirectional Granger causality runs from energy to income for...

This article employs cointegration and error-correction modelling to test the causal relationship between real income, exports and human capital stock using data for China over the period 1960 to 1999. We find that real exports, human capital and real income are cointegrated when real exports is the dependent variable, but are not cointegrated when human capital or real income are the dependent...

We propose a method of analysis of dynamical networks based on a recent measure of Granger causality between time series, based on kernel methods. The generalization of kernel-Granger causality to the multivariate case, here presented, shares the following features with the bivariate measures: (i) the nonlinearity of the regression model can be controlled by choosing the kernel function and (ii...

The rapidly growing literature on the relationship between energy consumption and economic growth has not univocally identified the ‘real’causal relationship yet. We argue that bivariate models, which analyze the causality at the level of the total economy, are not appropriate – especially in cases where both variables do not cover the same scope of economic activity. After discussing appropria...

In this research paper, attempt has been made to explore the relation especially the causal relation between stock market index i.e. BSE Sensex and three key macro economic variables of Indian economy by using correlation, unit root stationarity tests and Granger causality test. Monthly data has been used from April,1995 to March, 2009 for all the variables, like, BSE Sensex, wholesale price in...

Learning temporal causal structures among multiple time series is one of the major tasks in mining time series data. Granger causality is one of the most popular techniques in uncovering the temporal dependencies among time series; however it faces two main challenges: (i) the spurious effect of unobserved time series and (ii) the computational challenges in high dimensional settings. In this p...

Estimation of causal interactions between brain areas is necessary for elucidating large-scale functional brain networks underlying behavior and cognition. Granger causality analysis of time series data can quantitatively estimate directional information flow between brain regions. Here, we show that such estimates are significantly improved when the temporal sampling rate of functional magneti...