Presence Of Granger Causality Research Articles

Quantifying Evidence for—and against—Granger Causality with Bayes Factors

Testing for Granger causality relies on estimating the capacity of dynamics in one time series to forecast dynamics in another. The canonical test for such temporal predictive causality is based on fitting multivariate time series models and is cast in the classical null hypothesis testing framework. In this framework, we are limited to rejecting the null hypothesis or failing to reject the null – we can never validly accept the null hypothesis of no Granger causality. This is poorly suited for many common purposes, including evidence integration, feature selection, and other cases where it is useful to express evidence against, rather than for, the existence of an association. Here we derive and implement the Bayes factor for Granger causality in a multilevel modeling framework. This Bayes factor summarizes information in the data in terms of a continuously scaled evidence ratio between the presence of Granger causality and its absence. We also introduce this procedure for the multilevel generalization of Granger causality testing. This facilitates inference when information is scarce or noisy or if we are interested primarily in population-level trends. We illustrate our approach with an application on exploring causal relationships in affect using a daily life study.

Systemic risk in the Chinese financial system: A panel Granger causality analysis

In this paper, we investigate China’s changing financial interconnectedness via the presence of Granger-causality between firm level factors (Leverage, Market To Book Value and Returns) and systemic risk measures (ΔCoVaR, MES, and SRISK ). The analysis is based on 161 Chinese financial intermediaries (14 Traditional Banks, 16 Finance Services, 131 Real Estate Finance Developers) continuously listed over the period 2007:1–2021:1. We find that, in addition to traditional banks, finance companies and real estate finance developers pose systemic threats to the Chinese financial system, in particular during the Global Financial Crisis and the 2015 Chinese stock crash. Finally, the outbreak of COVID-19 pandemic has put under strain the Chinese financial system, in particular the finance services.

Tourism, Macroeconomics, Growth, and the St. Louis Equation

This study tested the following research questions: (a) Is tourism development helping to untangle the exogenous from the endogenous of monetary and fiscal policies behavior? (b) Is tourism development a determinant of economic growth in the presence of the macroeconomic variables? (c) Is there a stable long-run relationship among economic growth, tourism development, monetary, and fiscal policies in Nicaragua? (d) Is there a presence of Granger causality? Using nominal and real values, this study tested the adequacy of the expanded St. Louis equation with three methodologies: the Almon lag-distributed methodology; the autoregressive distributed lag bounds testing approach to cointegration; and the Granger methodology was applied to investigate causality. The data covered the period 1960–2016. First, the results have indicated that tourism development and monetary policy bear the task of the short-run adjustment to a long-run equilibrium. In the long-run, a 1% of sustained growth rate in tourism development would imply an estimated increase in gross domestic product (in real terms) of nearly 0.41%. Second, there is a log-run relationship among economic growth, monetary policy, fiscal policy, and tourism developments. Third, the results confirm the hypothesis that tourism development is an important, direct, and independent economic sector in explaining economic activity changes, in the case of Nicaragua. Fourth, there is unidirectional or one-way (TR → GDP) Granger causal effect running from tourism development to economic growth. This study has provided useful guidance for policymakers engaged in tourism policy formulation, a modeling and quantitative reference material to academia, and other researchers who might be interested in conducting research in similar or related areas of the study.

The Causal Relationship between Military Spending and Unemployment in the EU15

This paper analyses the possible presence of Granger causality between military spending and unemployment rates in the EU15 countries. The panel bootstrap test applied allows us to control for both the presence of cross-country heterogeneity and cross-sectional dependence. Considering two alternative measures of military spending, we find little evidence against the null hypothesis according to which it does not cause unemployment. These statistically significant causal relationships are found in countries that devote a higher share of their defence budget expenditures to personnel.

Granger causality between debt and growth: Evidence from OECD countries

This paper analyzes the possible presence of Granger causality between debt and growth in 16 OECD countries from 1980 to 2009. This is done considering not only government debt but also non-financial corporate and household debt. The panel bootstrap Granger causality test applied allows us to control for both the presence of cross-country heterogeneity and cross-sectional dependence. Our results barely provide evidence against the null hypothesis according to which government debt does not cause real GDP growth. More interestingly, we find evidence against the absence of causality from non-financial private debt – especially that of households – to growth.

Testing price volume relationships for Indian commodity futures

PurposeThe nature of price volume relationship in asset market has been an interesting subject in financial research as it reveals a very important aspect which has implications for market efficiency. The purpose of this paper is to examine price volume relationships in Indian commodity futures market.Design/methodology/approachThere are two competing models in price volume relationship. Mixture of distribution hypothesis, suggesting a positive contemporaneous relationship and sequential information arrival hypothesis (SIH), suggesting a positive intertemporal causal relationship. Both are tested using correlation coefficient and Granger causality test with vector auto regressive methodology.FindingsThough there exists contemporaneous correlation between volume and price change in some of the cases, but in general on the basis of the presence of Granger causality it follows that SIH is supported.Research limitations/implicationsAs only three commodities futures have been studied in this paper, this study can be extended to include more number of commodities currently being traded so as to make it more exhaustive.Practical implicationsThe research has been done with the data of MCX Gold, MCX Silver and MCX Crude. The results of causality suggest that inefficiency level is maximum in Silver which may be attributed to informational asymmetry.Originality/valueThe Indian commodity futures market is of very recent origin. Hence, very little research work has been undertaken in this space. The paper presents an assessment of the existence of informational asymmetry among the three commodity futures under the study.

Multivariate Subset Autoregressive Modelling with Zero Constraints for Detecting 'Overall Causality' - Financial and Economic Forecasting (Chapter 4)

The necessary and sufficient condition to test for 'overall causality', i.e., the presence of Granger-causality and instantaneous causal relations, in a bivariate and trivariate autoregressive model with recursive form is discussed. It is argued that the conventional AR model (the reduced form AR) is a more straightforward and effective means of testing for 'overall causality'. To detect instantaneous causality it is proposed to select the best subset system in a residual regression system in conjunction with model selection criteria. The Canadian money-income-bank rate system is re-examined in this way and by using a previously proposed algorithm we identify the optimum multivariate subset AR with constraints to detect whether there is 'overall causality' in that system.

Cointegration and a Test of the Quantity Theory of Money

The main implication of the Quantity Theory of Money is that long-run movements in the price level are determined primarily by long-run movements in the excess of money over real output. This implication is related to the concept of cointegration discussed in Granger (1986), which states cointegrated multiple time series share common long-run movements. It is shown that the general price level is cointegrated with money, real output, and the nominal rate of interest. These economic variables enter a price equation based on the Equation of Exchange. Furthermore, the appearance of this cointegration in the data seems consistent with the presence of Granger-causality from money and real output to the price level. It is also shown that an inflation equation that incorporates the above stated implication of the Quantity Theory of Money predicts quite well the actual behavior of inflation during the past decade or so. These results however hold for M2, not M1, measure of money.

On granger causality and the monetary approach to the balance of payments

It is shown that the presence of Granger causality from the reserves variable to the domestic credit variable is neither a necessary nor a sufficient condition for the presence of simultaneous equations bias in the standard reserve-flow equation of the monetary approach to the balance of payments. It is suggested that Hausman-type tests may be more suitable in this context. The argument is illustrated by means of an applied example using United Kingdom data.