A Partial Granger Causality Approach to Explore Causal Networks Derived From Multi-parameter Data

2008 ◽  
pp. 9-27 ◽  
Author(s):  
Ritesh Krishna ◽  
Shuixia Guo
Keyword(s):  
Granger Causality ◽  
Causal Networks

scholarly journals Dynamic Contagion of Systemic Risks on Global Main Equity Markets Based on Granger Causality Networks

2018 ◽  
Vol 2018 ◽  
pp. 1-13 ◽  
Author(s):  
Qiuhong Zheng ◽  
Liangrong Song
Keyword(s):  
Granger Causality ◽  
Stock Markets ◽  
Global Financial Crisis ◽  
Critical Role ◽  
Stock Index ◽  
Causality Test ◽  
Complex Network Theory ◽  
Significant Difference ◽  
Causal Networks ◽  
Global Stock Markets

A total of 156 Granger causal networks of stock markets are constructed by using the Granger causality test and time series sliding window based on stock index data of 34 major stock markets in the world from 2004 to 2017. The topological structures and evolution characteristics of the Granger causal networks are analyzed from the perspective of complex network theory. Empirical results demonstrate that the network topology has a significant difference during the global financial crisis and other periods. The causal relationships among different global stock markets exhibit a jump growth when each major crisis occurs. The contagion path is also short. A causal relationship between any two stock markets can usually be established with one stock market on average, not by using more than five stock markets. For risk contagion, the American stock markets exerted the largest influence in 12 years, followed by the European stock markets. Stock markets with high intermediate contagion ability play an important role in systemic risk contagion. Despite the crucial markets in Europe and America (e.g., USA, Brazil, and Mexico), stock markets with weak network correlation and strong media ability (e.g., the markets of Japan, Korea, Australia, and New Zealand) play a critical role in risk contagion.

How Normative are Inferences on Causal Networks?

2012 ◽  
Author(s):  
Reid Hastie ◽  
Benjamin M. Rottman
Keyword(s):  
Causal Networks

Tax revenue and economic growth in developing country: an autoregressive distribution lags approach

2020 ◽  
Vol 7 (54) ◽  
pp. 205-217
Author(s):  
Mnaku Honest Maganya
Keyword(s):  
Economic Growth ◽  
Granger Causality ◽  
Testing Procedure ◽  
Gdp Growth ◽  
Bounds Testing ◽  
Causality Test ◽  
Significance Level ◽  
Distributed Lag ◽  
Distributed Lag Model ◽  
The Government

AbstractTanzania, like most other developing countries, faces numerous economic challenges in striving to achieve sustainable economic growth and development through taxation. In the literature, the debate on how effective taxes are as a tool for promoting economic growth and economic development remains inconclusive, as various research have reported mixed effects of tax on economic growth. This article investigates the effect of taxation on economic growth in Tanzania using the recently developed technique of autoregressive distributed lag model (ARDL) bounds testing procedure for the period from 1996 to 2019. Various preliminary tests were conducted including stationary tests as well as the pair-wise Granger causality test. According to the results obtained, domestic goods and services (TGS) taxes are positively related to GDP growth and are statistically significant at 1% level. Income taxes, on the other hand, were found to be negatively related to GDP growth and to be statistically significant at 5% level. The pair-wise Granger causality results indicated that there is bidirectional Granger causality between TGS and GDP growth at 1 % significance level. The government should aim at growing, nurturing and sustaining tax base to positively drive economic growth even further.

Fraud and Bank Performance in Nigeria – Var Granger Causality Analysis

e-Finanse ◽  
2020 ◽  
Vol 16 (1) ◽  
pp. 20-26
Author(s):  
Taiwo A. Muritala ◽  
Muftau A. Ijaiya ◽  
Olatanwa H. Afolabi ◽  
Abdulrasheed B. Yinus
Keyword(s):  
Control Systems ◽  
Granger Causality ◽  
Internal Control ◽  
Banking Sector ◽  
Bank Performance ◽  
Causality Analysis ◽  
Vector Autoregressive ◽  
Granger Causality Analysis ◽  
Long Run ◽  
Multivariate Cointegration

AbstractThis paper examines the causality between fraud and bank performance in Nigeria over the period 2000-2016 for quarterly financial data using Johansen’s Multivariate Cointegration Model and Vector Autoregressive (VAR) Granger Causality analysis. The results show a long-run relationship between the variables. Bank performance was found to be linked to Granger fraud variables and vice versa at 10% significant level. This study reveals that there was a direct causal relationship between bank performance and fraud because increase in fraudulent activities in the banking sector leads to reduction in bank performance. Hence, this study recommends that internal control systems of banks should be strengthened so as to detect and prevent fraud. In this way, bank assets would be protected.

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