Granger causality

About: Granger causality is a research topic. Over the lifetime, 12565 publications have been published within this topic receiving 267540 citations. The topic is also known as: Granger causality test.

Detecting Causality in Complex Ecosystems

Abstract: Identifying causal networks is important for effective policy and management recommendations on climate, epidemiology, financial regulation, and much else. We introduce a method, based on nonlinear state space reconstruction, that can distinguish causality from correlation. It extends to nonseparable weakly connected dynamic systems (cases not covered by the current Granger causality paradigm). The approach is illustrated both by simple models (where, in contrast to the real world, we know the underlying equations/relations and so can check the validity of our method) and by application to real ecological systems, including the controversial sardine-anchovy-temperature problem.

Some recent developments in a concept of causality

Abstract: The paper considers three separate but related topics. (i) What is the relationship between causation and co-integration? If a pair of I(1) series are co-integration, there must be causation in at least one direction. An implication is that some tests of causation based on different series may have missed one source of causation. (ii) Is there a need for a definition of 'instantaneous causation' in a decision science? It is argued that no such definition is required. (iii) Can causality tests be used for policy evaluation? It is suggested that these tests are useful, but that they should be evaluated with case.

Testing for Linear and Nonlinear Granger Causality in the Stock Price-Volume Relation

Abstract: Linear and nonlinear Granger causality tests are used to examine the dynamic relation between daily Dow Jones stock returns and percentage changes in New York Stock Exchange trading volume. We find evidence of significant bidirectional nonlinear causality between returns and volume. We also examine whether the nonlinear causality from volume to returns can be explained by volume serving as a proxy for information flow in the stochastic process generating stock return variance as suggested by Clark's (1973) latent common-factor model. After controlling for volatility persistence in returns, we continue to find evidence of nonlinear causality from volume to returns.

The Association between Outside Directors, Institutional Investors and the Properties of Management Earnings Forecasts

Abstract: We investigate the relation of the board of directors and institutional ownership with the properties of management earnings forecasts. We find that firms with more outside directors and greater institutional ownership are more likely to issue a forecast and are inclined to forecast more frequently. In addition, these forecasts tend to be more specific, accurate and less optimistically biased. These results are robust to changes specification, Granger causality tests, and simultaneous equation analyses. The results are similar in the pre– and post–Regulation Fair Disclosure (Reg FD) eras. Additional analysis suggests that concentrated institutional ownership is negatively associated with forecast properties. This association is less negative in the post–Reg FD environment, which is consistent with Reg FD reducing the ability of firms to privately communicate information to select audiences.