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- W2227439381 abstract "Granger causality models are very popular when it comes to making decisions on which of a number of series of scores is on the dependent versus the independent side. With this chapter, we pursue two goals. First, we specify Granger causality models in terms of logit models and compare these with the routinely applied linear regression models. The comparison shows that, in order to make the models parallel, either model assumptions must be changed or model terms must be removed from (or inserted into) the model specification. The second goal involves extending Granger causality modeling. We propose conditioning terms on measures within the observed series. By implication, these models require higher-order interactions. In addition, model terms can be conditioned on covariates. Issues concerning parameter interpretation are discussed. Data examples are given from the fields of aggression development in adolescents and intimate partner violence." @default.
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- W2227439381 date "2015-01-01" @default.
- W2227439381 modified "2023-10-14" @default.
- W2227439381 title "Granger Causality: Linear Regression and Logit Models" @default.
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- W2227439381 doi "https://doi.org/10.1007/978-3-319-20585-4_6" @default.
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