Abstract
In advance of focusing in subsequent chapters on the main theme of the measures of interdependency, Chap. 1 provides a brief overview of the literature on empirical causal analysis and places the theme in a broader perspective, comparing a variety of conflicting views on how certain statistical associations can be viewed as causal. Among others, alluded to is the field experiment model of detecting causal effects by Neyman (1923) and its reliance on a counterfactual assumption. Controlled random experiments are compared with observational studies in econometrics. The concepts of causality and exogeneity in the framework of the simultaneous equation are discussed. Specifically, ancillarity and conditioning in statistical inferences are explained and their relation to exogeneity is expounded. A preliminary concept of Granger causality is introduced, and the role of prediction improvement in empirical analyses is emphasized.
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Hosoya, Y., Oya, K., Takimoto, T., Kinoshita, R. (2017). Introduction. In: Characterizing Interdependencies of Multiple Time Series. SpringerBriefs in Statistics(). Springer, Singapore. https://doi.org/10.1007/978-981-10-6436-4_1
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