Titlebook: Characterizing Interdependencies of Multiple Time Series; Theory and Applicati Yuzo Hosoya,Kosuke Oya,Ryo Kinoshita Book 2017 The Author(s)

初生

書目名稱Characterizing Interdependencies of Multiple Time Series影響因子(影響力)




書目名稱Characterizing Interdependencies of Multiple Time Series影響因子(影響力)學科排名




書目名稱Characterizing Interdependencies of Multiple Time Series網(wǎng)絡公開度




書目名稱Characterizing Interdependencies of Multiple Time Series網(wǎng)絡公開度學科排名




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書目名稱Characterizing Interdependencies of Multiple Time Series被引頻次學科排名




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書目名稱Characterizing Interdependencies of Multiple Time Series年度引用學科排名




書目名稱Characterizing Interdependencies of Multiple Time Series讀者反饋




書目名稱Characterizing Interdependencies of Multiple Time Series讀者反饋學科排名

招待

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學術(shù)討論會

Representation of the Partial Measures,he problem, this chapter introduces an operational way to define the partial causality and allied concepts between a pair of processes. The third-effect elimination is of the one-way effect component of the third series from a pair of subject-matter series to preserve the inherent feedback structure of the pair of interest.

debase

Internetnutzung am Arbeitsplatz,geneity 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.

Nucleate

Nucleate

Outsourcing von IT-Dienstleistungen,test statistic to determine whether such a change is associated with a structural change and is statistically significant. The properties of the measure and the test statistic are examined through a Monte Carlo simulation, and empirical examples are provided.

STERN

Introduction,geneity 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.

Cardioversion

The Measures of One-Way Effect, Reciprocity, and Association,ty matrix. The other two are based on distributed-lag representation and innovation orthogonalization, respectively. Each approach provides a different representation of the same quantity. Section?. introduces the overall and the frequency-wise measures of reciprocity and association.

obtuse

Inference on Changes in Interdependence Measures,test statistic to determine whether such a change is associated with a structural change and is statistically significant. The properties of the measure and the test statistic are examined through a Monte Carlo simulation, and empirical examples are provided.