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Title: Robust causality test of infinite variance processes Authors:  Fumiya Akashi - University of Tokyo (Japan) [presenting]
Masanobu Taniguchi - Waseda University (Japan)
Anna Clara Monti - University of Sannio (Italy)
Abstract: A robust causality test is developed for time series models with infinite variance innovation processes. First, we introduce a measure of dependence for vector nonparametric linear processes and derive the limit distribution of a previous test statistic in the infinite variance case. Second, we construct a weighted-version of the generalized empirical likelihood (GEL) test statistic, called the self-weighted GEL statistic in time domain. The limit distribution of the self-weighted GEL test statistic is shown to be a standard chi-squared one regardless of whether the model has finite variance or not. Some simulation experiments illustrate desired finite sample performance of the proposed method.