Robust causality test of infinite variance processes

Fumiya Akashi, Masanobu Taniguchi and Anna Clara Monti

Journal of Econometrics, 2020, vol. 216, issue 1, 235-245

Abstract: This paper develops a robust causality test for time series with infinite variance innovation processes. First, we introduce a measure of dependence for vector nonparametric linear processes, and derive the asymptotic distribution of the test statistic by Taniguchi et al. (1996) 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 the time domain. The limiting distribution of the self-weighted GEL test statistic is shown to be the usual chi-squared one regardless of whether the model has finite variance or not. Some simulation experiments illustrate satisfactory finite sample performances of the proposed test.

Keywords: Granger causality; Nonparametric hypothesis testing; Generalized empirical likelihood; Self-weighting (search for similar items in EconPapers)
JEL-codes: C12 C32 (search for similar items in EconPapers)
Date: 2020
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Persistent link: https://EconPapers.repec.org/RePEc:eee:econom:v:216:y:2020:i:1:p:235-245

DOI: 10.1016/j.jeconom.2020.01.016

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