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Activity Number: 338 - Time Series and Forecasting
Type: Contributed
Date/Time: Tuesday, August 1, 2017 : 10:30 AM to 12:20 PM
Sponsor: Business and Economic Statistics Section
Abstract #323865
Title: Testing for Causality Between Two Time Series Using a Parametric Bootstrap
Author(s): Thomas Fisher* and Zequn Sun
Companies: Miami University and Medical University of South Carolina
Keywords: Bootstrapping ; Causality ; Cross-correlation ; GARCH ; Portmanteau ; Time Series

We study the problem of determining if two time series are correlated in the mean and variance processes. Several test statistics, originally designed for determining a connection between two mean processes, are explored and formally introduced for determining a connection in variance. Simulations demonstrate the theoretical asymptotic distribution can be ineffective in finite samples. A parametric bootstrapping method is shown to be an effective tool in such an enterprise. A simulation study demonstrates the efficacy of the bootstrapping method. Lastly, an empirical example explores a connection between the Standard & Poor's 500 index and the Euro/US dollar exchange rate and demonstrates a level of robustness for the proposed method.

Authors who are presenting talks have a * after their name.

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