|
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
|
|
Abstract:
|
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.
