The views expressed here are those of the individual authors and not necessarily those of the JSM sponsors, their officers, or their staff.
Online Program Home
Abstract Details
Activity Number:
|
285
|
Type:
|
Topic Contributed
|
Date/Time:
|
Tuesday, July 31, 2012 : 8:30 AM to 10:20 AM
|
Sponsor:
|
Section on Statistical Learning and Data Mining
|
Abstract - #304159 |
Title:
|
Testing for White Noise Against Locally Stationary Alternatives
|
Author(s):
|
Georg Matthias Goerg*+
|
Companies:
|
Carnegie Mellon University
|
Address:
|
, Pittsburgh, PA, 15213,
|
Keywords:
|
autocorrelations ;
Ljung-Box ;
white noise ;
locally stationary ;
residual check ;
time varying
|
Abstract:
|
Many real-world systems have dynamics that evolve over time, yet stationary models still remain a popular choice in empirical time series studies. In this work I show that one reason for seemingly correct stationary models is a very low power of classic white noise tests against locally varying dynamics. In particular, if autocorrelations change over time but on average equal zero, standard white noise tests cannot detect this deviation from the null hypothesis due to their fundamental design. Here I introduce a moving-window version of the Ljung-Box statistic with an asymptotic chi-square distribution under the null and much larger power facing processes with time-varying autocorrelations. Simulations also show that stationary models often provide a spuriously good fit and thus time-varying dynamics remain undetected in the first place. A case study of tree-ring data demonstrates the importance of the new test for applied time series studies.
|
The address information is for the authors that have a + after their name.
Authors who are presenting talks have a * after their name.
Back to the full JSM 2012 program
|
2012 JSM Online Program Home
For information, contact jsm@amstat.org or phone (888) 231-3473.
If you have questions about the Continuing Education program, please contact the Education Department.