Abstract #300037

This is the preliminary program for the 2003 Joint Statistical Meetings in San Francisco, California. Currently included in this program is the "technical" program, schedule of invited, topic contributed, regular contributed and poster sessions; Continuing Education courses (August 2-5, 2003); and Committee and Business Meetings. This on-line program will be updated frequently to reflect the most current revisions.

To View the Program:
You may choose to view all activities of the program or just parts of it at any one time. All activities are arranged by date and time.

The views expressed here are those of the individual authors
and not necessarily those of the ASA or its board, officers, or staff.

Back to main JSM 2003 Program page
JSM 2003 Abstract #300037
Activity Number: 189
Type: Invited
Date/Time: Tuesday, August 5, 2003 : 8:30 AM to 10:20 AM
Sponsor: IMS
Abstract - #300037
Title: Polynomial Trend Regression with Long-Memory Errors
Author(s): Hwai-Chung Ho*+
Companies: Academia Sinica
Address: Nakang, Taipei 115, , , Taiwan R.O.C.
Keywords: Asymptotic normality ; Long memory ; Ordinary least squares ; Polynomial trend
Abstract:

For a time series generated by polynomial trend with stationary long-memory errors, the ordinary least squares estimator (OLSE) of the trend coefficients is asymptotically normal provided the error process is linear. The asymptotic distribution may no longer be normal, if the error is in the form of a long-memory linear process passing through a certain nonlinear transformation. But one hardly has sufficient information about the transformation to determine which type of limiting distribution the OLSE converges to and to apply the correct distribution to constructing valid confidence intervals for the coefficients based on the OLSE. The paper proposes a modified least squares estimator to bypass this drawback. It is shown that the asymptotic normality can be assured for the modified estimator with mild trade-off of efficiency even the error is nonlinear and the original limit for the OLSE is non-normal. The estimator performs fairly well when applied to various simulated series and two temperature datasets concerning global warming.

  • 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 2003 program

JSM 2003 For information, contact meetings@amstat.org or phone (703) 684-1221. If you have questions about the Continuing Education program, please contact the Education Department.
Revised March 2003