Abstract #301854

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 #301854
Activity Number: 328
Type: Contributed
Date/Time: Wednesday, August 6, 2003 : 8:30 AM to 10:20 AM
Sponsor: Business & Economics Statistics Section
Abstract - #301854
Title: Linear Prediction of Temporal Aggregates Under Model Misspecification
Author(s): Ka Sing Man*+
Companies: Syracuse University
Address: School of Management, Syracuse, NY, 13244-0001,
Keywords: linear prediction ; temporal aggregation ; time series ; model misspecification
Abstract:

This paper presents theoretical and empirical evidence to demonstrate possible aggregation gain in predicting future aggregates under a practical assumption of model misspecification. Empirical analysis of a number of economic time series suggests that disaggregate series is not always preferred for linear prediction of future aggregates, in terms of an out-of-sample prediction root-mean-square error criterion. One possible justification is model misspecification. That is, if the model fitted to the disaggregate series is misspecified or not the true data generating model, then it may not always produce the best forecasts. This opens up an opportunity for the aggregate model to predict better. It will be interesting to investigate when the aggregate model helps. We compare the efficiency loss of linear prediction of future aggregates of the periodic ARMA process, using the adapted disaggregate model and aggregate model. Some scenarios for possible aggregation gain are identified, and numerical evaluations are given to illustrate the results.

  • 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