Official websites use .gov
A .gov website belongs to an official government organization in the United States.
Secure .gov websites use HTTPS
A lock (
) or https:// means you’ve safely connected to the .gov website. Share sensitive information only on official, secure websites.
Topics
Data & Maps
Surveys & Programs
Resource Library
Initializing the Kalman Filter for Nonstationary Time Series Models
Initializing the Kalman Filter for Nonstationary Time Series Models
Working Paper Number RR87-33
Abstract
The problem of initializing the Kalman filter for nonstationary time series models is considered. Ansley and Kohn (1985a) and Kohn and Ansley (1986) develop a "modified Kalman filter" for use with nonstationary models to produce estimates from what they call a "transformation approach". We show the same results can be obtained with a suitable initialization of the ordinary Kalman filter. Assuming there are d starting values for the nonstationary series, we initialize the Kalman filter using data through time d with the transformation approach estimate of the state vector and its associated error covariance matrix at time d. We give details of the initialization for ARIMA models, ARIMA component models, and dynamic linear models. We present an example to illustrate how the results may differ from results obtained under more naive initializations that have been suggested.
Others in Series
Working Paper
Final Report on BIPS Graphic Support
Final Report on BIPS Graphic Support
Working Paper
Triangulated Data Structures For Map Merging and Other Applications...
Triangulated Data Structures For Map Merging and Other Applications in Geographic Information Systems
Working Paper
An Iterative GLS Approach to Maximum Likelihood Estimation of Regre...
An Iterative GLS Approach to Maximum Likelihood Estimation of Regression Models with ARIMA Errors
Related Information
Page Last Revised - October 28, 2021
✕
Is this page helpful?
Yes
No
Yes
No✕
NO THANKS
255 characters maximum
255 characters maximum reached
255 characters maximum reached
✕
Thank you for your feedback.
Comments or suggestions?
Comments or suggestions?
