Initializing the Kalman Filter for Nonstationary Time Series Models

By Bell, William

Description
Statistical Reference Document

Excerpt
Excerpt: The Kalman filter and variations of it have been widely advocated in recent years for time series filtering, prediction, interpolation, signal extraction, and likelihood evaluation. The algorithms requ re two things: (1) a known state-space model suitable for the problem, and (2) an estimate of the initial state vector and the variance of the error n this estimate. For stationary time series models the usual initialization uses the unconditional mean and variance of series models this approach is not available covariances change over time and are typical slate mean and covariance.

Table of Contents
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Book Id: WPLBN0000579948
Format Type: PDF eBook
File Size: 1,599,126 KB.
Reproduction Date: 2005
Full Text

Title:

Initializing the Kalman Filter for Nonstationary Time Series Models

Author:

Bell, William

Volume:

Language:

English

Subject:

Government publications, Census., Census report

Collection:

U.S. Census Bureau Collection

Subcollection:

Historic
Publication Date:

Publisher:

U.S. Census Bureau Department

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