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Initializing the Kalman Filter for Nonstationary Time Series Models

By Bell, William R.

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Book Id: WPLBN0000596588
Format Type: PDF eBook
File Size: 1,599,126 KB.
Reproduction Date: 2005
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Title: Initializing the Kalman Filter for Nonstationary Time Series Models  
Author: Bell, William R.
Volume:
Language: English
Subject: Government publications, Census., Census report
Collections: U.S. Census Bureau Collection
Historic
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Publisher: U.S. Census Bureau Department

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Bell, W. R. (n.d.). Initializing the Kalman Filter for Nonstationary Time Series Models. Retrieved from http://community.ebooklibrary.org/


Description
Statistical Reference Document

Excerpt
Introduction: 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. the initial state (see Akaike (1978) and Jones (1980).) For nonstationary time since uncond itional means and ly only calculable given the initial initializing the Kalman filter for nonstationary time series models. These include (1) letting the variance of the initial state be ?large? (Harvey and Phi llips 1979, Burridge and Wallis 1985); (2) usi ng the information filter with the inverse of the initial state varian ...

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