Journal article
A recursive approach to parameter estimation in regression and time series models
Communications in statistics. Theory and methods, Vol.8(12), pp.1227-1245
01/01/1979
DOI: 10.1080/03610927908827825
Abstract
In this paper we discuss the recursive (or on line) estimation in (i) regression and (ii) autoregressive integrated moving average (ARIMA) time series models. The adopted approach uses Kalman filtering techniques to calculate estimates recursively. This approach is used for the estimation of constant as well as time varying parameters. In the first section of the paper we consider the linear regression model. We discuss recursive estimation both for constant and time varying parameters. For constant parameters, Kalman filtering specializes to recursive least squares. In general, we allow the parameters to vary according to an autoregressive integrated moving average process and update the parameter estimates recursively. Since the stochastic model for the parameter changes will "be rarely known, simplifying assumptions have to be made. In particular we assume a random walk model for the time varying parameters and show how to determine whether the parameters are changing over time. This is illustrated with an example.
Details
- Title: Subtitle
- A recursive approach to parameter estimation in regression and time series models
- Creators
- Johannes Ledolter - University of Iowa
- Resource Type
- Journal article
- Publication Details
- Communications in statistics. Theory and methods, Vol.8(12), pp.1227-1245
- Publisher
- Marcel Dekker, Inc
- DOI
- 10.1080/03610927908827825
- ISSN
- 0361-0926
- eISSN
- 1532-415X
- Language
- English
- Date published
- 01/01/1979
- Academic Unit
- Statistics and Actuarial Science; Business Analytics
- Record Identifier
- 9984380589402771
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