The Identification and Estimation of Nonlinear Stochastic Systems

Peter Young

Chapter Chapter 6 in Nonlinear Dynamics and Statistics, 2001, pp 127-166 from Springer

Abstract: Abstract This chapter describes what might be called the system theorist’s approach to understanding dynamics of nonlinear stochastic systems. The method uses so-called state-dependent parameters, and is able to handle non-stationarity, as long as the state-dependent parameters vary slowly compared to the significant dynamics. One of the main points made here is that most realistic systems have time-varying inputs which can be measured; models must take this into account, and indeed modeling often becomes easier rather than harder when this is done. We describe the methods used, based on recursive fixed-interval smoothing, and present applications to some realistic problems.

Keywords: Random Walk Model; Nonlinear Stochastic System; White Noise Input; Generalize Random Walk; Squid Data (search for similar items in EconPapers)
Date: 2001
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Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-1-4612-0177-9_6

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DOI: 10.1007/978-1-4612-0177-9_6

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