Approximate conditional least squares estimation of a nonlinear state-space model via an unscented Kalman filter

Kwang Woo Ahn and Kung-Sik Chan

Computational Statistics & Data Analysis, 2014, vol. 69, issue C, 243-254

Abstract: The problem of estimating a nonlinear state-space model whose state process is driven by an ordinary differential equation (ODE) or a stochastic differential equation (SDE), with discrete-time data is studied. A new estimation method is proposed based on minimizing the conditional least squares (CLS) with the conditional mean function computed approximately via the unscented Kalman filter (UKF). Conditions are derived for the UKF–CLS estimator to preserve the limiting properties of the exact CLS estimator, namely, consistency and asymptotic normality, under the framework of infill asymptotics, i.e. sampling is increasingly dense over a fixed domain. The efficacy of the proposed method is demonstrated by simulation and a real application.

Keywords: Nonlinear time series; SIR model; State-space model; Unscented Kalman filter (search for similar items in EconPapers)
Date: 2014
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Citations: View citations in EconPapers (2)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:csdana:v:69:y:2014:i:c:p:243-254

DOI: 10.1016/j.csda.2013.07.038

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