Dynamic functional data analysis with non-parametric state space models

Márcio Laurini

Journal of Applied Statistics, 2014, vol. 41, issue 1, 142-163

Abstract: In this article, we introduce a new method for modelling curves with dynamic structures, using a non-parametric approach formulated as a state space model. The non-parametric approach is based on the use of penalised splines, represented as a dynamic mixed model. This formulation can capture the dynamic evolution of curves using a limited number of latent factors, allowing an accurate fit with a small number of parameters. We also present a new method to determine the optimal smoothing parameter through an adaptive procedure, using a formulation analogous to a model of stochastic volatility (SV). The non-parametric state space model allows unifying different methods applied to data with a functional structure in finance. We present the advantages and limitations of this method through simulation studies and also by comparing its predictive performance with other parametric and non-parametric methods used in financial applications using data on the term structure of interest rates.

Date: 2014
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Working Paper: Dynamic Functional Data Analysis with Nonparametric State Space Models (2012)
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Persistent link: https://EconPapers.repec.org/RePEc:taf:japsta:v:41:y:2014:i:1:p:142-163

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DOI: 10.1080/02664763.2013.838663

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