Dynamic Functional Data Analysis with Nonparametric State Space Models

Márcio Laurini

No 2012-01, IBMEC RJ Economics Discussion Papers from Economics Research Group, IBMEC Business School - Rio de Janeiro

Abstract: In this article we introduce a new methodology for modeling curves with a dynamic structure, using a non-parametric approach formulated as a state space model. The non-parametric approach is based on the use of penalized splines, represented as a dynamic mixed model. This formulation can capture the dynamic evolution of curves using a limited number of latent factors, allowing a accurate fit with a limited 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. This methodology allows unifying different methodologies applied to data with a functional structure in finance. We present the advantages and limitations of this methodology through a simulation study and also comparing its predictive performance with other parametric and non-parametric methods used in financial applications using data from term structure of interest rates.

Keywords: Functional Data; Penalized Splines; MCMC; Bayesian non-parametric methods (search for similar items in EconPapers)
JEL-codes: C11 C15 G12 (search for similar items in EconPapers)
Date: 2012-03-16
New Economics Papers: this item is included in nep-ecm, nep-ets and nep-ore
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Journal Article: Dynamic functional data analysis with non-parametric state space models (2014)
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