A Kalman particle filter for online parameter estimation with applications to affine models

In this paper we address the problem of estimating the posterior distribution of the static parameters of a continuous-time state space model with discrete-time observations by an algorithm that combines the Kalman filter and a particle filter. The proposed algorithm is semi-recursive and has a two...

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Vydané v:Statistical inference for stochastic processes : an international journal devoted to time series analysis and the statistics of continuous time processes and dynamic systems Ročník 24; číslo 2; s. 353 - 403
Hlavní autori: He, Jian, Khedher, Asma, Spreij, Peter
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Dordrecht Springer Netherlands 01.07.2021
Springer Nature B.V
Predmet:
Algorithms
Computing time
Convergence
Kalman filters
Mathematics
Mathematics and Statistics
Parameter estimation
Probability Theory and Stochastic Processes
State space models
State variable
Statistical Theory and Methods
93E11
Parameter estimation
65C35
Affine process
Kalman filter
State space model
62P05
Particle filter
Posterior distribution
ISSN:1387-0874, 1572-9311
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Popis
Shrnutí:In this paper we address the problem of estimating the posterior distribution of the static parameters of a continuous-time state space model with discrete-time observations by an algorithm that combines the Kalman filter and a particle filter. The proposed algorithm is semi-recursive and has a two layer structure, in which the outer layer provides the estimation of the posterior distribution of the unknown parameters and the inner layer provides the estimation of the posterior distribution of the state variables. This algorithm has a similar structure as the so-called recursive nested particle filter, but unlike the latter filter, in which both layers use a particle filter, our algorithm introduces a dynamic kernel to sample the parameter particles in the outer layer to obtain a higher convergence speed. Moreover, this algorithm also implements the Kalman filter in the inner layer to reduce the computational time. This algorithm can also be used to estimate the parameters that suddenly change value. We prove that, for a state space model with a certain structure, the estimated posterior distribution of the unknown parameters and the state variables converge to the actual distribution in L p with rate of order O ( N - 1 2 + Δ 1 2 ) , where N is the number of particles for the parameters in the outer layer and Δ is the maximum time step between two consecutive observations. We present numerical results of the implementation of this algorithm, in particularly we implement this algorithm for affine interest models, possibly with stochastic volatility, although the algorithm can be applied to a much broader class of models.
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ISSN:1387-0874
1572-9311
DOI:10.1007/s11203-021-09239-3