  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, this
proposed 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 $\mathcal{O}(N^{-\frac{1}{2}}+\delta^{\frac{1}{2}})$, where
$N$ is the number of particles for the parameters in the outer layer and
$\delta$ 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.
