est.VAS.KF: ML estimation for the Vasicek model (Kalman filter)

In alejandralopezperez/estsde: Parametric Estimation of SDE

ML estimation for the Vasicek model (Kalman filter)

Description

Parametric estimation for the Vasicek model using the Kalman filter algorithm, where the discretized version of the model is obtained with the Euler-Maruyama method. The parametric form of the Vasicek model used here is given by

dX_t = (α - κ X_t)dt + σ dW_t.

Usage

est.VAS.KF(X, Delta = deltat(X), par = NULL, mu0 = 0, Sigma0 = 1)

Arguments

X	a numeric vector, the sample path of the SDE.
Delta	a single numeric, the time step between two consecutive observations.
par	a numeric vector with dimension three indicating initial values of the parameters. Defaults to NULL, fits a linear model as an initial guess.
mu0	a single numeric, the initial mean. Defaults to zero.
Sigma0	a single numeric, the initial variance. Defaults to one.

Value

A list containing a matrix with the estimated coefficients and the associated standard errors.

Examples

x <- rVAS(360, 1/12, 0, 0.08, 0.9, 0.1)
est.VAS.KF(x)

alejandralopezperez/estsde documentation built on Sept. 4, 2022, 4:48 a.m.