est.VAS.LL: ML estimation for the Vasicek model (local linearization)

In alejandralopezperez/estsde: Parametric Estimation of SDE

ML estimation for the Vasicek model (local linearization)

Description

Parametric estimation for the Vasicek model using maximum likelihood and the discretized version of the model, obtained with the local linearization 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.LL(X, Delta = deltat(X), par = NULL)

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.

Value

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

References

Ozaki, T. (1992). A bridge between nonlinear time series models and nonlinear stochastic dynamical systems: a local linearization approach. Statistica Sinica, pages 113–135.

Shoji, I. and Ozaki, T. (1998). Estimation for nonlinear stochastic differential equations by a local linearization method. Stochastic Analysis and Applications, 16(4):733–752.

Examples

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

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