est.VAS.MCMC: MCMC estimation for the Vasicek model

In alejandralopezperez/estsde: Parametric Estimation of SDE

MCMC estimation for the Vasicek model

Description

Parametric estimation for the Vasicek model using Markov Chain Monte Carlo and involving data augmentation, as proposed in Elerian et al. (2001) and Eraker (2001). 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.MCMC(X, Delta = deltat(X), par = NULL, niter = 2000,
  burn_in = 500)

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.
niter	an integer, number of iterations.
burn_in	an integer indicating the number of initial iterations to be discarded.

Value

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

References

Elerian, O., Chib, S., and Shephard, N. (2001). Likelihood inference for discretely observed nonlinear diffusions. Econometrica, 69(4):959–993.

Eraker, B. (2001). MCMC analysis of diffusion models with application to finance. Journal of Business & Economic Statistics, 19(2):177–191.

Examples

set.seed(987)
x <- rVAS(480, 1/12, 0, 0.08, 0.9, 0.1)
est.VAS.MCMC(x)

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