estimate-Merton-method: Estimation for jump diffusion process

In SimoneHermann/BaPreStoPro: Bayesian Prediction of Stochastic Processes

Description

Bayesian estimation of a stochastic process Y_t = y_0 \exp( φ t - γ^2/2 t+γ W_t + \log(1+θ) N_t).

Usage

## S4 method for signature 'Merton'
estimate(model.class, t, data, nMCMC, propSd, adapt = TRUE,
  proposal = c("normal", "lognormal"), it.xi = 10)

Arguments

model.class	class of the jump diffusion model including all required information, see Merton-class
t	vector of time points
data	vector of observation variables
nMCMC	length of Markov chain
propSd	vector of proposal variances for ξ
adapt	if TRUE (default), proposal variance is adapted
proposal	proposal density for xi: "normal" (default) or "lognormal"
it.xi	number of iterations for MH step for ξ inside the Gibbs sampler

References

Hermann, S. and F. Ruggeri (2016). Modelling Wear Degradation in Cylinder Liners. SFB 823 discussion paper 06/16.

Hermann, S., K. Ickstadt and C. H. Mueller (2015). Bayesian Prediction for a Jump Diffusion Process with Application to Crack Growth in Fatigue Experiments. SFB 823 discussion paper 30/15.

Examples

model <- set.to.class("Merton", parameter = list(thetaT = 0.1, phi = 0.05, gamma2 = 0.1, xi = 10))
t <- seq(0, 1, by = 0.01)
data <- simulate(model, t = t, y0 = 0.5, plot.series = TRUE)
est <- estimate(model, t, data, 1000)
plot(est)
## Not run: 
est_hidden <- estimate(model, t, data$Y, 1000)
plot(est_hidden)

## End(Not run)

SimoneHermann/BaPreStoPro documentation built on May 9, 2019, 1:46 p.m.