Description Usage Arguments References Examples
Bayesian prediction of a stochastic process Y_t = y_0 \exp( φ t - γ2/2 t+γ W_t + \log(1+θ) N_t).
1 2 3 4 5 6 | ## S4 method for signature 'est.Merton'
predict(object, t, burnIn, thinning, Lambda.mat,
which.series = c("new", "current"), M2pred = 10, only.interval = TRUE,
level = 0.05, cand.length = 1000, pred.alg = c("Distribution",
"Trajectory", "simpleTrajectory", "simpleBayesTrajectory"), sample.length,
plot.prediction = TRUE)
|
object |
class object of MCMC samples: "est.Merton", created with method |
t |
vector of time points to make predictions for |
burnIn |
burn-in period |
thinning |
thinning rate |
Lambda.mat |
matrix-wise definition of intensity rate function (makes it faster) |
which.series |
which series to be predicted, new one ("new") or further development of current one ("current") |
M2pred |
optional, if current series to be predicted and t missing, |
only.interval |
if TRUE: only calculation of prediction intervals (only for pred.alg = "Distribution") |
level |
level of the prediction intervals |
cand.length |
length of candidate samples (if method = "vector"), for jump diffusion |
pred.alg |
prediction algorithm, "Distribution", "Trajectory", "simpleTrajectory" or "simpleBayesTrajectory" |
sample.length |
number of samples to be drawn, default is the number of posterior samples |
plot.prediction |
if TRUE, prediction intervals are plotted |
Hermann, S. (2016a). BaPreStoPro: an R Package for Bayesian Prediction of Stochastic Processes. SFB 823 discussion paper 28/16.
Hermann, S. (2016b). Bayesian Prediction for Stochastic Processes based on the Euler Approximation Scheme. SFB 823 discussion paper 27/16.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 | cl <- set.to.class("Merton",
parameter = list(thetaT = 0.1, phi = 0.05, gamma2 = 0.1, xi = c(3, 1/4)),
Lambda = function(t, xi) (t/xi[2])^xi[1])
t <- seq(0, 1, by = 0.01)
data <- simulate(cl, t = t, y0 = 0.5)
est <- estimate(cl, t, data, 1000)
plot(est)
## Not run:
pred1 <- predict(est, Lambda.mat = function(t, xi) (t/xi[,2])^xi[,1])
pred2 <- predict(est, Lambda.mat = function(t, xi) (t/xi[,2])^xi[,1], pred.alg = "Trajectory")
pred3 <- predict(est, pred.alg = "simpleTrajectory")
pred4 <- predict(est, pred.alg = "simpleBayesTrajectory")
## End(Not run)
|
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