JBtest | R Documentation |
Remove jumps and calculate the Gaussian quasi-likelihood estimator based on the Jarque-Bera normality test
JBtest(yuima,start,lower,upper,alpha,skewness=TRUE,kurtosis=TRUE,withdrift=FALSE)
yuima |
a yuima object (diffusion with compound Poisson jumps). |
lower |
a named list for specifying lower bounds of parameters. |
upper |
a named list for specifying upper bounds of parameters. |
alpha |
Insert Description Here. |
start |
initial values to be passed to the optimizer. |
skewness |
use third moment information ? by default, skewness=TRUE |
kurtosis |
use fourth moment information ? by default, kurtosis=TRUE |
withdrift |
use drift information for constructing self-normalized residuals or not? by default, withdrift = FALSE |
This function removes large increments which are regarded as jumps based on the iterative Jarque-Bera normality test, and after that, calculates the Gaussian quasi maximum likelihood estimator.
Removed |
Removed jumps and jump times |
OGQMLE |
Gaussian quasi maximum likelihood estimator before jump removal |
JRGQMLE |
Gaussian quasi maximum likelihood estimator after jump removal |
Figures |
For visualization, the jump points are presented. In addition, the histgram of the jump removed self-normalized residuals, transition of the estimators and the logarithm of Jarque-Bera statistics are given as figures |
The YUIMA Project Team
Contacts: Yuma Uehara y-uehara@ism.ac.jp
Masuda, H. (2013). Asymptotics for functionals of self-normalized residuals of discretely observed stochastic processes. Stochastic Processes and their Applications 123 (2013), 2752–2778
Masuda, H and Uehara, Y. (2018) Estimating Diffusion With Compound Poisson Jumps Based On Self-normalized Residuals, arXiv:1802.03945
## Not run: set.seed(123) mod <- setModel(drift="10-3*x", diffusion="theta*(2+x^2)/(1+x^2)", jump.coeff="1", measure=list(intensity="1",df=list("dunif(z, 3, 5)")), measure.type="CP") T <- 10 ## Terminal n <- 5000 ## generation size samp <- setSampling(Terminal=T, n=n) ## define sampling scheme yuima <- setYuima(model = mod, sampling = samp) yuima <- simulate(yuima, xinit=1,true.parameter=list(theta=sqrt(2)), sampling = samp) JBtest(yuima,start=list(theta=0.5),upper=c(theta=100) ,lower=c(theta=0),alpha=0.01) ## End(Not run)
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