[Yuima-commits] r799 - pkg/yuima/man

[noreply at r-forge.r-project.org]

Author: hirokimasuda
Date: 2022-06-22 07:30:14 +0200 (Wed, 22 Jun 2022)
New Revision: 799

Added:
   pkg/yuima/man/fitCIR.Rd
Log:
added

Added: pkg/yuima/man/fitCIR.Rd
===================================================================
--- pkg/yuima/man/fitCIR.Rd	                        (rev 0)
+++ pkg/yuima/man/fitCIR.Rd	2022-06-22 05:30:14 UTC (rev 799)
@@ -0,0 +1,59 @@
+% Generated by roxygen2
+\name{fitCIR}
+\alias{fitCIR}
+
+\title{Calculate preliminary estimator and one-step improvements of a Cox-Ingersoll-Ross diffusion}
+
+\description{
+This is a function to simulate the preliminary estimator and the corresponding one step estimators based on the Newton-Raphson and the scoring method of the Cox-Ingersoll-Ross process given via the SDE 
+
+	\eqn{\mathrm{d} X_t = (\alpha-\beta X_t)\mathrm{d} t + \sqrt{\gamma X_t}\mathrm{d} W_t}
+		
+with parameters \eqn{\beta>0,} \eqn{2\alpha>5\gamma>0} and a Brownian motion \eqn{(W_t)_{t\geq 0}}. This function uses the Gaussian quasi-likelihood, hence requires that data is sampled at high-frequency.
+}
+
+\usage{
+fitCIR(data)
+}
+
+\arguments{
+  \item{data}{
+  a numeric matrix 
+containing the realization of \eqn{(t_0,X_{t_0}), \dots,(t_n,X_{t_n})} with \eqn{t_j} denoting the \eqn{j}-th sampling times. \code{data[1,]} contains the sampling times \eqn{t_0,\dots, t_n} and \code{data[2,]} the corresponding value of the process \eqn{X_{t_0},\dots,X_{t_n}.} In other words \code{data[,j]=}\eqn{(t_j,X_{t_j})}. The observations should be equidistant. 
+  }
+}
+
+\value{
+  A list with four entries. The first three entries each contain a vector in the following order: The result of the preliminary estimator, Newton-Raphson method and the method of scoring. The last entry contains the model,	an object of \code{\link{yuima.model-class}}.
+  
+  If the sampling points are not equidistant the function will return \code{'Please use equidistant sampling points'.}
+
+}
+
+\details{
+The estimators calculated by this function can be found in the reference below.
+}
+
+\references{
+Y. Cheng, N. Hufnagel, H. Masuda. Estimation of ergodic square-root diffusion under high-frequency sampling. Econometrics and Statistics, Article Number: 346 (2022).
+}
+
+\author{
+Nicole Hufnagel 
+
+Contacts: \email{nicole.hufnagel at math.tu-dortmund.de}
+}
+
+\examples{
+#You can make use of the function simCIR to generate the data 
+data <- simCIR(alpha=3,beta=1,gamma=1, n=5000, h=0.05, equi.dist=TRUE)
+results <- fitCIR(data)
+}
+
+\keyword{CIR diffusion, high-frequency sampling}
+
+
+
+
+
+