fitCIR | R Documentation |
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
\mathrm{d} X_t = (α-β X_t)\mathrm{d} t + √{γ X_t}\mathrm{d} W_t
with parameters β>0, 2α>5γ>0 and a Brownian motion (W_t)_{t≥q 0}. This function uses the Gaussian quasi-likelihood, hence requires that data is sampled at high-frequency.
fitCIR(data)
data |
a numeric matrix
containing the realization of (t_0,X_{t_0}), …,(t_n,X_{t_n}) with t_j denoting the j-th sampling times. |
The estimators calculated by this function can be found in the reference below.
A list with three entries each contain a vector in the following order: The result of the preliminary estimator, Newton-Raphson method and the method of scoring.
If the sampling points are not equidistant the function will return 'Please use equidistant sampling points'.
Nicole Hufnagel
Contacts: nicole.hufnagel@math.tu-dortmund.de
Y. Cheng, N. Hufnagel, H. Masuda. Estimation of ergodic square-root diffusion under high-frequency sampling. Econometrics and Statistics, Article Number: 346 (2022).
#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)
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