Premium
Comparative study of estimation methods for continuous time stochastic processes
Author(s) -
Shoji Isao,
Ozaki Tohru
Publication year - 1997
Publication title -
journal of time series analysis
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.576
H-Index - 54
eISSN - 1467-9892
pISSN - 0143-9782
DOI - 10.1111/1467-9892.00064
Subject(s) - mathematics , monte carlo method , stochastic differential equation , diffusion process , discrete time and continuous time , diffusion , nonlinear system , stochastic process , discrete time stochastic process , constant (computer programming) , statistical physics , continuous time stochastic process , statistics , computer science , innovation diffusion , knowledge management , physics , quantum mechanics , thermodynamics , programming language
In this paper we investigate the finite sample performances of five estimation methods for a continuous‐time stochastic process from discrete observations. Applying these methods to two examples of stochastic differential equations, one with linear drift and state‐dependent diffusion coefficients and the other with nonlinear drift and constant diffusion coefficients, Monte Carlo experiments are carried out to evaluate the finite sample performance of each method. The Monte Carlo results indicate that the differences between the methods are large when the discrete‐ time interval is large. In addition, these differences are noticeable in estimations of the diffusion coefficients.