Title
Least squares estimators for discretely observed stochastic processes driven by small Lévy noises
Abstract
We study the problem of parameter estimation for discretely observed stochastic processes driven by additive small Levy noises. We do not impose any moment condition on the driving Levy process. Under certain regularity conditions on the drift function, we obtain consistency and rate of convergence of the least squares estimator (LSE) of the drift parameter when a small dispersion coefficient @e-0 and n-~ simultaneously. The asymptotic distribution of the LSE in our general setting is shown to be the convolution of a normal distribution and a distribution related to the jump part of the Levy process. Moreover, we briefly remark that our methodology can be easily extended to the more general case of semi-martingale noises.
Year
DOI
Venue
2013
10.1016/j.jmva.2013.01.012
J. Multivariate Analysis
Keywords
Field
DocType
drift parameter,drift function,parameter estimation,additive small levy noise,small dispersion coefficient,normal distribution,general setting,squares estimator,discretely observed stochastic,asymptotic distribution,general case,levy process,primary,least squares method,stochastic processes
Least squares,Econometrics,Normal distribution,Stochastic process,Rate of convergence,Estimation theory,Lévy process,Statistics,Mathematics,Asymptotic distribution,Estimator
Journal
Volume
ISSN
Citations 
116,
0047-259X
1
PageRank 
References 
Authors
0.63
0
3
Name
Order
Citations
PageRank
Hongwei Long1133.11
Yasutaka Shimizu28016.88
Wei Sun331.43