Statistical inference for time-varying ARCH processes
Dahlhaus, Rainer ; Subba Rao, Suhasini
Ann. Statist., Tome 34 (2006) no. 1, p. 1075-1114 / Harvested from Project Euclid
In this paper the class of ARCH(∞) models is generalized to the nonstationary class of ARCH(∞) models with time-varying coefficients. For fixed time points, a stationary approximation is given leading to the notation “locally stationary ARCH(∞) process.” The asymptotic properties of weighted quasi-likelihood estimators of time-varying ARCH(p) processes (p<∞) are studied, including asymptotic normality. In particular, the extra bias due to nonstationarity of the process is investigated. Moreover, a Taylor expansion of the nonstationary ARCH process in terms of stationary processes is given and it is proved that the time-varying ARCH process can be written as a time-varying Volterra series.
Publié le : 2006-06-14
Classification:  Derivative process,  locally stationary,  quasi-likelihood estimates,  time-varying ARCH process,  62M10,  62F10
@article{1152540743,
     author = {Dahlhaus, Rainer and Subba Rao, Suhasini},
     title = {Statistical inference for time-varying ARCH processes},
     journal = {Ann. Statist.},
     volume = {34},
     number = {1},
     year = {2006},
     pages = { 1075-1114},
     language = {en},
     url = {http://dml.mathdoc.fr/item/1152540743}
}
Dahlhaus, Rainer; Subba Rao, Suhasini. Statistical inference for time-varying ARCH processes. Ann. Statist., Tome 34 (2006) no. 1, pp.  1075-1114. http://gdmltest.u-ga.fr/item/1152540743/