The Annals of Statistics

Parameter Estimation for ARMA Models with Infinite Variance Innovations

Abstract

We consider a standard ARMA process of the form $\phi(B)X_t = \theta(B)Z_t$, where the innovations $Z_t$ belong to the domain of attraction of a stable law, so that neither the $Z_t$ nor the $X_t$ have a finite variance. Our aim is to estimate the coefficients of $\phi$ and $\theta$. Since maximum likelihood estimation is not a viable possibility (due to the unknown form of the marginal density of the innovation sequence), we adopt the so-called Whittle estimator, based on the sample periodogram of the $X$ sequence. Despite the fact that the periodogram does not, a priori, seem like a logical object to study in this non-$\mathscr{L}^2$ situation, we show that our estimators are consistent, obtain their asymptotic distributions and show that they converge to the true values faster than in the usual $\mathscr{L}^2$ case.

Article information

Source
Ann. Statist., Volume 23, Number 1 (1995), 305-326.

Dates
First available in Project Euclid: 11 April 2007

https://projecteuclid.org/euclid.aos/1176324469

Digital Object Identifier
doi:10.1214/aos/1176324469

Mathematical Reviews number (MathSciNet)
MR1331670

Zentralblatt MATH identifier
0822.62076

JSTOR