The Annals of Statistics

Estimation of the Parameters of Stochastic Difference Equations

Wayne A. Fuller, David P. Hasza, and J. Jeffery Goebel

Full-text: Open access

Abstract

Let $Y_t$ satisfy the stochastic difference equation $Y_t = \sum^q_{i=1} \psi_{ti} \alpha_i + \sum^p_{j=1} \gamma_j Y_{t-j} + e_t,$ where the $\{\psi_{ti}\}$ are fixed sequences and (or) weakly stationary time series and the $e_t$ are independent random variables, each with mean zero and variance $\sigma^2$. The form of the limiting distributions of the least squares estimators of $\alpha_i$ and $\gamma_j$ depend upon the absolute value of the largest root of the characteristic equation, $m^p - \sum^p_{j=1} \gamma_jm^{p-j} = 0$. Limiting distributions of the least squares estimators are established for the situations where the largest root is less than one, equal to one, and greater than one in absolute value. In all three situations the regression $t$-type statistic is of order one in probability under mild assumptions. Conditions are given under which the limiting distribution of the $t$-type statistic is standard normal.

Article information

Source
Ann. Statist., Volume 9, Number 3 (1981), 531-543.

Dates
First available in Project Euclid: 12 April 2007

Permanent link to this document
https://projecteuclid.org/euclid.aos/1176345457

Digital Object Identifier
doi:10.1214/aos/1176345457

Mathematical Reviews number (MathSciNet)
MR615429

Zentralblatt MATH identifier
0499.62082

JSTOR
links.jstor.org

Subjects
Primary: 62M10: Time series, auto-correlation, regression, etc. [See also 91B84]
Secondary: 62J05: Linear regression

Keywords
Stochastic difference equation time series regression for time series autoregressive process

Citation

Fuller, Wayne A.; Hasza, David P.; Goebel, J. Jeffery. Estimation of the Parameters of Stochastic Difference Equations. Ann. Statist. 9 (1981), no. 3, 531--543. doi:10.1214/aos/1176345457. https://projecteuclid.org/euclid.aos/1176345457


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