The Annals of Statistics

Estimation of the Parameters of Linear Time Series Models Subject to Nonlinear Restrictions

Neerchal K. Nagaraj and Wayne A. Fuller

Full-text: Open access

Abstract

Least squares estimators of the parameters of a linear time series model, where the parameters are constrained by a set of nonlinear restrictions, are studied. The model may contain lags of the dependent variable as regressors and the sums of squares of the explanatory variables may grow at different rates as the sample size increases. The estimation procedures can be applied to a regression model with an error process that satisfies either a stationary or a nonstationary autoregression.

Article information

Source
Ann. Statist., Volume 19, Number 3 (1991), 1143-1154.

Dates
First available in Project Euclid: 12 April 2007

Permanent link to this document
https://projecteuclid.org/euclid.aos/1176348242

Digital Object Identifier
doi:10.1214/aos/1176348242

Mathematical Reviews number (MathSciNet)
MR1126318

Zentralblatt MATH identifier
0729.62087

JSTOR
links.jstor.org

Subjects
Primary: 62M10: Time series, auto-correlation, regression, etc. [See also 91B84]
Secondary: 62J02: General nonlinear regression 62F12: Asymptotic properties of estimators

Keywords
Least squares nonlinear estimation nonstationary time series

Citation

Nagaraj, Neerchal K.; Fuller, Wayne A. Estimation of the Parameters of Linear Time Series Models Subject to Nonlinear Restrictions. Ann. Statist. 19 (1991), no. 3, 1143--1154. doi:10.1214/aos/1176348242. https://projecteuclid.org/euclid.aos/1176348242


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