Hostname: page-component-78c5997874-8bhkd Total loading time: 0 Render date: 2024-11-17T23:14:31.275Z Has data issue: false hasContentIssue false

Asymptotic Properties of the Maximum-Likelihood and Nonlinear Least-Squares Estimators for Noninvertible Moving Average Models

Published online by Cambridge University Press:  18 October 2010

Katsuto Tanaka
Affiliation:
Hitotsubashi University, Japan
S.E. Satchell
Affiliation:
Trinity College, Cambridge, U.K.

Abstract

Dealing with noninvertible, infinite-order moving average (MA) models, we study the asymptotic properties of an estimator of the noninvertible coefficient. The estimator is constructed acting as if the data were generated from a Gaussian MA process. Allowing for two cases on the initial values of the error process, we first discuss the condition for the existence of a consistent estimator. We then compute the probability of the estimator occurring at the boundary of the invertibility region. Some approximations are also suggested to the limiting distribution of the normalized estimator.

Type
Articles
Copyright
Copyright © Cambridge University Press 1989

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

REFERENCES

1.Anderson, T.W. & Takemura, A.. Why do noninvertible estimated moving averages occur? Journal of Time Series Analysis 7 (1986): 235254.CrossRefGoogle Scholar
2.Christiano, L.J.Cagan's model of hyperinflation under rational expectations. International Economic Review 28 (1987): 3349.CrossRefGoogle Scholar
3.Cryer, J.D. & Ledolter, J.. Small-sample properties of the maximum-likelihood estimator in the first-order moving average model. Biometrika 68 (1981): 691694.Google Scholar
4.Hall, P. & Heyde, C.C.. Martingale limit theory and its application. New York: Academic Press, 1980.Google Scholar
5.Hannan, E.J. & Heyde, C.C.. On limit theorems for quadratic functions of discrete time series. Annals of Mathematical Statistics 43 (1972): 20582066.CrossRefGoogle Scholar
6.Imhof, J.P.Computing the distribution of quadratic forms in normal variables. Biometrika 48 (1961): 419426.CrossRefGoogle Scholar
7.Phillips, P.C.B.Time series regression with a unit root. Econometrica 55 (1987): 277301.CrossRefGoogle Scholar
8.Plosser, C.I. & Schwert, G.W.. Estimation of a noninvertible moving average process: the case of overdifferencing. Journal of Econometrics 6 (1977): 199224.CrossRefGoogle Scholar
9.Sargan, J.D. & Bhargava, A.. Maximum-likelihood estimation of regression models with first-order moving average errors when the root lies on the unit circle. Econometrica 51 (1983): 799820.CrossRefGoogle Scholar
10.Tanaka, K.The Fredholm approach to asymptotic inference on nonstationary and noninvertible time series models. Discussion Paper, Hitotsubashi University, Tokyo, Japan, 1988.Google Scholar