Optimal Instrumental Variables Estimation for Arma Models

71 Pages Posted: 26 Jul 2000

See all articles by Guido M. Kuersteiner

Guido M. Kuersteiner

Boston University - Department of Economics

Date Written: March 1999


In this paper a new class of Instrumental Variables estimator for linear processes and in particular ARMA models is developed. Previously, IV estimators based on lagged observations as instruments have been used to account for unmodelled MA(q) errors in the estimation of the AR parameters. Here it is shown that those IV methods can be used to improve efficiency of linear time series estimators in the presence of unmodelled conditional heteroskedasticity. Moreover an IV estimator for both the AR and MA parts is developed. One consequence of these results is that Gaussian estimators for linear time series models are inefficient members of this IV class. A leading example of an inefficient member is the OLS estimator for AR(p) models which is known to be efficient under homoskedasticity.

Keywords: ARMA, conditional heteroskedasticity, insgtrumental variables, efficiency lower-bound, frequency domain

JEL Classification: C13, C22

Suggested Citation

Kuersteiner, Guido, Optimal Instrumental Variables Estimation for Arma Models (March 1999). Available at SSRN: https://ssrn.com/abstract=235809 or http://dx.doi.org/10.2139/ssrn.235809

Guido Kuersteiner (Contact Author)

Boston University - Department of Economics ( email )

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Boston, MA 02215
United States