Exact Maximum Likelihood Estimation of Regression Equations with a General Stationary Autoregressive Disturbance

Charles Beach and Stephen Yeo

Working Paper from Economics Department, Queen's University

Abstract: This paper develops an exact maximum likelihood technique for estimating regression equation with general p'th order autoregressive disturbances. Recent expression of the analytic inverse of the covariance matrix of a stationary AR(p) process provide the basis for an iterative, modified Gauss-Newton technique using exact first and approximate second derivatives. Empirical estimates are presented for regression models with and without a lagged dependent variable.

Pages: 39
Date: 1979
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Persistent link: https://EconPapers.repec.org/RePEc:qed:wpaper:343

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