Full Maximum Likelihood Estimation of Second-Order Autoregressive Error Models
AbstractThis paper develops a technique for estimating linear models with second-order autoregressive errors, which utilizes the full set of observations, and explicitly constrains the estimates of the error process to satisfy a priori stationarity conditions. A nonlinear solution technique which is new to econometrics and works very efficiently is put forward as part of the estimating procedure. Empirical results are presented which emphasize the importance of utilizing the full set of observations and the associated stationarity restrictions.
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Bibliographic InfoPaper provided by Queen's University, Department of Economics in its series Working Papers with number 259.
Date of creation: 1977
Date of revision:
Publication status: Published in Journal of Econometrics, 7, 1978
Other versions of this item:
- Beach, Charles M. & MacKinnon, James G., 1978. "Full maximum likelihood estimation of second- order autoregressive error models," Journal of Econometrics, Elsevier, vol. 7(2), pages 187-198, June.
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- James G. MacKinnon, 1978. "On the Role of Jacobian Terms in Maximum Likelihood Estimation," Working Papers 304, Queen's University, Department of Economics.
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- Vougas, Dimitrios V., 2008. "Generalized least squares transformation and estimation with autoregressive error," Statistics & Probability Letters, Elsevier, vol. 78(4), pages 402-404, March.
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