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Full Maximum Likelihood Estimation of Second-Order Autoregressive Error Models

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  • Charles M. Beach
  • James G. MacKinnon

Abstract

This 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.

Suggested Citation

  • Charles M. Beach & James G. MacKinnon, 1977. "Full Maximum Likelihood Estimation of Second-Order Autoregressive Error Models," Working Paper 259, Economics Department, Queen's University.
    • Handle: RePEc:qed:wpaper:259
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    Cited by:

    1. Gkritza, Konstantina & Karlaftis, Matthew G. & Mannering, Fred L., 2011. "Estimating multimodal transit ridership with a varying fare structure," Transportation Research Part A: Policy and Practice, Elsevier, vol. 45(2), pages 148-160, February.
    2. Elhorst, J. Paul & Lacombe, Donald J. & Piras, Gianfranco, 2012. "On model specification and parameter space definitions in higher order spatial econometric models," Regional Science and Urban Economics, Elsevier, vol. 42(1-2), pages 211-220.
    3. Ayako Suzuki, 2012. "Yardstick Competition to Elicit Private Information: An Empirical Analysis," Review of Industrial Organization, Springer;The Industrial Organization Society, vol. 40(4), pages 313-338, June.
    4. Vougas, Dimitrios V., 2008. "Generalized least squares transformation and estimation with autoregressive error," Statistics & Probability Letters, Elsevier, vol. 78(4), pages 402-404, March.
    5. James G. MacKinnon, 1978. "On the Role of Jacobian Terms in Maximum Likelihood Estimation," Working Paper 304, Economics Department, Queen's University.

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