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Full maximum likelihood estimation of second- order autoregressive error models

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  • Beach, Charles M.
  • MacKinnon, James G.

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.

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Bibliographic Info

Article provided by Elsevier in its journal Journal of Econometrics.

Volume (Year): 7 (1978)
Issue (Month): 2 (June)
Pages: 187-198

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Handle: RePEc:eee:econom:v:7:y:1978:i:2:p:187-198

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Web page: http://www.elsevier.com/locate/jeconom

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Cited by:
  1. Vougas, Dimitrios V., 2008. "Generalized least squares transformation and estimation with autoregressive error," Statistics & Probability Letters, Elsevier, vol. 78(4), pages 402-404, March.
  2. 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.
  3. Ayako Suzuki, 2012. "Yardstick Competition to Elicit Private Information: An Empirical Analysis," Review of Industrial Organization, Springer, vol. 40(4), pages 313-338, June.
  4. James G. MacKinnon, 1978. "On the Role of Jacobian Terms in Maximum Likelihood Estimation," Working Papers 304, Queen's University, Department of Economics.
  5. 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.

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