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Maximum Likelihood Estimation of ARMA Model with Error Processes for Replicated Observations

  • Wing-Keung Wong

    ()

    (National University of Singapore)

  • Robert B. Miller

    (University of Wisconsin-Madison)

  • Keshab Shrestha

    (Concordia University)

In this paper we analyse the repeated time series model where the fundamental component follows a ARMA process. In the model, the error variance as well as the number of repetition are allowed to change over time. It is shown that the model is identified. The maximum likelihood estimator is derived using the Kalman filter technique. The model considered in this paper can be considered as extension of the models considered by Anderson (1978), Azzalini (1981) and Wong and Miller (1990)

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File URL: http://www.fas.nus.edu.sg/ecs/pub/wp/wp0217.pdf
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Paper provided by National University of Singapore, Department of Economics in its series Departmental Working Papers with number wp0217.

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Length: 19 pages
Date of creation: 2002
Date of revision:
Handle: RePEc:nus:nusewp:wp0217
Contact details of provider: Web page: http://www.fas.nus.edu.sg/ecs/index.html
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  1. Wong, Wing-Keung & Bian, Guorui, 2005. "Estimating parameters in autoregressive models with asymmetric innovations," Statistics & Probability Letters, Elsevier, vol. 71(1), pages 61-70, January.
  2. Wong, Wing-Keung & Li, Chi-Kwong, 1999. "A note on convex stochastic dominance," Economics Letters, Elsevier, vol. 62(3), pages 293-300, March.
  3. Meher Manzur & Wing-Keung Wong & Inn-Chau Chee, 1999. "Measuring international competitiveness: experience from East Asia," Applied Economics, Taylor & Francis Journals, vol. 31(11), pages 1383-1391.
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