Maximum Likelihood Estimation of ARMA Model with Error Processes for Replicated Observations
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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- 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.
- Wing-Keung Wong & Guorui Bian, 2004. "Estimating Parameters in Autoregressive Models with Asymmetric Innovations," Departmental Working Papers wp0408, National University of Singapore, Department of Economics.
- Wong, Wing-Keung & Li, Chi-Kwong, 1999. "A note on convex stochastic dominance," Economics Letters, Elsevier, vol. 62(3), pages 293-300, March.
- 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. Full references (including those not matched with items on IDEAS)