Conditional Least Squares Estimators of the Parameters of the NLAR(p) Time Series Model

Abstract

Conditional least square estimators for the parameters of he NLAR(p) time series models are obtained. it is also shown that these estimators are consistent and asymptotically normal.

Keywords

• New Laplace Autoregressive process;
• Random coefficient autoregressive process;
• Conditional least squares estimator;
• Consistency;
• Asymptotic normal distribution

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References

1. IEEE Trans. on Information Theory v.IT-31 no.5 A new Laplace second-order autoregressive time series model -NLAR(2) Dewald, L.S.;Lewis, P.A.W.
2. Journal of Time Series Analysis v.9 no.4 On the existence of the stationary and ergodic NEAR(p) model Chan, Kungsik
3. Random coefficient Autoregressive Models : An Introduction Nicholls, D.F.;Quinn, B.F.
4. Journal of the Korean Statistical Scoity v.26 no.4 Existence Condition for the Stationary Ergodic New Laplace Autoregressive Model of Order p. Kim, W.K.;Billard, L.
5. Adv. Appl. Prob. v.13 A new autoregressive time series model in exponential variables (NEAR(1) Lawrance, A.J.;Lewis, P.A.W.
6. J.R. Statist. Soc. Ser. B. v.47 no.2 Modelling and residual analysis of non-linear autoregressive time series in exponential variables (with discussions) Lawrance, A.J.;Lewis, P.A.W.