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 OJS  Vol.2 No.5 , December 2012
The First Order Autoregressive Model with Coefficient Contains Non-Negative Random Elements: Simulation and Esimation
Abstract: This paper considered an autoregressive time series where the slope contains random components with non-negative values. The authors determine the stationary condition of the series to estimate its parameters by the quasi-maximum likelihood method. The authors also simulates and estimates the coefficients of the simulation chain. In this paper, we consider modeling and forecasting gold chain on the free market in Hanoi, Vietnam.
Cite this paper: P. Khanh, "The First Order Autoregressive Model with Coefficient Contains Non-Negative Random Elements: Simulation and Esimation," Open Journal of Statistics, Vol. 2 No. 5, 2012, pp. 498-503. doi: 10.4236/ojs.2012.25064.
References

[1]   T. Bollerslev, “Generalized Autoregressive Conditional Heteroscedasticity,” Journal of Econometrics, Vol. 31, No. 3, 1986, pp. 307-327. doi:10.1016/0304-4076(86)90063-1

[2]   D. Nicholls and B. Quinn, “Random Coefficient Autore- gressive Models: An Introduction,” Springer, New York, 1982. doi:10.1007/978-1-4684-6273-9

[3]   A. Aue, L. Horvath and J. Steinbach, “Estimation in Random Coefficient Autoregressive Models,” Journal of Time Series Analysis, Vol. 27, No. 1, 2006, pp. 61-76, doi:10.1111/j.1467-9892.2005.00453.x

 
 
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