The First Order Autoregressive Model with Coefficient Contains Non-Negative Random Elements: Simulation and Esimation

DOI: 10.4236/ojs.2012.25064   PDF   HTML     4,235 Downloads   6,087 Views   Citations

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.

Share and Cite:

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.

Conflicts of Interest

The authors declare no conflicts of interest.

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

  
comments powered by Disqus

Copyright © 2020 by authors and Scientific Research Publishing Inc.

Creative Commons License

This work and the related PDF file are licensed under a Creative Commons Attribution 4.0 International License.