Statistical inference for first-order random coefficient integer-valued autoregressive processes

Statistical inference for first-order random coefficient integer-valued autoregressive processes

In this paper, we apply the least-squares method to estimate the unknown parameters in first-order random coefficient integer-valued autoregressive ( RCINAR ( 1 ) ) processes. The least-squares estimator is derived and its limiting properties are discussed. Furthermore, we also derive a statistic to...

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Veröffentlicht in:Journal of inequalities and applications 2015-11, Vol.2015 (1), p.1-12, Article 359
Hauptverfasser: Zhao, Zhiwen, Hu, Yadi
Format: Artikel
Sprache:eng
Schlagworte:
Analysis
Applications of Mathematics
Autoregressive processes
Coefficients
Constraining
Inequalities
Least squares method
Mathematical models
Mathematics
Mathematics and Statistics
Statistical tests
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Zusammenfassung:In this paper, we apply the least-squares method to estimate the unknown parameters in first-order random coefficient integer-valued autoregressive ( RCINAR ( 1 ) ) processes. The least-squares estimator is derived and its limiting properties are discussed. Furthermore, we also derive a statistic to test the randomness of coefficients. Numerical results from simulation studies suggest that the proposed method is good for practical use.
ISSN:1029-242X
1025-5834
1029-242X
DOI:10.1186/s13660-015-0886-y