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Random environment integer-valued autoregressive process

Author

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  • Aleksandar S. Nastić
  • Petra N. Laketa
  • Miroslav M. Ristić

Abstract

type="main" xml:id="jtsa12161-abs-0001"> An r states random environment integer-valued autoregressive process of order 1, RrINAR(1), is introduced. Also, a random environment process is separately defined as a selection mechanism of differently parameterized geometric distributions, thus ensuring the non-stationary nature of the RrNGINAR(1) model based on the negative binomial thinning. The distributional and correlation properties of this model are discussed, and the k-step-ahead conditional expectation and variance are derived. Yule–Walker estimators of model parameters are presented and their strong consistency is proved. The RrNGINAR(1) model motivation is justified on simulated samples and by its application to specific real-life counting data.

Suggested Citation

  • Aleksandar S. Nastić & Petra N. Laketa & Miroslav M. Ristić, 2016. "Random environment integer-valued autoregressive process," Journal of Time Series Analysis, Wiley Blackwell, vol. 37(2), pages 267-287, March.
    • Handle: RePEc:bla:jtsera:v:37:y:2016:i:2:p:267-287
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    File URL: http://hdl.handle.net/10.1111/jtsa.12161
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    References listed on IDEAS

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    1. Hassan Bakouch & Miroslav Ristić, 2010. "Zero truncated Poisson integer-valued AR(1) model," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 72(2), pages 265-280, September.
    2. Weiß, Christian H., 2008. "The combined INAR(p) models for time series of counts," Statistics & Probability Letters, Elsevier, vol. 78(13), pages 1817-1822, September.
    3. R. K. Freeland & B. P. M. McCabe, 2004. "Analysis of low count time series data by poisson autoregression," Journal of Time Series Analysis, Wiley Blackwell, vol. 25(5), pages 701-722, September.
    4. A. Alzaid & M. Al-Osh, 1993. "Some autoregressive moving average processes with generalized Poisson marginal distributions," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 45(2), pages 223-232, June.
    5. Rong Zhu & Harry Joe, 2006. "Modelling Count Data Time Series with Markov Processes Based on Binomial Thinning," Journal of Time Series Analysis, Wiley Blackwell, vol. 27(5), pages 725-738, September.
    6. Haitao Zheng & Ishwar V. Basawa & Somnath Datta, 2006. "Inference for pth‐order random coefficient integer‐valued autoregressive processes," Journal of Time Series Analysis, Wiley Blackwell, vol. 27(3), pages 411-440, May.
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    Cited by:

    1. Shengqi Tian & Dehui Wang & Shuai Cui, 2020. "A seasonal geometric INAR process based on negative binomial thinning operator," Statistical Papers, Springer, vol. 61(6), pages 2561-2581, December.
    2. Annika Homburg & Christian H. Weiß & Layth C. Alwan & Gabriel Frahm & Rainer Göb, 2021. "A performance analysis of prediction intervals for count time series," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 40(4), pages 603-625, July.
    3. Yuvraj Sunecher & Naushad Mamode Khan & Miroslav M. Ristić & Vandna Jowaheer, 2019. "BINAR(1) negative binomial model for bivariate non-stationary time series with different over-dispersion indices," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 28(4), pages 625-653, December.
    4. Predrag M. Popović & Hassan S. Bakouch, 2020. "A bivariate integer-valued bilinear autoregressive model with random coefficients," Statistical Papers, Springer, vol. 61(5), pages 1819-1840, October.
    5. Sunecher Yuvraj & Mamode Khan Naushad & Jowaheer Vandna, 2019. "Modelling with Dispersed Bivariate Moving Average Processes," Journal of Time Series Econometrics, De Gruyter, vol. 11(1), pages 1-19, January.

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