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Constructing First Order Stationary Autoregressive Models via Latent Processes

Author

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  • MICHAEL K. PITT
  • CHRIS CHATFIELD
  • STEPHEN G. WALKER

Abstract

First order stationary autoregressive (AR(1)) models are introduced for which there exists a linear relation between the expectations of the observations, and where it is readily possible to arrange the marginal distributions to be other than normal.

Suggested Citation

  • Michael K. Pitt & Chris Chatfield & Stephen G. Walker, 2002. "Constructing First Order Stationary Autoregressive Models via Latent Processes," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 29(4), pages 657-663, December.
    • Handle: RePEc:bla:scjsta:v:29:y:2002:i:4:p:657-663
    DOI: 10.1111/1467-9469.00311
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    Citations

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    Cited by:

    1. Matteo Iacopini & Luca Rossini, 2019. "Bayesian nonparametric graphical models for time-varying parameters VAR," Papers 1906.02140, arXiv.org.
    2. Gianni Amisano & Roberto Casarin, 2008. "Particle Filters for Markov-Switching Stochastic-Correlation Models," Working Papers 0814, University of Brescia, Department of Economics.
    3. Theodoros Nicoleris & Spyridon J. Hatjispyros & Stephen G. Walker, 2006. "A Flaming-Viot Process and Bayesian non Parametric," ICER Working Papers - Applied Mathematics Series 17-2006, ICER - International Centre for Economic Research.
    4. Leisen, Fabrizio & Mena, Ramsés H. & Palma, Freddy & Rossini, Luca, 2019. "On a flexible construction of a negative binomial model," Statistics & Probability Letters, Elsevier, vol. 152(C), pages 1-8.
    5. Luis Nieto-Barajas & Eduardo Gutiérrez-Peña, 2022. "General dependence structures for some models based on exponential families with quadratic variance functions," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 31(3), pages 699-716, September.
    6. Ramsés H. Mena & Stephen G. Walker, 2005. "Stationary Autoregressive Models via a Bayesian Nonparametric Approach," Journal of Time Series Analysis, Wiley Blackwell, vol. 26(6), pages 789-805, November.
    7. de Alba, Enrique & Nieto-Barajas, Luis E., 2008. "Claims reserving: A correlated Bayesian model," Insurance: Mathematics and Economics, Elsevier, vol. 43(3), pages 368-376, December.
    8. Kalli, Maria & Griffin, Jim E., 2014. "Time-varying sparsity in dynamic regression models," Journal of Econometrics, Elsevier, vol. 178(2), pages 779-793.
    9. Pitt, Michael K. & Walker, Stephen G., 2006. "Extended constructions of stationary autoregressive processes," Statistics & Probability Letters, Elsevier, vol. 76(12), pages 1219-1224, July.
    10. Tian, Fengping & Yang, Ke & Chen, Langnan, 2017. "Realized volatility forecasting of agricultural commodity futures using the HAR model with time-varying sparsity," International Journal of Forecasting, Elsevier, vol. 33(1), pages 132-152.
    11. Martínez-Ovando Juan Carlos & Walker Stephen G., 2011. "Time-series Modelling, Stationarity and Bayesian Nonparametric Methods," Working Papers 2011-08, Banco de México.
    12. Zhen, X. & Basawa, I.V., 2009. "Observation-driven generalized state space models for categorical time series," Statistics & Probability Letters, Elsevier, vol. 79(24), pages 2462-2468, December.
    13. Ramsés Mena & Stephen Walker, 2007. "On the Stationary Version of the Generalized Hyperbolic ARCH Model," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 59(2), pages 325-348, June.
    14. Yang Lu, 2020. "A simple parameter‐driven binary time series model," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 39(2), pages 187-199, March.

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