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Forecasting discrete valued low count time series

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  • Freeland, R. K.
  • McCabe, B. P. M.

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Bibliographic Info

Article provided by Elsevier in its journal International Journal of Forecasting.

Volume (Year): 20 (2004)
Issue (Month): 3 ()
Pages: 427-434

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Handle: RePEc:eee:intfor:v:20:y:2004:i:3:p:427-434

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Web page: http://www.elsevier.com/locate/ijforecast

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Citations

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Cited by:
  1. Jan G. de Gooijer & Rob J. Hyndman, 2005. "25 Years of IIF Time Series Forecasting: A Selective Review," Tinbergen Institute Discussion Papers 05-068/4, Tinbergen Institute.
  2. T M Christensen & A. S. Hurn & K A Lindsay, 2008. "Discrete time-series models when counts are unobservable," NCER Working Paper Series 35, National Centre for Econometric Research.
  3. Jason Ng & Catherine S. Forbes & Gael M. Martin & Brendan P.M. McCabe, 2011. "Non-Parametric Estimation of Forecast Distributions in Non-Gaussian, Non-linear State Space Models," Monash Econometrics and Business Statistics Working Papers 11/11, Monash University, Department of Econometrics and Business Statistics.
  4. B.P.M. McCabe & G.M. Martin, 2003. "Coherent Predictions of Low Count Time Series," Monash Econometrics and Business Statistics Working Papers 8/03, Monash University, Department of Econometrics and Business Statistics.
  5. Christoph Jeßberger, 2011. "Multilateral Environmental Agreements up to 2050: Are They Sustainable Enough?," Ifo Working Paper Series Ifo Working Paper No. 98, Ifo Institute for Economic Research at the University of Munich.
  6. Andersson, Jonas & Karlis, Dimitris, 2008. "Treating missing values in INAR(1) models," Discussion Papers 2008/14, Department of Business and Management Science, Norwegian School of Economics.
  7. Feigin, Paul D. & Gould, Phillip & Martin, Gael M. & Snyder, Ralph D., 2008. "Feasible parameter regions for alternative discrete state space models," Statistics & Probability Letters, Elsevier, vol. 78(17), pages 2963-2970, December.
  8. Axel Groß-Klußmann & Nikolaus Hautsch, 2011. "Predicting Bid-Ask Spreads Using Long Memory Autoregressive Conditional Poisson Models," SFB 649 Discussion Papers SFB649DP2011-044, Sonderforschungsbereich 649, Humboldt University, Berlin, Germany.
  9. Ralph D. Snyder & Gael M. Martin & Phillip Gould & Paul D. Feigin, 2007. "An Assessment of Alternative State Space Models for Count Time Series," Monash Econometrics and Business Statistics Working Papers 4/07, Monash University, Department of Econometrics and Business Statistics.
  10. Timothy Christensen & Stan Hurn & Kenneth Lindsay, 2009. "It Never Rains but it Pours: Modeling the Persistence of Spikes in Electricity Prices," The Energy Journal, International Association for Energy Economics, vol. 0(Number 1), pages 25-48.
  11. De Gooijer, Jan G. & Hyndman, Rob J., 2006. "25 years of time series forecasting," International Journal of Forecasting, Elsevier, vol. 22(3), pages 443-473.
  12. R. Freeland, 2010. "True integer value time series," AStA Advances in Statistical Analysis, Springer, vol. 94(3), pages 217-229, September.
  13. Jung, Robert C. & Tremayne, A.R., 2006. "Coherent forecasting in integer time series models," International Journal of Forecasting, Elsevier, vol. 22(2), pages 223-238.
  14. McCabe, B.P.M. & Martin, G.M., 2005. "Bayesian predictions of low count time series," International Journal of Forecasting, Elsevier, vol. 21(2), pages 315-330.
  15. Scotto, Manuel G. & Weiß, Christian H. & Silva, Maria Eduarda & Pereira, Isabel, 2014. "Bivariate binomial autoregressive models," Journal of Multivariate Analysis, Elsevier, vol. 125(C), pages 233-251.
  16. Hee-Young Kim & Yousung Park, 2008. "A non-stationary integer-valued autoregressive model," Statistical Papers, Springer, vol. 49(3), pages 485-502, July.

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