1,177
Views
23
CrossRef citations to date
0
Altmetric
Original Articles

On count time series prediction

&
Pages 357-373 | Received 28 Feb 2013, Accepted 07 Jul 2013, Published online: 01 Aug 2013

References

  • Czado C, Gneiting T, Held L. Predictive model assessment for count data. Biometrics. 2009;65:1254–1261. doi: 10.1111/j.1541-0420.2009.01191.x
  • Dawid AP. Statistical theory: the prequential approach. J R Stat Soc Ser A. 1984;147:278–292. doi: 10.2307/2981683
  • Fokianos K, Rahbek A, Tjøstheim D. Poisson autoregression. J Am Stat Assoc. 2009;104:1430–1439. doi: 10.1198/jasa.2009.tm08270
  • Jung R, Tremayne A. Useful models for time series of counts or simply wrong ones? AStA Adv Stat Anal. 2011;95: 59–91. doi: 10.1007/s10182-010-0139-9
  • Mikosch T. Non-life insurance mathematics. 2nd ed. An introduction with the Poisson process. Berlin: Springer-Verlag; 2009
  • Christou V, Fokianos K. Quasi-likelihood inference for negative binomial time series models; under review.
  • Lawless JF. Negative binomial and mixed Poisson regression. Can J Stat. 1987;15:209–225. doi: 10.2307/3314912
  • Cameron AC, Trivedi PK. Regression analysis of count data. 1st ed. Cambridge: Cambridge University Press; 1998.
  • Zhu F. A negative binomial integer-valued GARCH model. J Time Series Anal. 2011;32:54–67. doi: 10.1111/j.1467-9892.2010.00684.x
  • Ferland R, Latour A, Oraichi D. Integer-valued GARCH processes. J Time Series Anal. 2006;27:923–942. doi: 10.1111/j.1467-9892.2006.00496.x
  • Neumann M. Absolute regularity and ergodicity of Poisson count processes. Bernoulli. 2011;17:1268–1284. doi: 10.3150/10-BEJ313
  • Doukhan P, Fokianos K, Tjøstheim D. On weak dependence conditions for Poisson autoregressions. Stat Probab Lett. 2012;82:942–948. doi: 10.1016/j.spl.2012.01.015
  • Davis RA, Liu H. Theory and inference for a class of observation-driven models with application to time series of counts; 2012. Available from: ArXiv e-prints, http://adsabs.harvard.edu/abs/2012arXiv1204.3915D
  • Gao J, King M, Lu Z, Tjøstheim D. Specification testing in nonlinear and nonstationary time series regression. Ann Stat. 2009;37:3893–3928. doi: 10.1214/09-AOS698
  • Fokianos K, Neumann MH. A goodness-of-fit test for Poisson count processes. Electron J Stat. 2013;7:793–819. doi: 10.1214/13-EJS790
  • Berkes I, Horváth L, Kokoszka P. GARCH processes: structure and estimation. Bernoulli. 2003;9:201–227. doi: 10.3150/bj/1068128975
  • Francq C, Zakoıan J-M. Maximum likelihood estimation of pure GARCH and ARMA-GARCH processes. Bernoulli. 2004;10:605–637. doi: 10.3150/bj/1093265632
  • Mikosch T, Straumann D. Quasi-maximum-likelihood estimation in conditionally heteroscedastic time series: a stochastic recurrence equations approach. Ann Stat. 2006;34:2449–2495. doi: 10.1214/009053605000000840
  • Bardet J-M, Wintenberger O. Asymptotic normality of the quasi-maximum likelihood estimator for multidimensional causal processes. Ann Stat. 2010;37:2730–2759. doi: 10.1214/08-AOS674
  • Zeger SL, Liang K-Y. Longitudinal data analysis for discrete and continuous outcomes. Biometrics. 1986;42: 121–130. doi: 10.2307/2531248
  • Godambe VP, Heyde CC. Quasi-likelihood and optimal estimation. Int Stat Rev. 1987;55:231–244. doi: 10.2307/1403403
  • Heyde CC. Quasi-likelihood and its applications: a general approach to optimal parameter estimation. New York: Springer; 1997.
  • Gneiting T, Balabdaoui F, Raftery AE. Probabilistic forecasts, calibration and sharpness. J R Stat Soc Ser B (Stat Methodol). 2007;69:243–268. doi: 10.1111/j.1467-9868.2007.00587.x
  • Zucchini W, MacDonald LI. Hidden Markov models for time series. Boca Raton, FL: CRC Press; 2009.

Reprints and Corporate Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

To request a reprint or corporate permissions for this article, please click on the relevant link below:

Academic Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

Obtain permissions instantly via Rightslink by clicking on the button below:

If you are unable to obtain permissions via Rightslink, please complete and submit this Permissions form. For more information, please visit our Permissions help page.