Some recent theory for autoregressive count time series
AbstractIn this paper an overview is given over recent theoretical developments in autoregressive count time series. The focus is on generalized autoregressive models where the autoregressive structure is incorporated via a link function. Starting from an ordinary autoregressive model the difficulties in extending standard theory of statistical inference to count time series are highlighted. Special attention is given to the issues of ergodicity and asymptotic theory of estimation. Two main approaches are mentioned, a perturbation approach and the use of a weak dependence concept. The main emphasis is on the former. Linear as well as log-linear and nonlinear models are treated. It is argued that the developed theory forms a necessary basis for modelling and application of these count time series. The setting of the paper is one of simple models and conditional distributions of Poisson type. But it is claimed that the framework is general enough to handle many extensions with an accompanying flexibility in applications of these models. Copyright Sociedad de Estadística e Investigación Operativa 2012
Download InfoIf you experience problems downloading a file, check if you have the proper application to view it first. In case of further problems read the IDEAS help page. Note that these files are not on the IDEAS site. Please be patient as the files may be large.
As the access to this document is restricted, you may want to look for a different version under "Related research" (further below) or search for a different version of it.
Bibliographic InfoArticle provided by Springer in its journal TEST.
Volume (Year): 21 (2012)
Issue (Month): 3 (September)
Contact details of provider:
Web page: http://www.springerlink.com/link.asp?id=120411
Find related papers by JEL classification:
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
- Jung, Robert C. & Liesenfeld, Roman & Richard, Jean-FranÃ§ois, 2011.
"Dynamic Factor Models for Multivariate Count Data: An Application to Stock-Market Trading Activity,"
Journal of Business & Economic Statistics,
American Statistical Association, vol. 29(1), pages 73-85.
- Jung, Robert & Liesenfeld, Roman & Richard, Jean-François, 2008. "Dynamic Factor Models for Multivariate Count Data: An Application to Stock-Market Trading Activity," Economics Working Papers 2008,12, Christian-Albrechts-University of Kiel, Department of Economics.
- Heinen, Andreas, 2003.
"Modelling Time Series Count Data: An Autoregressive Conditional Poisson Model,"
8113, University Library of Munich, Germany.
- HEINEN, Andréas, 2003. "Modelling time series count data: an autoregressive conditional Poisson model," CORE Discussion Papers 2003062, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
- René Ferland & Alain Latour & Driss Oraichi, 2006. "Integer-Valued GARCH Process," Journal of Time Series Analysis, Wiley Blackwell, vol. 27(6), pages 923-942, November.
- Konstantinos Fokianos & Roland Fried, 2010. "Interventions in INGARCH processes," Journal of Time Series Analysis, Wiley Blackwell, vol. 31(3), pages 210-225, 05.
- Sandmann, Gleb & Koopman, Siem Jan, 1998. "Estimation of stochastic volatility models via Monte Carlo maximum likelihood," Journal of Econometrics, Elsevier, vol. 87(2), pages 271-301, September.
- Jensen, S ren Tolver & Rahbek, Anders, 2004. "Asymptotic Inference For Nonstationary Garch," Econometric Theory, Cambridge University Press, vol. 20(06), pages 1203-1226, December.
- Konstantinos Fokianos & Benjamin Kedem, 2004. "Partial Likelihood Inference For Time Series Following Generalized Linear Models," Journal of Time Series Analysis, Wiley Blackwell, vol. 25(2), pages 173-197, 03.
- Meitz, Mika & Saikkonen, Pentti, 2004.
"Ergodicity, mixing, and existence of moments of a class of Markov models with applications to GARCH and ACD models,"
Working Paper Series in Economics and Finance
573, Stockholm School of Economics, revised 20 Apr 2007.
- Meitz, Mika & Saikkonen, Pentti, 2008. "Ergodicity, Mixing, And Existence Of Moments Of A Class Of Markov Models With Applications To Garch And Acd Models," Econometric Theory, Cambridge University Press, vol. 24(05), pages 1291-1320, October.
- Mika Meitz & Pentti Saikkonen, 2007. "Ergodicity, mixing, and existence of moments of a class of Markov models with applications to GARCH and ACD models," Economics Series Working Papers 327, University of Oxford, Department of Economics.
- Drost, Feike C. & van den Akker, Ramon & Werker, Bas J.M., 2008.
"Note on integer-valued bilinear time series models,"
Statistics & Probability Letters,
Elsevier, vol. 78(8), pages 992-996, June.
- Drost, F.C. & Akker, R. van den & Werker, B.J.M., 2007. "Note on Integer-Valued Bilinear Time Series Models," Discussion Paper 2007-47, Tilburg University, Center for Economic Research.
- Drost, F.C. & Akker, R. van den & Werker, B.J.M., 2008. "Note on integer-valued bilinear time series models," Open Access publications from Tilburg University urn:nbn:nl:ui:12-347715, Tilburg University.
- Feike C. Drost & Ramon van den Akker & Bas J. M. Werker, 2009. "Efficient estimation of auto-regression parameters and innovation distributions for semiparametric integer-valued AR("p") models," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 71(2), pages 467-485.
- Konstantinos Fokianos & Dag Tjøstheim, 2012. "Nonlinear Poisson autoregression," Annals of the Institute of Statistical Mathematics, Springer, vol. 64(6), pages 1205-1225, December.
- Yunwei Cui & Robert Lund, 2009. "A new look at time series of counts," Biometrika, Biometrika Trust, vol. 96(4), pages 781-792.
- Jung, Robert C. & Kukuk, Martin & Liesenfeld, Roman, 2006. "Time series of count data: modeling, estimation and diagnostics," Computational Statistics & Data Analysis, Elsevier, vol. 51(4), pages 2350-2364, December.
- Christian Weiß, 2008. "Thinning operations for modeling time series of counts—a survey," AStA Advances in Statistical Analysis, Springer, vol. 92(3), pages 319-341, August.
- Richard A. Davis, 2003. "Observation-driven models for Poisson counts," Biometrika, Biometrika Trust, vol. 90(4), pages 777-790, December.
- Fokianos, Konstantinos & Tjøstheim, Dag, 2011. "Log-linear Poisson autoregression," Journal of Multivariate Analysis, Elsevier, vol. 102(3), pages 563-578, March.
- Heinen, Andreas & Rengifo, Erick, 2007. "Multivariate autoregressive modeling of time series count data using copulas," Journal of Empirical Finance, Elsevier, vol. 14(4), pages 564-583, September.
- Hans Jensen, 2004. "Review Essay," Review of Social Economy, Taylor & Francis Journals, vol. 62(1), pages 101-112.
- Chao Wang & Wai Keung Li, 2011. "On the autopersistence functions and the autopersistence graphs of binary autoregressive time series," Journal of Time Series Analysis, Wiley Blackwell, vol. 32(6), pages 639-646, November.
- Brendan P.M. McCabe & Gael M. Martin & David Harris, 2009. "Optimal Probabilistic Forecasts for Counts," Monash Econometrics and Business Statistics Working Papers 7/09, Monash University, Department of Econometrics and Business Statistics.
- Doukhan, Paul & Louhichi, Sana, 1999. "A new weak dependence condition and applications to moment inequalities," Stochastic Processes and their Applications, Elsevier, vol. 84(2), pages 313-342, December.
- Sant'Anna, Pedro H. C., 2013. "Testing for Uncorrelated Residuals in Dynamic Count Models with an Application to Corporate Bankruptcy," MPRA Paper 48376, University Library of Munich, Germany.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Guenther Eichhorn) or (Christopher F Baum).
If references are entirely missing, you can add them using this form.