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The glarma Package for Observation-Driven Time Series Regression of Counts

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  • Dunsmuir, William T. M.
  • Scott, David J.

Abstract

We review the theory and application of generalized linear autoregressive moving average observation-driven models for time series of counts with explanatory variables and describe the estimation of these models using the R package glarma. Forecasting, diagnostic and graphical methods are also illustrated by several examples.

Suggested Citation

  • Dunsmuir, William T. M. & Scott, David J., 2015. "The glarma Package for Observation-Driven Time Series Regression of Counts," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 67(i07).
    • Handle: RePEc:jss:jstsof:v:067:i07
    DOI: http://hdl.handle.net/10.18637/jss.v067.i07
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    References listed on IDEAS

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    8. Heinen, Andreas, 2003. "Modelling Time Series Count Data: An Autoregressive Conditional Poisson Model," MPRA Paper 8113, University Library of Munich, Germany.
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    10. 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.
    11. Roman Liesenfeld & Ingmar Nolte & Winfried Pohlmeier, 2008. "Modelling financial transaction price movements: a dynamic integer count data model," Studies in Empirical Economics, in: Luc Bauwens & Winfried Pohlmeier & David Veredas (ed.), High Frequency Financial Econometrics, pages 167-197, Springer.
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