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

William T. M. Dunsmuir and David J. Scott

Journal of Statistical Software, 2015, vol. 067, issue i07

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.

Date: 2015-10-07
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (4)

Downloads: (external link)
https://www.jstatsoft.org/index.php/jss/article/view/v067i07/v67i07.pdf
https://www.jstatsoft.org/index.php/jss/article/do ... /glarma_1.4-0.tar.gz
https://www.jstatsoft.org/index.php/jss/article/do ... ile/v067i07/v67i07.R

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:jss:jstsof:v:067:i07

DOI: 10.18637/jss.v067.i07

Access Statistics for this article

Journal of Statistical Software is currently edited by Bettina Grün, Edzer Pebesma and Achim Zeileis

More articles in Journal of Statistical Software from Foundation for Open Access Statistics
Bibliographic data for series maintained by Christopher F. Baum (baum@bc.edu).