Time Series Count Models
We are leveraging too far on speculation, at least from what I can see. PLEASE do read the posting guide! "http://www.R-project.org/posting-guide.html". In particular, try the simplest example you can find that illustrates your question, and explain your concerns to us in terms of a short series of R commands and the resulting output. With counts, especially if there were only a few zeros, I'd start by taking logarithms (after replacing 0's by something like 0.5 or by adding something like 0.5 to avoid sending 0's to (-Inf)) and use "lme", if that seemed appropriate. Then if I got drastically different answers from other software, I would suspect a problem. Other possibilities for count data are the following: * "lmer" library(lme4) [see Douglas Bates. Fitting linear mixed models in R. R News, 5(1):27-30, May 2005, www.r-project.org -> Newsletter -> "Volume 5/1, May 2005: PDF". * "glmmPQL" in library(MASS). * "glmmML" in library(glmmML) However, I don't know if any of these as the capability now to handle short time series like you described. You might also consider the IEKS package by Bjarke Mirner Klein (http://www.stat.sdu.dk/publications/monographs/m001/KleinPhdThesis.pdf and http://genetics.agrsci.dk/~bmk/IEKS.R). spencer graves
Brett Gordon wrote:
Thanks for the suggestion. Is such a model appropriate for count data? The library you reference seems to just be form standard regressions (ie those with continuous dependent variables). Thanks, Brett On 7/16/05, Spencer Graves <spencer.graves at pdf.com> wrote:
Have you considered "lme" in library(nlme)? If you want to go this
route, I recommend Pinheiro and Bates (2000) Mixed-Effect Models in S
and S-Plus (Springer).
spencer graves
Brett Gordon wrote:
Hello, I'm trying to model the entry of certain firms into a larger number of distinct markets over time. I have a short time series, but a large cross section (small T, big N). I have both time varying and non-time varying variables. Additionally, since I'm modeling entry of firms, it seems like the number of existing firms in the market at time t should depend on the number of firms at (t-1), so I would like to include the lagged cumulative count. My basic question is whether it is appropriate (in a statistical sense) to include both the time varying variables and the lagged cumulative count variable. The lagged count aside, I know there are standard extensions to count models to handle time series. However, I'm not sure if anything changes when lagged values of the cumulative dependent variable are added (i.e. are the regular standard errors correct, are estimates consistent, etc....). Can I still use one of the time series count models while including this lagged cumulative value? I would greatly appreciate it if anyone can direct me to relevant material on this. As a note, I have already looked at Cameron and Trivedi's book. Many thanks, Brett
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______________________________________________ R-help at stat.math.ethz.ch mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide! http://www.R-project.org/posting-guide.html
Spencer Graves, PhD Senior Development Engineer PDF Solutions, Inc. 333 West San Carlos Street Suite 700 San Jose, CA 95110, USA spencer.graves at pdf.com www.pdf.com <http://www.pdf.com> Tel: 408-938-4420 Fax: 408-280-7915