From: Brett Gordon <brgordon_at_gmail.com>

Date: Tue 19 Jul 2005 - 09:40:08 EST

R-help@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 Received on Tue Jul 19 09:45:03 2005

Date: Tue 19 Jul 2005 - 09:40:08 EST

Paul,

Thank you so much for your thoughtful reply. I agree - there are many possible descriptions for my data, and I realize that I don't want to get bogged down with figuring out the 'best' model if something simple will work well. For me, I think the difficulty is going to be handling the cumulative aspect of the lagged variable.

To be clear, suppose that y_1, ..., y_T are the counts. At time t=3, I want to include the quantity (y_1 + y_2) as an independent variable, and so on. I wonder if this is as simple as solving a conditional ML problem......I'll have to look more deeply into it.

Again, thanks for the references.

-Brett

On 7/18/05, Paul Johnson <pauljohn@ku.edu> wrote:

> Dear Brett:

*>
**> There are books for this topic that are more narrowly tailored to your
**> question. Lindsey's Models for Repeated Measurements and Diggle, et al's
**> Analysis of Longitudinal Data. Lindsey offers an R package on his web
**> site. If you dig around, you will find many modeling papers on this,
**> although in my mind none coalesced into a completely clear path such as
**> "throw in these variables and you will get the right estimates".
**>
**> The problem, as you will see, is that there are many possible
**> mathematical descriptions of the idea that there is time dependence in a
**> count model.
**>
**> My political science colleagues John Williams and Pat Brandt published 2
**> articles on time series with counts. My favorite is the second one
**> here. There is R code for the Pests model.
**> http://www.utdallas.edu/~pbrandt/pests/pests.htm
**>
**> Brandt, Patrick T., John T. Williams, Benjamin O. Fordham and Brian
**> Pollins. 2000. "Dynamic Modelling For Persistent Event Count Time
**> Series." American Journal of Political Science 44(4): 823-843.
**>
**> Brandt, Patrick T. and John T. Williams. 2001. "A Linear Poisson
**> Autoregressive Model: the Poisson AR(p) Model." Political Analysis 9(2):
**> 164-184.
**>
**> I worked really hard on TS counts a while ago because a student was
**> trying that. If you look at J Lindsay's book Models for Repeated
**> Measures you will make some progress on understanding his method
**> kalcount. That's in the repeated library you get from his web site.
**>
**> Here are the notes I made a couple of years ago
**>
**> http://lark.cc.ku.edu/~pauljohn/stats/TimeSeries/
**>
**> Look for files called TSCountData*.pdf.
**>
**>
**> It all boils down to the fact that you can't just act like it is an OLS
**> model and throw Y_t-1 or something like that on the right had side.
**> Instead, you have to think in a more delicate way about the process you
**> are modeling and hit it from that other direction.
**>
**> Here are some of the articles for which I kept copies.
**>
**> U. Bokenholt, "Mixed INAR(1) Poisson regression models" Journal of
**> Econometrics, 89 (1999): 317-338
**>
**> A.C. Harvey and C. Fernandes, "Time Series Models for Count or
**> Qualitative Observations, " Journal of Business & Economic Statistics, 4
**> (1989): 407-
**>
**>
**> I recall liking this one a lot
**>
**> J E Kelsall and Scott Zeger and J M Samet "Frequency Domain Log-linear
**> Models; air pollution and mortality" Appl. Statis 48 1999 331-344.
**>
**> Good luck, let me know what you find out.
**>
**> pj
**>
**> 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
**> >
**> > ______________________________________________
**> > R-help@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
**>
**>
**> --
**> Paul E. Johnson email: pauljohn@ku.edu
**> Dept. of Political Science http://lark.cc.ku.edu/~pauljohn
**> 1541 Lilac Lane, Rm 504
**> University of Kansas Office: (785) 864-9086
**> Lawrence, Kansas 66044-3177 FAX: (785) 864-5700
*

>

R-help@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 Received on Tue Jul 19 09:45:03 2005

*
This archive was generated by hypermail 2.1.8
: Fri 03 Mar 2006 - 03:33:46 EST
*