Re: [R] Time Series Count Models

From: Spencer Graves <spencer.graves_at_pdf.com>
Date: Tue 19 Jul 2005 - 09:19:07 EST

          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:

          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@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
>>>
>>>______________________________________________
>>>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
>>
>>--
>>Spencer Graves, PhD
>>Senior Development Engineer
>>PDF Solutions, Inc.
>>333 West San Carlos Street Suite 700
>>San Jose, CA 95110, USA
>>
>>spencer.graves@pdf.com
>>www.pdf.com <http://www.pdf.com>
>>Tel: 408-938-4420
>>Fax: 408-280-7915
>>
> 
> 
> ______________________________________________
> R-help@stat.math.ethz.ch mailing list
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> 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@pdf.com
www.pdf.com <http://www.pdf.com>
Tel:  408-938-4420
Fax: 408-280-7915

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Received on Tue Jul 19 09:25:13 2005

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