Re: [R] Time Series Count Models

From: Spencer Graves <>
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! "". 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 ( and

          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 <> 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:
>>>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,
>>> mailing list
>>>PLEASE do read the posting guide!
>>Spencer Graves, PhD
>>Senior Development Engineer
>>PDF Solutions, Inc.
>>333 West San Carlos Street Suite 700
>>San Jose, CA 95110, USA
>> <>
>>Tel: 408-938-4420
>>Fax: 408-280-7915
> ______________________________________________
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> PLEASE do read the posting guide!

Spencer Graves, PhD
Senior Development Engineer
PDF Solutions, Inc.
333 West San Carlos Street Suite 700
San Jose, CA 95110, USA <>
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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