# [R] Time series count model?

Achim Zeileis zeileis at ci.tuwien.ac.at
Tue Nov 20 16:12:50 CET 2001

```pauljohn at ukans.edu wrote:
>
> I fear I need someone to throw a brick at my head to shake loose
> the cobwebs. And it might as well be the r friends as anybody!
>
> I am trying to counsel a student who has count data (with many
> 0's and small nuumbers) that is a time series.  He was fitting
> linear models to this data, but the count nature of the data
> causes me a lot of concern, and I am looking around for a time
> series approach to a model which allows all the bells and
> whistles of count models. By chance, I notice that Congdon's
> WinBUGS examples have an example that has a count model with
> time series issues, but I fear it might be too great of a leap
> for us to justify a paradigm switch to Bayesian statistics in
> order to fit this one model.
>
> If you were using R, how would you get a foot hold on this
> problem?

I remember that I experimented with some code I hacked according to

HARVEY, A.C. "Forecasting, structural time series models and the
Kalman filter"

who has a chapter about count data and Poisson observations. He suggests
a time series of Poisson observations, whose mean is chosen by a gamma
distribution with time-variable paramters (if I recall it correctly).

Maybe that reference might help
Z

> I want a NegativeBinomial count model (terminology maybe
> ambiguous: that means a Poisson with input (mean) value derived
> from a Gamma(Xb,1) process) and the possiblity of zero-inflated
> data, which means the likelihood of an observation is multiplied
> by 1 or 0 according to a draw from a logistic distribution. (I
> realize this is starting to sound junked up, but Scott Long's
> book on Regression with Qualitative Depenedent Variables writes
> out all the details.)
>
> Because the theory that inspires this model is a dynamic
> process, it has a lagged dependent variable on the right hand
> side. So when I say X here, I mean a matrix in which lagged y's
> are included as columns. So the individual observation's
> likelihood is (I believe this is the Negative Binomial model
> with zero inflagion factor)
>
> input = draw from Gamma(exp(Xb),1))
> zif = draw from logistic(Xb)
>
> if (zif == 1) then:
> p(y |X,b) = Pois( input )
>
> if (zif == 0) then:
> y=0
>
> I suppose it is not necessarily true the zero inflation logit
> part depends on the exact same coefficients as the Gamma part,
> but lets worry about that later....
>
> We have a copy of Stata sitting around here and the students
> have found in there a canned procedure to estimate that model,
> it does various specificiation tests, such as a test for whether
> the zero inflation part is necessary.  But that does not attend
> to the time-series part. I don't do Stata myself, I am learning
> R, and would like to see if I can do this in R.
>
> We need to incorporate the possibility of an "error term" that
> is influenced by its own lagged values in the usual ARIMA
> sense.  Looking back to the justification for the Negative
> Binomial in the first place, I remember one justification for
> the NB model was a Poisson with heterogeneity.
>
> p(y | X,b) = Pois (exp(Xb + e))
>
> I don't think I ever understood very well why this leads to a NB
> model. Maybe that's where I need to study.
>
> Nevertheless, where can I go if I start with that theory, but
> the e are not independent, say they are MA(1)
>
> e_t = g*e_{t-1} + u_t
>
> and u_t is Normal(0,sigma^2).
>
> Should I just write out a big log likelihood function and use
> R's optim to fit it?
>
> It seems like I'm missing out on something by going that route,
> though.
>
> --
> Paul E. Johnson                       email: pauljohn at ukans.edu
> Dept. of Political Science
> http://lark.cc.ukans.edu/~pauljohn
> University of Kansas                  Office: (785) 864-9086
> Lawrence, Kansas 66045                FAX: (785) 864-5700
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