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RE: st: Time Series Poisson
Cameron McIntosh <email@example.com>
STATA LIST <firstname.lastname@example.org>
RE: st: Time Series Poisson
Sun, 30 Oct 2011 21:42:54 -0400
Hi Richard, Tirthankar
I might also suggest:
Oh, M.-S., & Lim, Y.B. (2001). Bayesian analysis of time series Poisson data. Journal of Applied Statistics, 28(2), 259-271.
Drescher, D. (2008). Testing for presence of a latent process in count series. Journal of Statistical Computation and Simulation, 78(7), 595-607.
Jung, R.C., Kukuk, M., & Liesenfeld, R. (2006). Time series of count data: modeling, estimation and diagnostics. Computational Statistics & Data Analysis, 51(4), 2350-2364.
Jorgensen, B., Lundbye-Christensen, S., Song, P.X-K., & Sun, Li. (1999). A State Space Model for Multivariate Longitudinal Count Data. Biometrika, 86(1), 169-181.
Knape, J., Jonzén, N., Sköld, M., & Sokolov, L. (2009). Multivariate state-space modelling of bird migration count data. Environmental and Ecological Statistics, 3(Section I), 59-79.
Knape et al. (2009) model a 54-year time series of various bird species counts, your student might follow their Bayesian strategy... I was also thinking that it might be possible to fit a latent growth curve or multilevel model to this type of data, with Poisson links on the 45 indicators:
Liu, H. (2007). Growth Curve Models for Zero-Inflated Count Data: An Application to Smoking Behavior. Structural Equation Modeling, 14(2), 247-279.
Alosh, M. (2009). Modeling longitudinal count data with dropouts. Pharmaceutical Statistics, 9(1), 35-45.
Min, Y., & Agresti, A. (2005). Random effect models for repeated measures of zero-inflated count data. Statistical Modelling, 5(1), 1-19. Coelho-Barrosa, E.A., Achcar, J.A., & Mazucheli, J. (2010). Longitudinal Poisson modeling: an application for CD4 counting in HIV-infected patients. Journal of Applied Statistics, 37(5), 865-880.
My two cents,
> Date: Sun, 30 Oct 2011 18:04:46 -0700
> Subject: Re: st: Time Series Poisson
> From: email@example.com
> To: firstname.lastname@example.org
> See chapter 4 in this book:
> On Sun, Oct 30, 2011 at 6:53 PM, Richard Williams
> <email@example.com> wrote:
> > One of my students (a political scientist of course -- they always bring up
> > these weird problems I have never encountered myself!) has a data set that
> > consists of 45 yearly records for the United States. The dependent variable
> > is a count. It sounded to me like the sort of thing that should be analyzed
> > by a time series poisson model. But, unfortunately, I wasn't even sure that
> > such a thing existed - I was hoping there was a tspoisson command, but no
> > such luck.
> > However, I found this Stata Technical Bulletin for a very old user-written
> > command called nwest. http://www.stata.com/products/stb/journals/stb39.pdf.
> > It says "This article discusses the calculation of standard errors that are
> > robust to heteroscedasticity and serial correlation for probit, logit, and
> > poisson regression models."
> > I also found this slightly newer post from 2003:
> > http://www.stata.com/statalist/archive/2003-06/msg00258.html.
> > What I take from this is that he should -tsset- his data and use -glm- to
> > estimate a Poisson model with Newey-West standard errors, e.g. something
> > like
> > glm y x1 x2 x3, family(poisson) link(log) vce(hac nwest)
> > Does this sound right, and if so is this the best he can do, at least with
> > Stata?
> > -------------------------------------------
> > Richard Williams, Notre Dame Dept of Sociology
> > OFFICE: (574)631-6668, (574)631-6463
> > HOME: (574)289-5227
> > EMAIL: Richard.A.Williams.5@ND.Edu
> > WWW: http://www.nd.edu/~rwilliam
> > *
> > * For searches and help try:
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> > * http://www.ats.ucla.edu/stat/stata/
> Tirthankar Chakravarty
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