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From |
Cameron McIntosh <cnm100@hotmail.com> |

To |
STATA LIST <statalist@hsphsun2.harvard.edu> |

Subject |
RE: st: Time Series Poisson |

Date |
Mon, 31 Oct 2011 07:27:34 -0400 |

Rich, I also assume you must be modeling some type of aggregate (US-level) count, so I wonder if an ecological growth curve could be fit to the series: Yang, C.C., Yang, C.C., & Yeh, K.H. (2005). Ecological-Inference-Based Latent Growth Models: Modeling Changes of Alienation. Quality & Quantity, 39(2), 125-135. I have some other papers that you might want to take a peak at, but unfortunately the nifty, pre-programmed Stata-specific solution seems elusive. :) For example, Wu and Cao (2011) apply their blockwise empirical likelihood approach to analyze counts on polio cases in the US and daily non-accidental deaths in Toronto -- so a bit similar to your case in having only one geographic area and one outcome (they also have one or two time-varying covariates but I don't think these would be required to use the approach). Schmidt, A.M., & Pereira, J.B.M. (2011). Modelling Time Series of Counts in Epidemiology. International Statistical Review, 79(1), 48–69. Wu, R., & Cao, J. (2011). Blockwise empirical likelihood for time series of counts. Journal of Multivariate Analysis, 102(3), 661-673. Franke, J., Kirch, C., & Kamgaing, J.T. (May 15, 2011). Changepoints in Times Series of Counts.http://mspcdip.mathematik.uni-karlsruhe.de/~ckirch/papers/pp_INARCH_CP.pdf Thomas, S.J. (May 2010). Model-based clustering for multivariate time series of counts. Doctoral Dissertation. Houston, TX: Rice University.http://scholarship.rice.edu/bitstream/handle/1911/62066/3421317.PDF?sequence=1 Freeland, R.K., & McCabe, B.P.M. (2004). Analysis of low count time series data by poisson autoregression. Journal of Time Series Analysis, 25(5), 701-722. Davis, R.A., Dunsmuir, W.T.M., & Wang, Y. (2000). On Autocorrelation in a Poisson Regression Model. Biometrika, 87(3), 491-505. Jung, R.C., & Tremayne, A.R. (2003). Testing for serial dependence in time series models of counts. Journal of Time Series Analysis, 24(1), 65–84. Hay, J.L., & Pettitt, A.N. (2001). Bayesian analysis of a time series of counts with covariates: an application to the control of an infectious disease. Biostatistics, 2(4), 433-444.http://biostatistics.oxfordjournals.org/content/2/4/433.full.pdf Fokianos, K. (2001). Truncated Poisson Regression for Time Series of Counts. Scandinavian Journal of Statistics, 28(4), 645–659. Brandt, P.T., Williams, J.T., Fordham, B.O., & Pollins, B. (2000). Dynamic Modeling for Persistent Event-Count Time Series. American Journal of Political Science, 44(4), 823-843. Shen, H., & Huang, J.Z. (2008). Forecasting time series of inhomogenous poisson processes with application to call center workforce management. The Annals of Applied Statistics, 2(2), 601–623.http://www.unc.edu/~haipeng/publication/poissonSVD.pdf Boucher, J.-P., & Guillen, M. (2009). A survey on models for panel count data with applications to insurance. RACSAM, 103(2), 277–294.http://www.rac.es/ficheros/doc/00698.pdf Sparks, R.S., Keighley, T., & Muscatello, D. (2009). Improving EWMA Plans for Detecting Unusual Increases in Poisson Counts. Journal of Applied Mathematics and Decision Sciences, 2009, Article ID 512356. FrüHwirth-Schnatter, S., & Wagner, H. (2006). Auxiliary mixture sampling for parameter-driven models of time series of counts with applications to state space modelling. Biometrika, 93(4), 827-841. Best, Cam > Date: Sun, 30 Oct 2011 23:46:55 -0500 > To: statalist@hsphsun2.harvard.edu > From: richardwilliams.ndu@gmail.com > Subject: RE: st: Time Series Poisson > > Thanks Cameron and Tirthankar. Many of these look > promising. To be clear, there is only 1 country > involved here, with separate records for each of > 45 years. Some of these sources sound like they > might be designed for cross-sectional time series > rather than time series, but I don't understand > either well enough to really say. (And I am still > hoping for that nifty Stata-specific solution, > since I am not sure how far we will get if we > have to figure out how to program this ourselves!) > > At 08:42 PM 10/30/2011, Cameron McIntosh wrote: > >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, > >Cam > > > Date: Sun, 30 Oct 2011 18:04:46 -0700 > > > Subject: Re: st: Time Series Poisson > > > From: tirthankar.chakravarty@gmail.com > > > To: statalist@hsphsun2.harvard.edu > > > > > > Richard, > > > > > > See chapter 4 in this book: > > > http://amzn.com/0471363553 > > > > > > T > > > > > > > > > On Sun, Oct 30, 2011 at 6:53 PM, Richard Williams > > > <richardwilliams.ndu@gmail.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: > > > > * http://www.stata.com/help.cgi?search > > > > * http://www.stata.com/support/statalist/faq > > > > * http://www.ats.ucla.edu/stat/stata/ > > > > > > > > > > > > > > > > -- > > > Tirthankar Chakravarty > > > tchakravarty@ucsd.edu > > > tirthankar.chakravarty@gmail.com > > > > > > * > > > * For searches and help try: > > > * http://www.stata.com/help.cgi?search > > > * http://www.stata.com/support/statalist/faq > > > * http://www.ats.ucla.edu/stat/stata/ > > > >* > >* For searches and help try: > >* http://www.stata.com/help.cgi?search > >* http://www.stata.com/support/statalist/faq > >* http://www.ats.ucla.edu/stat/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: > * http://www.stata.com/help.cgi?search > * http://www.stata.com/support/statalist/faq > * http://www.ats.ucla.edu/stat/stata/ * * For searches and help try: * http://www.stata.com/help.cgi?search * http://www.stata.com/support/statalist/faq * http://www.ats.ucla.edu/stat/stata/

**References**:**st: Time Series Poisson***From:*Richard Williams <richardwilliams.ndu@gmail.com>

**Re: st: Time Series Poisson***From:*Tirthankar Chakravarty <tirthankar.chakravarty@gmail.com>

**RE: st: Time Series Poisson***From:*Cameron McIntosh <cnm100@hotmail.com>

**RE: st: Time Series Poisson***From:*Richard Williams <richardwilliams.ndu@gmail.com>

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