%0 Journal Article %1 BrandtWilliams:2000 %A Brandt, Patrick T. %A Williams, John T. %A Fordham, Benjamin O. %A Pollins, Brian %D 2000 %J American Journal of Political Science %K methodology theory time-series %N 4 %P 823--843 %T Dynamic Modeling for Persistent Event Count Time Series %V 44 %X We present a method for estimating event-count models when the data is generated from a persistent time-series process. A Kalman filter is used to estimate a Poisson exponentially weighted moving average (PEWMA) model. The model is compared to extant methods (Poisson regression, negative binomial regression, and ARIMA models). Using Monte Carlo experiments, we demonstrate that the PEWMA provides significant improvements in efficiency. As an example, we present an analysis of Pollins (1996) models of long cycles in international relations.

@article{BrandtWilliams:2000, abstract = {We present a method for estimating event-count models when the data is generated from a persistent time-series process. A Kalman filter is used to estimate a Poisson exponentially weighted moving average (PEWMA) model. The model is compared to extant methods (Poisson regression, negative binomial regression, and ARIMA models). Using Monte Carlo experiments, we demonstrate that the PEWMA provides significant improvements in efficiency. As an example, we present an analysis of Pollins (1996) models of long cycles in international relations.}, added-at = {2010-03-02T17:25:53.000+0100}, author = {Brandt, Patrick T. and Williams, John T. and Fordham, Benjamin O. and Pollins, Brian}, bdsk-file-1 = {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}, bdsk-url-1 = {http://www.jstor.org/stable/info/2669284}, biburl = {https://www.bibsonomy.org/bibtex/240423141f686075d200d5be8992db141/jrennstich}, date-modified = {2010-02-28 21:03:30 -0500}, interhash = {069b800787d72fa2d2a32d3941fce231}, intrahash = {40423141f686075d200d5be8992db141}, journal = {American Journal of Political Science}, keywords = {methodology theory time-series}, month = {October}, number = 4, pages = {823--843}, timestamp = {2010-03-02T17:27:31.000+0100}, title = {Dynamic Modeling for Persistent Event Count Time Series}, volume = 44, year = 2000 }