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A Linear Poisson Autoregressive Model: The Poisson AR(p) Model

Published online by Cambridge University Press:  04 January 2017

Patrick T. Brandt  and
John T. Williams
Patrick T. Brandt
Affiliation:
Department of Political Science and Workshop in Political Theory and Policy Analysis, Woodburn Hall 210, Indiana University, Bloomington, Indiana 47405. e-mail: [email protected]:http://www.polsci.indiana.edu/jotwilli/home
John T. Williams
Affiliation:
Department of Political Science and Workshop in Political Theory and Policy Analysis, Woodburn Hall 210, Indiana University, Bloomington, Indiana 47405. e-mail: [email protected]:http://www.polsci.indiana.edu/jotwilli/home
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Abstract

Time series of event counts are common in political science and other social science applications. Presently, there are few satisfactory methods for identifying the dynamics in such data and accounting for the dynamic processes in event counts regression. We address this issue by building on earlier work for persistent event counts in the Poisson exponentially weighted moving-average model (PEWMA) of Brandt et al. (American Journal of Political Science 44(4):823–843, 2000). We develop an alternative model for stationary mean reverting data, the Poisson autoregressive model of order p, or PAR(p) model. Issues of identification and model selection are also considered. We then evaluate the properties of this model and present both Monte Carlo evidence and applications to illustrate.

Type
Research Article
Information
Political Analysis , Volume 9 , Issue 2 , 2001 , pp. 164 - 184
Copyright
Copyright © 2001 by the Society for Political Methodology 

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