Implicit Substantive Assumptions Underlying the Generalized Event Count Estimator

Author:

Achen Christopher H.

Abstract

The Generalized Event Count (GEC) estimator (King 1989a) is a statistical model for event counts. Its great attraction is that it provides a general likelihood function for count data, regardless of whether the data come from a Poisson, binomial, or negative binomial distribution. In consequence, it has been used in several recent statistical studies of event counts in the social sciences.Underlying the GEC, however, are unorthodox substantive assumptions about how the event counts have been generated (Amato, this volume). This paper gives some simple examples in which the GEC logic is clearly visible, and it shows how failures of the implicit assumptions can lead to erroneous GEC coefficient estimates and standard errors.

Publisher

Cambridge University Press (CUP)

Subject

Political Science and International Relations,Sociology and Political Science

Reference31 articles.

1. In the data set discussed here, the GEC's standard errors are too low essentially because it misjudges the binomial variance in the event counts. The point is most easily seen when xi = 0. In the data themselves, the conditional sample variance is 0.45 at xi = 0. The conventional MLE parameter estimates imply that this conditional binomial variance should be 0.475, quite close to the actual value. The GEC parameter estimates, on the other hand, imply that the variance should be 0.296, which is much too small. The resulting error is propagated to the standard errors of the coefficients. (See Amato, this volume, for additional discussion of the GEC standard errors.)

2. For nonintegral n, the mean and variance formulas hold only to a very good approximation (see footnote 5), which is sufficient for the expositional purpose at hand.

3. A Comparison of n Estimators for the Binomial Distribution;Olkin;Journal of the American Statistical Association,1981

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