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Applying the GLM Variance Assumption to Overcome the Scale-Dependence of the Negative Binomial QGPML Estimator
Authors:Clément Bosquet  Hervé Boulhol
Institution:1. Spatial Economics Research Centre , London School of Economics , London , United Kingdom;2. OECD , Université Paris, Panthéon-Sorbonne , Paris , France
Abstract:Recently, various studies have used the Poisson Pseudo-Maximal Likehood (PML) to estimate gravity specifications of trade flows and non-count data models more generally. Some papers also report results based on the Negative Binomial Quasi-Generalised Pseudo-Maximum Likelihood (NB QGPML) estimator, which encompasses the Poisson assumption as a special case. This note shows that the NB QGPML estimators that have been used so far are unappealing when applied to a continuous dependent variable which unit choice is arbitrary, because estimates artificially depend on that choice. A new NB QGPML estimator is introduced to overcome this shortcoming.
Keywords:Gamma PML  Negative binomial estimator  Pseudo-maximum likelihood methods  Poisson regression
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