Accounting for Excess Zeros and Sample Selection in Poisson and Negative Binomial Regression Models

Source: RePEc

ABSTRACT We present several modifications of the Poisson and negative binomial models for count data to accommodate cases in which the number of zeros in the data exceed what would typically be predicted by either model. The excess zeros can masquerade as overdispersion. We present a new test procedure for distinguishing between zero inflation and overdispersion. We also develop a model for sample selection which is analogous to the Heckman style specification for continuous choice models. An application is presented to a data set on consumer loan behavior in which both of these phenomena are clearly present.

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    ABSTRACT: Scholars may become journal editors because editors may generate more citations of their own works. This paper empirically establishes that a scholar's publications are more likely to be cited by papers in a journal that is edited by the scholar. We then test if editors exercise influence on authors to cite editors’ papers by either pressuring authors (“editor-pressure” hypothesis) or accepting articles with references to the editors’ papers (“editor-selection” hypothesis), by using the keyword analysis and the forward citation analysis, respectively. We find no evidence for the two hypotheses, which leaves self-selection as a possible cause for the editor effect. JEL classification: J01 Motivations des directeurs de revues. Les chercheurs peuvent devenir directeurs de revues parce que ce rôle peut engendrer plus de citations de leurs propres travaux. Ce mémoire montre empiriquement que les publications d'un chercheur sont davantage susceptibles d’être citées dans une revue qu'il dirige. On teste deux hypothèses à savoir si les directeurs exercent de l'influence sur les auteurs pour citer leurs travaux (soit en pressant les auteurs – hypothèse de pression du directeur – soit en acceptant les articles qui font référence à leurs travaux – hypothèse de sélection du directeur) à l'aide d'une analyse des mots-clés et des références (forward citation) respectivement. Les résultats ne supportent ni l'une ni l'autre des hypothèses, ce qui laisse l'auto-sélection du chercheur comme une cause possible de l'effet-directeur.
    Canadian Journal of Economics/Revue Canadienne d`Economique 02/2014; 47(1). · 0.61 Impact Factor
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    ABSTRACT: Genetic modification of plants may result in unintended effects causing potentially adverse effects on the environment. A comparative safety assessment is therefore required by authorities, such as the European Food Safety Authority, in which the genetically modified plant is compared with its conventional counterpart. Part of the environmental risk assessment is a comparative field experiment in which the effect on non-target organisms is compared. Statistical analysis of such trials come in two flavors: difference testing and equivalence testing. It is important to know the statistical properties of these, for example, the power to detect environmental change of a given magnitude, before the start of an experiment. Such prospective power analysis can best be studied by means of a statistical simulation model. This paper describes a general framework for simulating data typically encountered in environmental risk assessment of genetically modified plants. The simulation model, available as Supplementary Material, can be used to generate count data having different statistical distributions possibly with excess-zeros. In addition the model employs completely randomized or randomized block experiments, can be used to simulate single or multiple trials across environments, enables genotype by environment interaction by adding random variety effects, and finally includes repeated measures in time following a constant, linear or quadratic pattern in time possibly with some form of autocorrelation. The model also allows to add a set of reference varieties to the GM plants and its comparator to assess the natural variation which can then be used to set limits of concern for equivalence testing. The different count distributions are described in some detail and some examples of how to use the simulation model to study various aspects, including a prospective power analysis, are provided.
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    ABSTRACT: Innovation networks can contribute both to the creation of new knowledge and to the diffu-sion of existing and newly created knowledge. The Industrial Co-operative Research (ICR) programme aims at both targets at the same time. The success of this programme depends essentially on the performance of the innovation networks which have developed in close as-sociation with public financing. This contribution presents results obtained from evaluating network activities at the project level. The participation of firms in several network activities and the patterns of adoption of the new knowledge generated are analysed. By applying probit and zero-inflated Poisson regressions, we find that closeness of cooperation between research institutes and firms both in the creation and diffusion of knowledge is related with the subse-quent use of project results.

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Jun 2, 2014