"A Regression Error Specification Test (reset) for Generalized Linear Models".

Generalized linear models (GLMs) are generalizations of linear regression models, which allow fitting regression models to response data that follow a general exponential family. GLMs are used widely in social sciences for fitting regression models to count data, qualitative response data and duration data. While a variety of specification tests have been developed for the linear regression model and are routinely applied for testing for misspecification of functional form, omitted variables, and the normality assumption, such tests and their applications to GLMs are uncommon. This paper develops a regression error specification test (RESET) for GLMs as an extension of the popular RESET for the linear regression model (Ramsey (1969)). Applications of the RESET to three economic data sets are presented and the finite sample power properties are studied via a Monte Carlo experiment.