Alternative Versions of the RESET Test for Binary Response Index Models: A Comparative Study


  • The authors thank the editor and the referees for helpful comments. Aspects of this research were presented at the 25th Annual Congress of the European Economic Association, Glasgow, the 16th International Conference on Computing in Economics and Finance, London, and the 3rd International Conference of the ERCIM Working Group on Computing & Statistics, London. Financial support from Fundação para a Ciência e a Tecnologia is gratefully acknowledged (grant PTDC/ECO/64693/2006).


Binary response index models may be affected by several forms of misspecification, which range from pure functional form problems (e.g. incorrect specification of the link function, neglected heterogeneity, heteroskedasticity) to various types of sampling issues (e.g. covariate measurement error, response misclassification, endogenous stratification, missing data). In this article we examine the ability of several versions of the RESET test to detect such misspecifications in an extensive Monte Carlo simulation study. We find that: (i) the best variants of the RESET test are clearly those based on one or two fitted powers of the response index; and (ii) the loss of power resulting from using the RESET instead of a test directed against a specific type of misspecification is very small in many cases.