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Researchers faced with a sequence of candidate model specifications mustoften choose the best specification that does not violate a testableidentification assumption. One option in this scenario is sequentialspecification tests: hypothesis tests of the identification assumption over thesequence. Borrowing an idea from the change-point literature, this paper showshow to use the distribution of p-values from sequential specification tests toestimate the point in the sequence where the identification assumption ceasesto hold. Unlike current approaches, this method is robust to individual errantp-values and does not require choosing a test level or tuning parameter. Thispaper demonstrates the method's properties with a simulation study, andillustrates it by application to the problems of choosing a bandwidth in aregression discontinuity design while maintaining covariate balance and ofchoosing a lag order for a time series model.

Sequential Specification Tests to Choose a Model: A Change-Point Approach