A Goodness Of Fit Test For Ergodic Markov Processes

Abstract

We introduce a goodness of fit test for ergodic Markov processes. Our test compares the data against the set of stationary densities implied by the class of models specified in the null hypothesis, and rejects if no model in the class yields a stationary density that matches with the data. No alternative needs to be specified in order to implement the test. Although our test compares densities it involves no smoothing parameters, and is powerful against 1√n local alternatives.