NEGLECTED SERIAL CORRELATION TESTS IN UCARIMA MODELS

We derive computationally simple and intuitive score tests of neglected serial correlation in unobserved component univariate models using frequency domain techniques. In some common situations in which the information matrix is singular under the null we derive extremum tests that are asymptotically equivalent to likelihood ratio tests, which become one-sided, and explain how to compute reliable Wald tests. We also explicitly relate the incidence of those problems to the model identification conditions and compare our tests with tests based on the reduced form prediction errors. Our Monte Carlo exercises assess the finite sample reliability and power of our proposed tests.