Abstract: Difficulties with inference in predictive regressions are generally attributed to strong persistence in the predictor series. We show that the major source of the problem is actually the nuisance intercept parameter and propose basing inference on the Restricted Likelihood, which is...

Difficulties with inference in predictive regressions are generally attributed tostrong persistence in the predictor series. We show that the major source of the problem is actually the nuisance intercept parameter and propose basing inference on the Restricted Likelihood,which is free of such...

We study the small sample behaviour of two goodness-of-fit tests for time series models whichhave been proposed recently in the literature. Both tests are generalizations of the popular Box-Ljung-Pierce portmanteau test, one in the time domain and the other in the frequency domain.The tests are...

We present a goodness of fit test for time series models based on the discrete spectral averageestimator. Unlike current tests of goodness of fit, the asymptotic distribution of our test statisticallows the null hypothesis to be either a short or long range dependence model. Our test isin the...

Difficulties with inference in predictive regressions are generally attributed to strong persistence in the predictor series. We show that the major source of the problem is actually the nuisance intercept parameter and propose basing inference on the Restricted Likelihood,which is free of such...