LAUSR

The paper proposes a sequential monitoring scheme for detecting changes in parameter values for general time series models using pairwise likelihood. Under this scheme, a change-point is declared when the cumulative sum of the first derivatives of pairwise likelihood exceeds a certain boundary function. The scheme is shown to have asymptotically zero Type II error with a prescribed level of Type I error. With the use of pairwise likelihood, the scheme is applicable to many complicated time series models in a computationally efficient manner. For example, the scheme covers time series models involving latent processes, such as stochastic volatility models and Poisson regression models with log link function.