This paper proposes asymptotically optimal tests for unstable parameter process under the feasible circumstance that the researcher has little information about the unstable parameter process and the error distribution, and suggests conditions under which the knowledge of those processes does...

In There is evidence that exponential smoothing methods as well as time varying parameter models perform relatively well in forecasting comparisons. The aim of this paper is to introduce a new forecasting technique by integrating the exponential smoothing model with regressors whose coefficients...

This paper proposes a new fractional model with a time-varying long-memory parameter. The latter evolves nonlinearly according to a transition variable through a logistic function. We present a LR-based test that allows to discriminate between the standard fractional model and our model. We...

Time-varying GARCH-M models are commonly employed in econometrics and financial economics. Yet the recursive nature of the conditional variance makes exact likelihood analysis of these models computationally infeasible. This paper outlines the issues and suggests to employ a Markov chain Monte...

This paper proposes a new fractional model with a time-varying long-memory parameter. The latter evolves nonlinearly according to a transition variable through a logistic function. We present a LR-based test that allows to discriminate between the standard fractional model and our model. We...

This paper proposes asymptotically point optimal tests for parameter instability under the feasible circumstance that the researcher has little information about the unstable parameter process and the error distribution. The shape of the unstable parameter process is not identified but is...