Parameters in AutoRegressive Moving Average (ARMA) models are locally nonidentified, due to the problem of root cancellation. Parameters can be constructed which represent this identification problem. We argue that ARMA parameters should be analyzed conditional on these identifying parameters....

This paper shows how to cope with a problem of model selection and simplification using the principle of coherence (Gabriel (1969): A procedure involving testing a set of models ought not accept a model while rejecting a more general model). The mathematical lattice theory is used to define a...

The motivation for this paper is to determine the potential economic value of advanced modelling methods for devising trading decision tools for 10-year Government bonds. Two advanced methods are used: time-varying parameter models with the implementation of state space modelling using a Kalman...

Parameters in ARMA models are only locally identified. It is shown that the use of diffuse priors in these models leads to a preference for locally nonidentified parameter values. We therefore suggest to use likelihood based priors like the Jeffreys' priors which overcome these problems. An...

Stock returns, whether nominal or real, are commonly found to depend negatively on actual inflation, expected inflation and unexpected inflation. This runs contrary to the Fisher hypothesis generalised to apply to stocks, whereby stocks should be a hedge against inflation. However, another...

We develop a $C_{p}$ statistic for the selection of regression models with stationary and nonstationary ARIMA error term. We derive the asymptotic theory of the maximum likelihood estimators and show they are consistent and asymptotically Gaussian. We also prove that the distribution of the sum...

In this article we extend the results derived for scan statistics in Wang and Glaz (2014) for independent normal observations. We investigate the performance of two approximations for the distribution of fixed window scan statistics for time series models. An R algorithm for computing...