Root cancellation in Auto Regressive Moving Average (ARMA) models leads to local non-identification of parameters. When we use diffuse or normal priors on the parameters of the ARMA model, posteriors in Bayesian analyzes show an a posteriori favor for this local non-identification. We show that...

Root cancellation in Auto Regressive Moving Average (ARMA) models leads tolocal non-identification of parameters. When we use diffuse or normal priorson the parameters of the ARMA model, posteriors in Bayesian analyzes show ana posteriori favor for this local non-identification. We show that the...

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.<br>...

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 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...

Asset liability management (ALM) is an important and challenging problem for institutional investors and financial intermediaries. The requirement to fulfill its liablilities constrains the institutional investor in its asset allocation possiblilites. We formulate an ALM model for pension funds...

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....

In this paper we propose a Bayesian analysis of seasonal unit roots in quarterly observed time series. Seasonal unit root processes are useful to describe economic series with changing seasonal fluctuations. A natural alternative model for similar purposes contains deterministic seasonal mean...