Statistics seminar: Confidence distributions for the autoregressive parameter

Participate on site or on Zoom (meeting ID: 699 250 9213)

Abstract: The notion of confidence distributions is applied to inference about the parameter in a simple autoregressive model, allowing the parameter to take the value one. This makes it possible to compare to asymptotic approximations in both the stationary and the non stationary cases at the same time. The main point, however, is to compare to a Bayesian analysis of the same problem. The similarity between the confidence and non-informative Bayesian frameworks is exploited. It is shown that, in the stationary case, asymptotically a full analogy is obtained for a flat prior. However, if a unit parameter is allowed, for the analogy to hold there has to be a spike at one of some size in the prior.
Simulation studies and two empirical examples illustrate the ideas. 

This is a seminar in our statistics seminar series.