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Cowles Foundation Discussion Papers

1991

Bayesian analysis

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Comment On ‘To Criticize The Critics’, By Peter C.B. Phillips, Christopher A. Sims Jul 1991

Comment On ‘To Criticize The Critics’, By Peter C.B. Phillips, Christopher A. Sims

Cowles Foundation Discussion Papers

In his paper “To Criticize the Critics” (1991), Peter Phillips discusses Bayesian methodology for time series models. The main point that Uhlig and I set out to make, however, was that careful consideration of the implications of the likelihood principle suggests that much of the recent work under the “unit root” label in the econometrics literature is being incorrectly interpreted in practice. We pointed out that time series models with possible unit roots are one of the few domains within which the implications of a likelihood principle approach to inference are difference, even in the large samples, from those of …


Bayesian Routes And Unit Roots: De Rebus Prioribus Semper Est Disputandum, Peter C.B. Phillips Jul 1991

Bayesian Routes And Unit Roots: De Rebus Prioribus Semper Est Disputandum, Peter C.B. Phillips

Cowles Foundation Discussion Papers

This paper provides detailed responses to the following 8 discussants of my paper “To Criticize the Critics: An Objective Bayesian Analysis of Stochastic Trends”: Gary Koop and Mark Steel; Edward Leamer; In-Moo Kim and G. S. Maddala Dale J. Poirier; Peter C. Schotman and Herman K. van Dijk; James H. Stock; David Dejong and Charles H. Whiteman; and Christopher Sims. This reply puts new emphasis on the call made in the earlier paper for objective Bayesian analysis in time series; it underlines the need for a new approach, especially with regard to posterior odds testing; and it draws attention to …


Time Series Modelling With A Bayesian Frame Of Reference: I. Concepts And Illustrations, Peter C.B. Phillips, Werner Ploberger May 1991

Time Series Modelling With A Bayesian Frame Of Reference: I. Concepts And Illustrations, Peter C.B. Phillips, Werner Ploberger

Cowles Foundation Discussion Papers

This paper offers a general approach to time series modeling that attempts to reconcile classical and methods. The central idea put forward to achieve reconciliation is that the Bayesian approach relies implicitly a frame of reference for the data generating mechanism that is quite different from the one that is employed in the classical approach. Differences in inferences from the two approaches are therefore to be expected unless the altered frame reference is taken into account. We show that the new frame of reference in Bayesian inference is a consequence of a change of measure that arises naturally in the …