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 a classical hypothesis testing approach. Phillips addresses this part of our paper only indirectly
This paper starts with a brief description of the introduction of the likelihood approach in econome...
We employ a Bayesian perspective to identify the type of prior needed to support the inference that ...
INTRODUCTION: The point of departure for our paper is that most modern statistical models are built ...
In his paper “To Criticize the Critics” (1991), Peter Phillips discusses Bayesian methodology for ti...
This paper provides detailed responses to the following 8 discussants of my paper “To Criticize the ...
textabstractThis paper is a comment on P. C. B. Phillips, `To criticise the critics: an objective Ba...
(i) Statistical inference after Neyman-Pearson. Statistical inference as an alternative to Neyman-Pe...
This paper examines several grounds for doubting the value of much of the special attention recently...
For a Bayesian approach to be useful, the priors and posteriors must be carefully interpreted to gua...
Professor Zellner has greatly contributed to econometrics in many aspects. This paper c...
This note is a discussion of the paper “Confidence distribution ” by Min-ge Xie and Kesar Singh, to ...
Our original article provided a relatively detailed summary of Harold Jeffreys’s philosophy on stati...
This paper starts with a brief description of the introduction of the likelihood approach in econome...
Some researchers, for example, Koop (1992), and Sims (1988), advocated for Bayesian alternatives to ...
This paper starts with a brief description of the introduction of the likelihood approach in econome...
This paper starts with a brief description of the introduction of the likelihood approach in econome...
We employ a Bayesian perspective to identify the type of prior needed to support the inference that ...
INTRODUCTION: The point of departure for our paper is that most modern statistical models are built ...
In his paper “To Criticize the Critics” (1991), Peter Phillips discusses Bayesian methodology for ti...
This paper provides detailed responses to the following 8 discussants of my paper “To Criticize the ...
textabstractThis paper is a comment on P. C. B. Phillips, `To criticise the critics: an objective Ba...
(i) Statistical inference after Neyman-Pearson. Statistical inference as an alternative to Neyman-Pe...
This paper examines several grounds for doubting the value of much of the special attention recently...
For a Bayesian approach to be useful, the priors and posteriors must be carefully interpreted to gua...
Professor Zellner has greatly contributed to econometrics in many aspects. This paper c...
This note is a discussion of the paper “Confidence distribution ” by Min-ge Xie and Kesar Singh, to ...
Our original article provided a relatively detailed summary of Harold Jeffreys’s philosophy on stati...
This paper starts with a brief description of the introduction of the likelihood approach in econome...
Some researchers, for example, Koop (1992), and Sims (1988), advocated for Bayesian alternatives to ...
This paper starts with a brief description of the introduction of the likelihood approach in econome...
This paper starts with a brief description of the introduction of the likelihood approach in econome...
We employ a Bayesian perspective to identify the type of prior needed to support the inference that ...
INTRODUCTION: The point of departure for our paper is that most modern statistical models are built ...