Pollin and Ash, being good Post-Keynesian economists who have likely read Keynes’ critiques of econometric and statistical studies, recognise well that simply throwing decades of heterogeneous historical data into a statistical blender and then proclaiming the mush that results as some sort of Grand Truth for the absurdity that it is. They are well aware that when it comes to history context is key; a severe one-year recession in New Zealand in the late-1950s is such a different historical constellation than demobilisation in the US after WWII that to compare them in simple statistical terms is patently absurd.
The irritating thing is that most economists and media commentators will admit this as being true, yet they will continue to engage with such nonsense regardless. After all, it is so much easier to comment on single numbers – however mysteriously arrived at – than it is to discuss complex historical constellations....
Keynes compared such statistical manipulation to black magic and alchemy in that he recognised what a powerful influence the idea of objective interpretations of manifestly subjective and context-dependent data could have over the minds of men. Today we would do well to keep this in mind as the dust settles on the Reinhart-Rogoff controversy and the whole edifice of the economic priesthood and its pseudo-scientific methods remain intact.Naked Capitalism
Defrocking Reinhart and Rogoff – Controversy Ignores Fundamental Issues in the Use and Abuse of Statistical Studies
Philip Pilkington, research assistant at Kingston University in London
I've been considering related issues for some time now; this may be of interest to some-
ReplyDeleteWhy Inferential Statistics are Inappropriate for Development Studies and How the Same Data Can be Better Used
Abstract:
The purpose of this paper is twofold:
1) to highlight the widely ignored but fundamental problem of 'super-populations' for the use of inferential statistics in development studies. We do not to dwell on this problem however as it has been sufficiently discussed in older papers by statisticians that social scientists have nevertheless long chosen to ignore; the interested reader can turn to those for greater detail.
2)to show that descriptive statistics both avoid the problem of super-populations and can be a powerful tool when used correctly. A few examples are provided.
The paper ends with considerations of some reasons we think are behind the adherence to methods that are known to be inapplicable to many of the types of questions asked in development studies yet still widely practiced.
Why Inferential Statistics are Inappropriate for Development Studies and How the Same Data Can be Better Used , Clint Ballinger