The rise and fall of logical positivism is the most spectacular philosophical story of the twentieth century. Rising to prominence in the second quarter of the twentieth century, it swept away all contenders, and became widely accepted throughout the academia. Logical positivism provided a particular understanding of the nature of knowledge, as well as that of science and of scientific methodology. The foundations of the social sciences were re-formulated in the light of this new understanding of what science is. Later on, it became clear that the central tenets of the positivist philosophy were wrong. Logical positivism had a “spectacular crash,” and there was some dispute about who had “killed” logical positivism1. As a logical consequence, it became necessary to re-examine the foundations of the social science, and to find new bases on which to re-construct them. This has occurred to differing degrees in different disciplines. One of the most recalcitrant has been economics. As discussed in Zaman (2011), the foundations of economics continue to be based on erroneous logical positivist ideas, and hence require radical revisions.
It is not our intention in this paper to describe the whole story, which would be lengthy as well as complex and contentious. We will confine our attention to tracing the harmful impact of a limited number of positivist ideas on how econometrics has developed. We will argue that these ideas have led to a wrong methodology being accepted and used in econometrics. This wrong methodology has prevented progress and advance in knowledge. As evidence for this lack of progress, in a talk on the 100th anniversary of the first published regression by Yule, Freedman (1997:113) writes: “For nearly a century, investigators in the social sciences have used regression models to deduce cause-and-effect relationships from patterns of association. … . In my view, this enterprise has not been successful.”
This paper is structured as follows. Section two below provides a summary of the key positivist ideas, and how they lead to a distorted understanding of science. The third section provides several case studies showing how positivist methodologies lead us to consider the wrong questions. The fourth section shows the contrast and opposition between positivist and realist methodologies. The fifth section sketches some possible alternative methods which could be used to avoid these mistakes and make progress. The sixth section summarized the conclusions.
We will also offer a very tentative and preliminary sketch of alternative approaches that could be more successful.
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