Rescuing Econometrics
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Rescuing Econometrics
Haavelmo’s 1944 monograph, The Probability Approach in Econometrics, is widely acclaimed as the manifesto of econometrics. This book challenges Haavelmo’s probability approach, shows how its use is delivering defective and inefficient results, and argues for a paradigm shift in econometrics towards a full embrace of machine learning, with its attendant benefits. Machine learning has only come into existence over recent decades, whereas the universally accepted and current form of econometrics has developed over the past century. A comparison between the two is, however, striking. The practical achievements of machine learning significantly outshine those of econometrics, confirming the presence of widespread inefficiencies in current econometric research. The relative efficiency of machine learning is based on its theoretical foundation, and particularly on the notion of Probably Approximately Correct (PAC) learning. Careful examination reveals that PAC learning theory delivers the goals of applied economic modelling research far better than Haavelmo’s probability approach. Econometrics should therefore renounce its outdated foundation, and rebuild itself upon PAC learning theory so as to unleash its pent-up research potential. The book is catered for applied economists, econometricians, economists specialising in the history and methodology of economics, advanced students, philosophers of social sciences.
Rescuing Econometrics
Haavelmo’s 1944 monograph, The Probability Approach in Econometrics, is widely acclaimed as the manifesto of econometrics. This book challenges Haavelmo’s probability approach, shows how its use is delivering defective and inefficient results, and argues for a paradigm shift in econometrics towards a full embrace of machine learning, with its attendant benefits. Machine learning has only come into existence over recent decades, whereas the universally accepted and current form of econometrics has developed over the past century. A comparison between the two is, however, striking. The practical achievements of machine learning significantly outshine those of econometrics, confirming the presence of widespread inefficiencies in current econometric research. The relative efficiency of machine learning is based on its theoretical foundation, and particularly on the notion of Probably Approximately Correct (PAC) learning. Careful examination reveals that PAC learning theory delivers the goals of applied economic modelling research far better than Haavelmo’s probability approach. Econometrics should therefore renounce its outdated foundation, and rebuild itself upon PAC learning theory so as to unleash its pent-up research potential. The book is catered for applied economists, econometricians, economists specialising in the history and methodology of economics, advanced students, philosophers of social sciences.
Karl Popper and Situational Analysis
Author: Alfonso Palacio-Vera
language: en
Publisher: Taylor & Francis
Release Date: 2024-08-02
‘Situational Analysis’ represents Karl Popper’s methodological proposal for the social sciences. Although it has been widely studied and interpreted, this book argues that Situational Analysis remains underestimated and undeveloped and, if properly reformulated, could yet become a broad methodological framework that encompasses both the ‘historical’ social sciences and a large section of the ‘theoretical’ social sciences. The first part of this book develops this idea by returning to Popper’s texts. This book calls for both a reformulation of Situational Analysis and a clarification of the status and role of Popper’s ‘Rationality Principle’. This book also develops Situational Analysis by addressing two aspects of the Problem-Situation facing individual actors that are only sketched out in Popper’s work: institutions and expectations. The second part of this book utilizes the reformulated version of Situational Analysis to shed light on the core methodological differences across some of the most widespread approaches in contemporary economics and sociology. In particular, by showing that many of these approaches are variants of the reformulated version of Situational Analysis presented in the first part of this book, this book argues that apparent methodological differences across these approaches may be reinterpreted as differences in the way social scientists reconstruct the Problem-Situation facing individual actors. This book will be vital reading for academic economists, sociologists, philosophers of science, and other social scientists interested in methodology.