000			02169nam a22002177a 4500
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020			_a9781032586052
040			_aMAIN
082			_a330.01 _bQIN-24
100			_aQin, Duo
245			_aRescuing econometrics : from the probability approach to probably approximately correct learning / Duo Qin.
250			_a1st ed.
260			_aLondon ; New York : _bRoutledge Taylor & Francis Group, _c2024.
300			_axii, 100 pages ; 24 cm.
440			_aRoutledge/inem advances in economic methodology
500			_aSummary "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 Econometrics should therefore renounce its outdated foundation, and rebuild itself upon PAC learning theory so as to unleash its pent-up research potential.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"-- Provided by publisher.
500			_aIncludes bibliographical references and index.
650			_aEconometrics.
942			_cBK
999			_c22382 _d22382