000			02187 a2200205 4500
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020			_a9781032586052
082			_a330.015195 _bQIN
100			_aQin, Duo _921297
245			_aRescuing econometrics: _bfrom the probability approach to probably approximately correct learning
260			_bRoutledge _aLondon _c2024
300			_axii, 100 p.
365			_aGBP _b145.00
490			_aRoutledge INEM Advances in Economic Methodology
520			_aHaavelmo’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 Econometrics should therefore renounce its outdated foundation, and rebuild itself upon PAC learning theory so as to unleash its pent-up research potential.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. (https://www.routledge.com/Rescuing-Econometrics-From-the-Probability-Approach-to-Probably-Approximately-Correct-Learning/Qin/p/book/9781032586052)
650			_aEconomics
650			_aEconometrics
942			_cBK _2ddc
999			_c8987 _d8987