Rescuing econometrics : from the probability approach to probably approximately correct learning / Duo Qin. (Record no. 22382)

MARC details
000 -LEADER
fixed length control field 02169nam a22002177a 4500
005 - DATE & TIME
control field 20250508112316.0
008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION
fixed length control field 250508b |||||||| |||| 00| 0 eng d
020 ## - ISBN
International Standard Book Number 9781032586052
040 ## - CATALOGING SOURCE
Original cataloging agency Indian Institute of Management Raipur
082 ## - DDC NUMBER
Classification number 330.01
Book Number QIN-24
100 ## - MAIN ENTRY--PERSONAL NAME
Personal name Qin, Duo
245 ## - TITLE STATEMENT
Title Rescuing econometrics : from the probability approach to probably approximately correct learning / Duo Qin.
250 ## - EDITION STATEMENT
Edition statement 1st ed.
260 ## - PUBLICATION, DISTRIBUTION, ETC. (IMPRINT)
Place of publication, distribution, etc London ; New York :
Name of publisher, distributor, etc Routledge Taylor & Francis Group,
Date of publication, distribution, etc 2024.
300 ## - PHYSICAL DESCRIPTION
Pages xii, 100 pages ; 24 cm.
440 ## - Series Statement
Series Title Routledge/inem advances in economic methodology
500 ## - GENERAL NOTE
General note Summary<br/>"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 Econometrics should therefore renounce its outdated foundation, and rebuild itself upon PAC learning theory so as to unleash its pent-up research potential.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 ## - GENERAL NOTE
General note Includes bibliographical references and index.
650 ## - Subject
Subject Econometrics.
942 ## - ADDED ENTRY ELEMENTS (KOHA)
Koha item type Books
Holdings
Withdrawn status Lost status Source of classification or shelving scheme Damaged status Not for loan Department Location (home branch) Sublocation or collection (holding branch) Shelving location Date acquired Vendor Name Discount Koha issues (times borrowed) Koha full call number Accession No. Koha date last seen Koha item type Price effective from
    Dewey Decimal Classification   Not for loan Reference Indian Institute of Management Raipur Indian Institute of Management Raipur Reference 26/04/2025 Overseas Press 35%   330.01 QIN-24 13349 08/05/2025 Books 08/05/2025