Rescuing econometrics: (Record no. 8987)

MARC details
000 -LEADER
fixed length control field 02187 a2200205 4500
005 - DATE AND TIME OF LATEST TRANSACTION
control field 20250323121711.0
008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION
fixed length control field 250323b |||||||| |||| 00| 0 eng d
020 ## - INTERNATIONAL STANDARD BOOK NUMBER
International Standard Book Number 9781032586052
082 ## - DEWEY DECIMAL CLASSIFICATION NUMBER
Classification number 330.015195
Item number QIN
100 ## - MAIN ENTRY--PERSONAL NAME
Personal name Qin, Duo
245 ## - TITLE STATEMENT
Title Rescuing econometrics:
Remainder of title from the probability approach to probably approximately correct learning
260 ## - PUBLICATION, DISTRIBUTION, ETC. (IMPRINT)
Name of publisher, distributor, etc. Routledge
Place of publication, distribution, etc. London
Date of publication, distribution, etc. 2024
300 ## - PHYSICAL DESCRIPTION
Extent xii, 100 p.
365 ## - TRADE PRICE
Price type code GBP
Price amount 145.00
490 ## - SERIES STATEMENT
Series statement Routledge INEM Advances in Economic Methodology
520 ## - SUMMARY, ETC.
Summary, etc. 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.<br/><br/>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 Econometrics should therefore renounce its outdated foundation, and rebuild itself upon PAC learning theory so as to unleash its pent-up research potential.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.<br/><br/>(https://www.routledge.com/Rescuing-Econometrics-From-the-Probability-Approach-to-Probably-Approximately-Correct-Learning/Qin/p/book/9781032586052)
650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name as entry element Economics
650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name as entry element Econometrics
942 ## - ADDED ENTRY ELEMENTS (KOHA)
Koha item type Book
Source of classification or shelving scheme Dewey Decimal Classification
Holdings
Withdrawn status Lost status Source of classification or shelving scheme Damaged status Not for loan Collection code Bill No Bill Date Home library Current library Shelving location Date acquired Source of acquisition Cost, normal purchase price Total Checkouts Full call number Accession Number Date last seen Copy number Cost, replacement price Price effective from Koha item type
    Dewey Decimal Classification     Public Policy & General Management IN32229 08-03-2025 Indian Institute of Management LRC Indian Institute of Management LRC General Stacks 03/18/2025 Overseas Press India Private 10565.43   330.015195 QIN 007827 03/18/2025 1 16254.50 03/18/2025 Book

©2025-2026 Pragyata: Learning Resource Centre. All Rights Reserved.
Indian Institute of Management Bodh Gaya
Uruvela, Prabandh Vihar, Bodh Gaya
Gaya, 824234, Bihar, India

Powered by Koha