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Handbook of Econometrics Durlauf Hansen Heckman Matzkin Hardback Volume 7A

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Handbook of Econometrics

This comprehensive presentation of advances in econometrics features top scholars summarizing literature in their fields of expertise

Steven Durlauf (Volume editor), Lars Peter Hansen (Volume editor), James J. Heckman (Volume editor), Rosa Liliana Matzkin (Volume editor)

9780444636492

Hardback, published 30 November 2020

592 pages

23.5 x 19 x 3.3 cm, 1.68 kg

Handbook of Econometrics, Volume 7A, examines recent advances in foundational issues and \""hot\"" topics within econometrics, such as inference for moment inequalities and estimation of high dimensional models. With its world-class editors and contributors, it succeeds in unifying leading studies of economic models, mathematical statistics and economic data. Our flourishing ability to address empirical problems in economics by using economic theory and statistical methods has driven the field of econometrics to unimaginable places. By designing methods of inference from data based on mod]

Handbook of Econometrics Durlauf Hansen Heckman Matzkin Hardback Volume 7A

Handbook of Econometrics

This comprehensive presentation of advances in econometrics features top scholars summarizing literature in their fields of expertise

Steven Durlauf (Volume editor), Lars Peter Hansen (Volume editor), James J. Heckman (Volume editor), Rosa Liliana Matzkin (Volume editor)

9780444636492

Hardback, published 30 November 2020

592 pages

23.5 x 19 x 3.3 cm, 1.68 kg

Handbook of Econometrics, Volume 7A, examines recent advances in foundational issues and \""hot\"" topics within econometrics, such as inference for moment inequalities and estimation of high dimensional models. With its world-class editors and contributors, it succeeds in unifying leading studies of economic models, mathematical statistics and economic data. Our flourishing ability to address empirical problems in economics by using economic theory and statistical methods has driven the field of econometrics to unimaginable places. By designing methods of inference from data based on mod]

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