Peter Davis & Eliana Garcés 
Quantitative Techniques for Competition and Antitrust Analysis [EPUB ebook] 

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This book combines practical guidance and theoretical background for analysts using empirical techniques in competition and antitrust investigations. Peter Davis and Eliana Garcés show how to integrate empirical methods, economic theory, and broad evidence about industry in order to provide high-quality, robust empirical work that is tailored to the nature and quality of data available and that can withstand expert and judicial scrutiny. Davis and Garcés describe the toolbox of empirical techniques currently available, explain how to establish the weight of pieces of empirical work, and make some new theoretical contributions.
The book consistently evaluates empirical techniques in light of the challenge faced by competition analysts and academics–to provide evidence that can stand up to the review of experts and judges. The book’s integrated approach will help analysts clarify the assumptions underlying pieces of empirical work, evaluate those assumptions in light of industry knowledge, and guide future work aimed at understanding whether the assumptions are valid. Throughout, Davis and Garcés work to expand the common ground between practitioners and academics.

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Mengenai Pengarang

Peter Davis is deputy chairman of the United Kingdom’s Competition Commission. He previously served on the faculties of MIT Sloan and the London School of Economics. He holds a Ph D in economics from Yale University.
Eliana Garcés is a member of the cabinet of the European Commissioner for Consumer Affairs. She was previously a member of the Chief Economist’s Team within the European Commission’s Directorate General for Competition. She holds a Ph D in economics from the University of California, Los Angeles.

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Bahasa Inggeris ● Format EPUB ● Halaman-halaman 592 ● ISBN 9781400831869 ● Saiz fail 12.6 MB ● Penerbit Princeton University Press ● Bandar raya Princeton ● Negara US ● Diterbitkan 2009 ● Muat turun 24 bulan ● Mata wang EUR ● ID 2365358 ● Salin perlindungan Adobe DRM
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