Essential Mathematics for Economics

Essential Mathematics for Economics
Author: by Alexis Akira Toda (Author)
Publisher finelybook 出版社: Chapman and Hall/CRC
Edition 版次: 1st
Publication Date 出版日期: 2024-10-08
Language 语言: English
Print Length 页数: 286 pages
ISBN-10: 1032698942
ISBN-13: 9781032698946


Book Description
By finelybook

Essential Mathematics for Economics covers mathematical topics that are essential for economic analysis in a concise but rigorous fashion. The book covers selected topics such as linear algebra, real analysis, convex analysis, constrained optimization, dynamic programming, and numerical analysis in a single volume. The book is entirely self-contained, and almost all propositions are proved.

Features

  • Replete with exercises and illuminating examples
  • Suitable as a primary text for an advanced undergraduate or postgraduate course on mathematics for economics
  • Basic linear algebra and real analysis are the only prerequisites.
  • Supplementary materials such as Matlab codes, teaching slides etc. are posted on the book website https://github.com/alexisakira/EME.

About the Author

Alexis Akira Toda was born in Montreal, Canada to a Japanese father and a French-Canadian mother and moved to Japan at the age of four. He received a B.A. in medicine from University of Tokyo in 2004 and then an M.A. in economics in 2008 while practicing anesthesia. He moved to the United States in 2008 and obtained a Ph.D. in economics from Yale University in 2013. After teaching mathematics for economics, mathematical economics, and finance at UC San Diego for eleven years, he moved to Emory University in 2024, where he is a professor of economics. He is the author of more than 40 research articles published in economics, mathematics, physics, and medical journals on a wide variety of topics including general equilibrium, macro-finance, consumption and savings, income and wealth distributions, asset price bubbles, power laws, dynamic programming, econometrics, and numerical methods, among others. He can be reached at https://alexisakira.github.io.

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