Mathematics and Programming for the Quantitative Economist: Fundamentals

This book covers calculus, linear algebra, probability and statistics, and computer programming (using Python) at the level required in strong, technically-oriented undergraduate and masters level economics programs. It is the first of a two-volume set, with the second volume focusing on mathematics and programming for networks and dynamic models. Together, the two volumes cover mathematics and programming used in economics, econometrics, data science, machine learning and artificial intelligence. It is intended to be used as a main text for mathematics for economics courses ranging from intermediate to masters levels, and as a resource or reference text for other economics, econometrics and data science courses. It should also be useful as a reference text for economics and data science practitioners who wish to learn more about the mathematics of these subjects.

Anthony Tay is Associate Professor of Economics at the Singapore Management University. He holds a PhD in Economics from the University of Pennsylvania. His research areas are in economic forecasting methodology, empirical macroeconomics and empirical finance.

Ismail Baydur is Assistant Professor of Economics at the Singapore Management University. He obtained his PhD in Economics from the University of Virginia. His research areas are in macroeconomics and labor economics.

Daniel Preve is Associate Professor of Economics (Education) at the Singapore Management University. He holds a PhD in Statistics from Uppsala University. His research areas are in econometric theory and financial econometrics. He is the director of the Master of Data Science in Economics program and the undergraduate Data Science and Analytics 2nd Major at the School of Economics, Singapore Management University.