Master's Math Camp

Syllabus

Lecture Notes ( Based on Dowling and Simon & Blume)

• Review (Basic Math Concepts)

• Lecture 1 (Linear Algebra I)

• Lecture 2 (Differentiation)

• Lecture 3 (Multivariable Calculus)

• Lecture 4 (Linear Algebra II)

• Lecture 5 (Optimization)

• Lecture 6-7 (Probability )

• Lecture 8 (Statistical Inference)

• Lecture 9 (Introduction to Econometrics: OLS) from Principles of Econometrics 4th Ed. by R.Carter Hill , William E. Griffiths & C. Lim

• Lecture 10 (Intro to Programming: Stata)

  • Lecture Materials (zip type file)

Further Programming Resources:

• Optional Alternative Lecture ( Intro to Programming: Stata ) by Oscar Torres-Reyna at Princeton University

  • Data (From Stock and Watson’s Introduction to Econometrics, 3rd Ed.)

  • Do - File

  • Stata Resources:

    • Stata Website

    • Stata YouTube channel

    • UCLA Institute for Digital Research and Education

    • Stata Cheat Sheet

    • Applied Econometrics using Stata Perez & Truglia

    • Intro to Stata Christopher Baum

The purpose of this course is to prepare students for the mathematical rigor of the M.A. in Applied Economics and M.S. in Economics programs. The topics covered will include a review of basic concepts from pre-calculus, linear algebra, differentiation, multivariate calculus, integral calculus, optimization methods, probability theory and statistical inference. Particular emphasis will be placed on the application of these mathematical methods to topics in economics.

Problem Sets

• PS Review - Solutions

• PS 1 Sol Code: R Matlab (Ex8) - Python (Ex8)

• PS 2 - Solutions

• PS 3 - Solutions

• PS 4 - Solutions

• PS 5 Solutions

• PS 6 Solutions

• PS 7 Solutions

• PS 8 Solutions