Numerical Methods

URL study guide

https://studiegids.vu.nl/en/courses/2024-2025/E_EOR2_NUME

Course Objective

Acquainting the student with numerical methods and applications to econometric problems.

Course Content

Several methods will be discussed for solving numerical problems in econometrics. Topics include:floating point representation of numbers on computersnumerical differentiationnumerical integration: quadrature and Monte Carlo integrationinterpolation methodsfinding zeros of functions: bisection, Newton(-Raphson), Secant methodsunivariate optimization: golden section search.random number generation: inverse CDF, rejection samplingmultivariate optimization: Newton(-Raphson) and BFGS with linesearch, Nelder-Mead. Differential Evolution.optimization under restrictions using transformations.using optimization methods to compute Maximum Likelihood estimators in non-Gaussian/non-linear econometric modelsPower method for computing eigenvalues and eigenvectors.solving a system of linear equations: Gaussian Elimination

Teaching Methods

Classes and tutorials.

Method of Assessment

Intermediate exam – Individual assessment Final exam – Individual assessment Assignment
- Groups of 3-4 students

Literature

Cheney & Kincaid (2012), Numerical Mathematics and Computing. 7th edition.

Additional Information

Please note that this course is part of an entry requirement for Data Science Practical (part of BSc Econometrics and Data Science).

Recommended background knowledge

Programming, Linear Algebra, Analysis II, Probability Theory, Statistics