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 computers
- numerical differentiation
- numerical integration: quadrature and Monte Carlo integration
- interpolation methods
- finding zeros of functions: bisection, Newton(-Raphson), Secant
methods
- univariate optimization: golden section search.
- multivariate 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 models
- Power method for computing eigenvalues and eigenvectors.
- Monte Carlo simulation methods

Teaching Methods

Classes and computer practicals.

Method of Assessment

Intermediate exam – Individual assessment
Final exam – Individual assessment
Individual assignment - Groups of 1 or 2 students

Literature

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

Recommended background knowledge

Programming, Linear Algebra, Analysis II.