Learn advanced optimisation techniques and apply them to economic problems.
Understand the concept of integral and learn some integration techniques.
Learn how to solve some simple discrete as well as continuous dynamic systems and to analyse equilibrium points.
Learn advanced multiple regression techniques.
Learn some univariate as well as multivariate time series techniques.
Apply multiple regression and time series techniques to economic problems using statistical software.

The course QM3 is a continuation of the first year Economics courses QM1 and QM2 and is divided in a mathematics part and a statistics part. The mathematics part consists of three topics: Optimization, Integration, and Dynamic Models. Optimization considers unconstrained and constrained optimization of univariate and multivariate functions (Lagrange, Kuhn-Tucker). Integration covers elementary and advanced integration techniques for univariate functions (integration by parts, integration by substitution, improper integrals). Dynamic Models provides an introduction to discrete dynamics (difference equations) and continuous dynamics (differential equations) including equilibrium analysis. Each topic has a separate reader. The statistics part formally studies the regression model, including the problem of omitted variables bias, the testing of general linear parameter restrictions, and the large-sample (asymptotic) properties of regression. The focus is on the analysis of cross-section data; time-series data is briefly discussed at the end. Using the statistical package Eviews, empirical results are generated by yourself. This part is based on the following textbook. Jeffrey M. Wooldridge (2018). Introductory Econometrics: a modern approach. 7th international student edition, Thomson South-Western, Cengage Learning.

Jeffrey M. Wooldridge (2018). Introductory Econometrics: a modern approach. 7th international student edition, Thomson South-Western, Cengage Learning.

The courses Quantitative Methods I (EBC1005/1006/1007) and Quantitative Methods II (EBC 1033/1034/1035), taught at the University of Maastricht. In particular the following subjects should have been mastered:
Mathematics: exponential and logarithmic functions, (partial) derivative and rules a.o. chain rule, optimisation of functions of one and two variables, Lagrange.
Statistics: random variable, probability distributions, confidence interval, hypothesis testing, linear regression.
An advanced level of English.