Computational Econometrics

Vrije Universiteit Amsterdam

Course Description

  • Course Name

    Computational Econometrics

  • Host University

    Vrije Universiteit Amsterdam

  • Location

    Amsterdam, The Netherlands

  • Area of Study

    Economics

  • Language Level

    Taught In English

  • Course Level Recommendations

    Upper

    ISA offers course level recommendations in an effort to facilitate the determination of course levels by credential evaluators.We advice each institution to have their own credentials evaluator make the final decision regrading course levels.

    Hours & Credits

  • ECTS Credits

    6
  • Recommended U.S. Semester Credits
    3
  • Recommended U.S. Quarter Units
    4
  • Overview

    Period 1

    COURSE OBJECTIVE
    This course in the minor Applied Econometrics is targeted at Bachelor Econometrics students and Bachelor students with different backgrounds who have already had an introduction to programming and econometrics/statistics. The objective is to acquaint the student with Bayesian statistics and applications thereof to econometric problems, using advanced computational methods.

    COURSE CONTENT
    This course will cover Bayesian statistics where the topics include the prior and posterior density, Bayesian hypothesis testing, Bayesian prediction, and Bayesian Model Averaging for forecast combination. Several models will be considered, including the Bernoulli/binomial distribution, the Poisson distribution and the normal distribution. Obviously, attention will be paid to the Bayesian analysis of linear regression models. Also simple time series models will be considered. An important part of the courses is the treatment of simulation-based methods such as Markov chain Monte Carlo (Gibbs sampling, data augmentation, Metropolis-Hastings method) and Importance Sampling, that are often needed to compute Bayesian estimates and predictions and to perform Bayesian tests.

    TEACHING METHODS
    Lectures and exercises in the computer lab.

    TYPE OF ASSESSMENT
    Final written exam – Individual assessment. Exercises - groups of 1 or 2 students.

    RECOMMENDED BACKGROUND KNOWLEDGE
    (i) Introductory courses in Econometrics and Statistics; (ii) Basic programming skills: some familiarity with one of [Python, MATLAB, Ox, R]

Course Disclaimer

Courses and course hours of instruction are subject to change.

Some courses may require additional fees.

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