Introductory Econometrics for Business and Economics

Vrije Universiteit Amsterdam

Course Description

  • Course Name

    Introductory Econometrics for Business and Economics

  • Host University

    Vrije Universiteit Amsterdam

  • Location

    Amsterdam, The Netherlands

  • Area of Study


  • Language Level

    Taught In English

  • Course Level Recommendations


    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

  • Recommended U.S. Semester Credits
  • Recommended U.S. Quarter Units
  • Overview

    By the end of this course students will have had an introduction to modern econometric techniques, that will enable them to conduct an
    empirical study of their own. In particular, students will be familiar with econometric methods for cross-sectional and panel data, and with
    real-world applications in macroeconomics, finance and business.

    First, a review is given of least squares estimation and testing in the simple linear cross-sectional regression model. We discuss the classical assumptions, and the consequences arising when these assumptions are not fulfilled. The linear model with multiple regressors is discussed using matrix notation. Furthermore, we cover maximum likelihood estimation, and models that are nonlinear in variables. Finally, an introduction to panel data analysis is given.
    Throughout the course, the focus lies on developing an intuition for state-of-the-art econometric concepts. A balance is struck between
    theoretical derivations and empirical applications. Extensive use is made of the statistical software R, both for in-class illustration and for hands-on exercises. An introduction to R is provided in the tutorial of the first week.

    Lectures (4h per week) and tutorials (2h per week).

    Final written exam (85%) and practical assignment (15%)

    This course assumes familiarity with probabilistic concepts such as discrete and continuous random variables, conditional expectations,
    hypothesis testing and central limit theorems, with the basics of matrix calculus, and with the essentials of regression analysis. This material, excluding matrix calculus, corresponds more or less to chapters 1-5 in the book by Stock/Watson (see literature references), and students are recommended to refresh their memory prior to the first lecture.

Course Disclaimer

Courses and course hours of instruction are subject to change.

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