Empirical Methods

MET4 Empirical Methods

  • Topics

    Topics

    This course builds on and extends the methodology and key subjects from the first year. Students will be trained in the use of empirical analysis for decision making. Special emphasis is given to interpretation of economic and behavioural data. Students will learn to distinguish random variation from systematic variation and causality from correlation. Methodological issues are integrated with other economic subjects through examples and specific applications. While the main focus in the first year course in statistics is univariate analyses, this course also covers multivariate methods.

     

    The following topics are covered:

    1. Introduction to scientific methods in social sciences
      • Methodology
      • Qualitative vs quantitative analysis
      • Research ethics
    2. Descriptive statistics
      • Population and sample
      • Types of data and information
      • Central location, variance and co-variance
    3. Comparing two populations
      • Sampling distributions
      • Comparing two means
      • Comparing two variances
      • Comparing two proportions
    4. Comparing more than two groups
      • One-way analysis of variance
      • Two-way analysis of variance
    5. Chi-squared tests
      • Goodness-of-fit (more than two proportions)
      • Test for independence in a contingency table
    6. Non-parametric tests
    7. Regression analysis
      • Simple regression
      • Multiple regression: Modelling and residual analysis
      • Panel data
      • Categorical regression (logit and probit)
    8. Time series analyses and prognoses

  • Learning outcome

    Learning outcome

    Having completed the course, students will

    - have an understanding of statistical methods used in economics and business administration

    - be able to perform simple analytical calculations and empirical analysis of data they may encounter in graduate studies or business life

    - have knowledge of statistical software, and experience with project work and reporting

  • Teaching

    Teaching

    Teaching consists of lectures and data labs. In the data labs students will work with exercises and cases from the textbook. Student will have to hand in an assignment to document competence in the use of statistical software and reporting of results.

  • Required prerequisites

    Required prerequisites

    It is assumed that the students are in command of the contents of MET2 and MET3.

  • Requirements for course approval

    Requirements for course approval

    Course approval is given when the hand-in assignment is accepted (approved/not approved).

  • Assessment

    Assessment

    The final grade is based on a written school exam (3 hours) which counts 70 % and a take home exam (3 days) which counts 30 %. The take home exam is done in groups of 2-4 students.

    The two exams can be taken independently, but it is recommended to take them within one term. Both exams must be passed to pass the course MET4. Retake of one or both exams is possible.

  • Grading Scale

    Grading Scale

    Grading scale A-F

  • Computer tools

    Computer tools

    Excel and Gretl

  • Semester

    Semester

    Autumn (English) and spring (Norwegian)

  • Literature

    Literature

    Keller: Statistics for Management and Economics, 10th Edition or Managerial Statistics, 8th or 9th Edition. Cengage.

    Lecture notes

    Parts of the curriculum are also covered in Jan Ubøe, Statistikk for økonomifag, which is used in MET2.

Overview

ECTS Credits
7.5
Teaching language
English (Autumn) and Norwegian (Spring)
Semester
Spring, Autumn

Course responsible

Evelina Gavrilova-Zoutman, Department of Business and Management Science (Autumn), Håkon Otneim, Institutt for foretaksøkonomi (spring)