Empirical Methods

MET4 Empirical Methods

Autumn 2020

  • 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 behavioral 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. Chi-squared tests

    • Goodness-of-fit (more than two proportions)
    • Test for independence in a contingency table

    5. Regression analysis

    • Simple regression
    • Multiple regression: Modelling and residual analysis
    • Panel data
    • Categorical regression (logit and probit)

    6. Introduction to machine learning

    7. Time series

  • Learning outcome

    • Knowledge:
      • Explain the theoretical foundation of the statistical methods that are covered in the course.

     

    • Skills:
      • Substantiate the choice of a statistical method in a given realistic problem, and then apply the method.
      • Use software to make descriptive statistics using numerical and graphical methods.
      • Perform inference about one and two populations based on one or two samples.
      • Use linear regression to identify the linear dependence structure based on a set of explanatory variables and a response variable, for cross-sectional and panel data.
      • Distinguish between correlation and causality in empirical problems.
      • Interpret and precisely describe the result of a statistical analysis.
      • Know recent methods in developments in machine learning.
      • Trade bias against variance in order to optimize the predictive power of machine learning techniques.
      • Build an empirical model using variables and functional form in order to solve specific problems.
      • Identify basic time series models and make predictions

    • General competence:
      • Recognize empirical arguments in public discourse and in the news, and criticize the choice of methods, execution and interpretation.
      • Perform basic data analysis using modern computer tools.

  • Teaching

    Teaching consists of plenary 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.

  • Recommended prerequisites

    It is recommended that the students are in command of the contents of MET3.

  • Required prerequisites

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

  • Requirements for course approval

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

    Course approval from INT010 is valid for MET4. Students with course approval from INT010 are recommended to attend MET4 lectures and data labs prior to the exam.

  • Assessment

    The final grade is based on a written school exam (3 hours), which counts 70 %, and a take home exam (3 days, from 09:00 on day 1 until 14:00 on day 3), which counts 30 %. The take home exam is done in groups of 2-4 students.

    Examination questions are written in Norwegian and English both semesters and can be answered in both language in both semesters.

    The two exams elements can be taken independently, but it is recommended to take them within one term.

  • Grading Scale

    A-F for both assessment elements and the overall course grade.

  • Computer tools

    R

  • Literature

    Keller: Statistics for Management and Economics, 11th Edition (or earlier editions, including 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
Norwegian
Semester

Autumn and Spring. Offered Autumn 2020.

Please note: Due to the present corona situation, please expect parts of this course description to be changed before the autumn semester starts. Particularly, but not exclusively, this relates to teaching methods, mandatory requirements and assessment.

Course responsible

Associate Professor Håkon Otneim, Institutt for foretaksøkonomi

Associate Professor Geir Drage Berentsen, Institutt for foretaksøkonomi