MET4 Empirical Methods

• 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 interactive sessions and lectures given at campus. Most of the curriculum will be supported by online based modules containing short videos, exercises and notes.  The students will work with data labs containing 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 have acquired knowledge equivalent to the contents of MET2 and MET3.

• Required prerequisites

  None.

• Credit reduction due to overlap

  None.

• Compulsory Activity

  Approved hand-in assignment.

  Previously approved compulsory activity (work requirement) remain valid.

• Assessment

  6-hours digital school exam with access to R.

• Grading Scale

  A-F

• Computer tools

  R

• Literature

  Keller: Statistics for Management and Economics, 2th Edition, Cengage

  Lecture notes.

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

• Permitted Support Material

  Calculator 

  One bilingual dictionary (Category I) 

  All in accordance with Supplementary provisions to the Regulations for Full-time Study Programmes at the Norwegian School of Economics Ch.4 Permitted support material https://www.nhh.no/en/for-students/regulations/https://www.nhh.no/en/for-students/regulations/ and https://www.nhh.no/en/for-students/examinations/examination-support-materials/https://www.nhh.no/en/for-students/examinations/examination-support-materials/