MØLBA3007 Advanced statistical modelling

    • Number of credits
      7,5
    • Teaching semester
      2023 Autumn
    • Language of instruction
      English
    • Campus
      Lillehammer
    • Required prerequisite knowledge

      LMØS120 Management accounting and control (Økonomisk styring og kontroll) and LMSM130 Quantitative Method (Kvantitativ metode)

Course content
  • Multiple regression:  linear- and non-linear regression
  • Endogeneity issues and methods
  • Models for limited dependent variables
  • Models based on panel data
  • Partial least squares Structural Equation Modelling (PlsSEM)

Learning Outcome

Upon passing the course, students have achieved the following learning outcomes:

Knowledge

The student

  • has specialized knowledge about endogenous regressors, models with limited dependent variables, statistical models for panel data and how to model a set of equations with observed and unobserved variables (k1)
  • has advanced knowledge of the underlying assumptions of the selected statistical models covered in the course (k2)
  • can review and critically assess selected research problems in social sciences where application of advanced statistical methods is required (k3)
  • can interpret and explain research results based on the use of advanced statistical models (k4)
Skills

The student can

  • test and critically assess the reliability and validity of complex surveys (f1)
  • justify the choice of methodology based on critical reflection about model properties and the underlying assumptions (f2)
  • plan, design, and conduct advanced empirical studies (f3)
  • analyse data from a range of sources within business, economics, and finance using advanced statistical techniques (f4)
  • demonstrate professional reporting skills on the topics covered in the course through both oral presentations and written reports (f5)
General competence

The student can

  • complete a research project built on the use of advanced statistical models (g1)
  • debate findings from research using advanced statistical models with peers (g2)
Teaching and working methods
  • Lectures
  • Tutorial videos
  • Research-based teaching
  • Case studies
Required coursework
  • Mandatory homework assignments must be handed in before each teaching module (a total of 4). These will be combinations of practical and theoretical exercises covering key topics in the course.
  • Three out of four homework assignments must be passed to be allowed to take the exam.
  • Attendance on at least 50 % of the courses lectured teaching.
  • Oral presentation of the final written academic report (see below).
Form of assessment

Group semester project (two students per group). The students shall plan, prepare, and execute a research project using selected techniques covered in the course. The data collected shall be analysed and reported in a final written academic report.

Assessments
Form of assessmentGrading scaleGroupingDuration of assessmentSupport materialsProportionComment
Written assignment
ECTS - A-F
Group (2 - 2)
  • All
Faculty
Inland School of Business and Social Sciences
Department
Department of Business Administration