BØK730 Econometrics (Autumn 2023)

About the course

This course provides a broad overview of statistical methods for analyzing single equation econometrics models. Apart from presenting the theory behind the models, the course emphasizes a hands-on approach to econometric analysis, from preparation of the data to estimation and post-estimation. We loosely follow the chapters in Introduction to Econometrics, 4th ed. by Stock and Watson. As this book is intended for both undergraduate and graduate level studies, some introductory content will be omitted as it is assumed that participants have knowledge from previous statistics and econometrics classes. The lectures will cover the following topics:

• Review of Simple Regression

• Review of Multiple Regression

• Multiple Regression in Stata: Data Preparation and Estimation

• Violations of the Classical Assumptions: Heteroscedasticity

• Violations of the Classical Assumptions: Multicollinearity

• Violations of the Classical Assumptions: Serial Correlation

• Instrumental Variables Regression

• Experiments and Quasi-Experiments

• Panel Data Models

• Binary Dependent Variable Models

• Marginal effects in binary dependent variable models

• Time Series Models

The course is connected to the following study programs

• Master of Science in Economics and Business Administration
• Exchange programme - Master's level

Recommended requirements

Background knowledge in statistics 

The student's learning outcomes after completing the course

Knowledge

After completing the course, students should have skills and knowledge in statistical methodology constituting a base from which they can successfully carry out solid empirical work, e.g., in their master thesis or later in their academic or professional careers. Specifically, the students should be able to:

• confidently perform estimation and testing of hypotheses about main population parameters such as means, proportions, variances

• specify and estimate linear regression models, using appropriate theory and sample data

• identify and handle nonlinear effects in regression models using transformations and dummy variables

• identify and handle heteroscedasticity, multicollinearity and autocorrelation in regression data

• employ binary response and limited dependent variable models in cases where the outcome is not continuous

• work with basic time series models and do forecasting with moving averages, exponential smoothing

• use panel data methods for continuous, binary and ordered responses

• interpret the result of statistical analyses and explain the results in nontechnical language

Skills
It is expected that the students will:

• be able to apply modern computer programs in regression analyses of empirical data, including statistical testing to investigate whether the classical assumptions in regression analysis are satisfied

• be a qualified user of basic econometric methods

• perform be a critical reader of the literature concerning empirical analyses

• be able to perform statistical analysis using the statistical package Stata

Competence
Furthermore, the students should:

• be able to read and understand project reports and journal articles that make use of the concepts and methods that are introduced in the course

• be able to make use of econometric models in their own academic work, for example in analyses needed for their master’s thesis

 

Forms of teaching and learning

Classroom lectures and computer lab sessions. Double two-hour sessions per week.  

Coursework requirements - conditions for taking the exam

• Obligatorisk arbeidskrav: Oppgave(r)

• Antall arbeidskrav: 2

• Påkrevde arbeidskrav: 2

• Fremmøte: Ikke påkrevd

• Kommentar: There will be two mandatory assignments. Both need to be passed to take the final exam.

Examination

• Vurderingsform: Digital skoleeksamen - Inspera

• Andel: 100%

• Varighet: 4 timer

• Gruppering: Individuell

• Karakterskala: Bokstavkarakter (A - F)

• Hjelpemidler:

Syllabus

Pensumoversikt

• BØK730 Econometrics