Entry requirements

30 credits in statistics

Learning outcomes

A student that has completed the course should

* have deeper knowledge of statistical theory and methodology particularly in economical and social science applications

* be able to estimate models for cross-sectional data and time series data

be able to interpret the results of an implemented model adaptation

* be aware of limitations and common sources of errors in the analysis

* have ability to present results in oral and written form.

Content

General about econometric models and their application within economic planning. Linear-regression models with one or several explanatory variables. Nonlinear models. Estimation and hypothesis testing. Gauss-Markovs Nonlinear theorem. Heteroscedasticity and autocorrelation. Multicollinearity. Measurement errors. Instrumental variables. Dummy variables. Models with a dichotomous variable as dependent variable: LPM - and the Logit-model. Simultaneous equation models: the simultanity bias, identification. The two-stage least square method.

Instruction

Lectures

Assessment

The examination comprises a written test at the end of the course and compulsory assignments, (laboratory sessions). Three grades are awarded for the course: not passed, passed, and passed with distinction.

"If there are special reasons for doing so, an examiner may make an exception from the method of assessment indicated and allow a student to be assessed by another method. An example of special reasons might be a certificate regarding special pedagogical support from the University's disability coordinator."