Overview

This unit is designed so that students learn fundamental techniques of data analysis, basic econometric methods and learn to use data to solve real-world problems by estimating relevant parameters (such as elasticities, marginal values etc). Students acquire expertise in applying data analysis and econometric methods, including regression analysis and its extensions, to various types of data. Students also learn how to use econometrics to test theory, analyse economic and business behaviour, and assist in policy formation. The subject is application orientated and practical work is performed using statistical software.

Requisites

Prerequisites
FIN10002 Financial Statistics
Teaching periods
Location
Start and end dates
Last self-enrolment date
Census date
Last withdraw without fail date
Results released date

Learning outcomes

Students who successfully complete this unit will be able to:

  • Demonstrate an understanding of important econometric methods and interpret estimation results
  • Analyse the concepts of model specification and diagnostic testing procedure in time series and cross sectional econometrics
  • Solve problems using econometric computer software and commercial databases
  • Work in groups to solve econometric problems and clearly communicate their results and interpretations

Teaching methods

Hawthorn

Type Hours per week Number of weeks Total (number of hours)
On-campus
Class		2.00 12 weeks 24
Online
Lecture		1.00 12 weeks 12
Unspecified Activities
Independent Learning		9.50 12 weeks 114
TOTAL150

OUA

Type Hours per week Number of weeks Total (number of hours)
Online
Directed Online Learning and Independent Learning		12.50 12 weeks 150
TOTAL150

Assessment

Type Task Weighting ULO's
AssessmentIndividual 40 - 60% 1,2 
AssessmentIndividual 20 - 30% 1,2 
PresentationGroup 10 - 20% 1,2,3,4 
Project ReportGroup 10 - 20% 1,2,3,4 

Content

  • Linear Regression
  • Interval Estimation and Hypothesis Testing
  • Prediction, Goodness of Fit and Modelling Issues
  • Multiple Regression Model
  • Further Inference in the Multiple Regression Model
  • Nonlinear relationships
  • Heteroskedasticity
  • Dynamic models, autocorrelation and forecasting
  • Non-stationary time series data and co-integration
  • Qualitative and limited dependent variable models

Study resources

Reading materials

A list of reading materials and/or required textbooks will be available in the Unit Outline on Canvas.