MODELLING WITH ECONOMETRICS: CHALLENGES AND SOLUTIONS

ECM3MEC

2020

Credit points: 15

Subject outline

This subject builds on the multiple regression methodology and presents some general estimation and testing methods used in modern econometrics for applied economics and finance. The first part covers estimation and testing methods that address issues which arise when working with data that have a more general structure, such as cross-sectional data, discrete choice or outcome data and panel data. The second part of the subject will cover issues that are related to testing and estimation using non-stationary time series data in economics and finance, involving both unit root testing and co-integration. Empirical applications in economics and finance will be carried out using EVIEWS or similar software.

SchoolLa Trobe Business School

Credit points15

Subject Co-ordinatorJae Kim

Available to Study Abroad/Exchange StudentsYes

Subject year levelYear Level 3 - UG

Available as ElectiveNo

Learning ActivitiesN/A

Capstone subjectNo

Subject particulars

Subject rules

PrerequisitesECM2IE OR ECO2EME

Co-requisitesN/A

Incompatible subjectsN/A

Equivalent subjectsN/A

Quota Management StrategyN/A

Quota-conditions or rulesN/A

Special conditionsN/A

Minimum credit point requirementN/A

Assumed knowledgeN/A

Career Ready

Career-focusedNo

Work-based learningNo

Self sourced or Uni sourcedN/A

Entire subject or partial subjectN/A

Total hours/days requiredN/A

Location of WBL activity (region)N/A

WBL addtional requirementsN/A

Graduate capabilities & intended learning outcomes

Graduate Capabilities

COMMUNICATION - Communicating and Influencing
DISCIPLINE KNOWLEDGE AND SKILLS
INQUIRY AND ANALYSIS - Creativity and Innovation
INQUIRY AND ANALYSIS - Critical Thinking and Problem Solving
INQUIRY AND ANALYSIS - Research and Evidence-Based Inquiry

Intended Learning Outcomes

01. Conduct the least-squares estimation for a multiple regression model, and interpret the estimation and statistical testing results
02. Explain the maximum likelihood estimation method in the context of multiple regression and its application to model diagnostic testing
03. Explain the limited dependent variable estimation method and apply it to economic and financial data (such logit, probit, tobit and multinomial equation models)
04. Design and conduct Monte Carlo simulation, estimate panel data model and interpret the estimation results
05. Explain the meaning of a unit-root (or non-stationary series and its consequences for multiple regression model, and identify its implications for long-run equilibrium relationships among economic and financial data

Subject options

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Start date between: and    Key dates

Melbourne (Bundoora), 2020, Semester 1, Day

Overview

Online enrolmentYes

Maximum enrolment sizeN/A

Subject Instance Co-ordinatorJae Kim

Class requirements

Laboratory Class Week: 10 - 22
One 1.00 h laboratory class per week on weekdays during the day from week 10 to week 22 and delivered via face-to-face.
"Conducted as computer lab classes where students work on applications and assessments relevant to the curriculum."

Lecture Week: 10 - 22
One 2.00 h lecture per week on weekdays during the day from week 10 to week 22 and delivered via face-to-face.

Assessments

Assessment elementCommentsCategoryContributionHurdle% ILO*
One 2-hour final examination, equivalent to 2,000 words per student.N/AN/AN/ANo50 SILO1, SILO2, SILO3, SILO4, SILO5
Two tutorial quizzes, equivalent to 250 words each per student, or 500 words in total.N/AN/AN/ANo10 SILO1, SILO2
Two individual quantitative assignments on empirical estimation, equivalent to 500 words each.N/AN/AN/ANo20 SILO1, SILO3, SILO4
One group research-related assignment, equivalent to 1,000 words per student. Groups of two, so total equivalent word count of 2,000 words for the assignment.N/AN/AN/ANo20 SILO1, SILO2, SILO3, SILO4, SILO5