ecm3mec modelling with econometrics challenges and solutions

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 (Pre 2022)

Credit points15

Subject Co-ordinatorJae Kim

Available to Study Abroad/Exchange StudentsYes

Subject year levelYear Level 3 - UG

Exchange StudentsNo

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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Melbourne (Bundoora), 2020, Semester 1, Day

Overview

Online enrolmentYes

Maximum enrolment sizeN/A

Enrolment informationN/A

Subject Instance Co-ordinatorJae Kim

Class requirements

Laboratory ClassWeek: 10 - 22
One 1.00 hour 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.

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

Assessments

Assessment element	Comments	Category	Contribution	Hurdle	%	ILO*
One 2-hour final examination, equivalent to 2,000 words per student.	N/A	N/A	N/A	No	50	SILO1, SILO2, SILO3, SILO4, SILO5
Two tutorial quizzes, equivalent to 250 words each per student, or 500 words in total.	N/A	N/A	N/A	No	10	SILO1, SILO2
Two individual quantitative assignments on empirical estimation, equivalent to 500 words each.	N/A	N/A	N/A	No	20	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/A	N/A	N/A	No	20	SILO1, SILO2, SILO3, SILO4, SILO5