ETC5420 · Microeconometrics

Overview

This unit involves the analysis of micro-level cross-sectional and panel data to study the behaviour of individuals and other micro-units as decision makers. It studies the specification, estimation, inference and evaluation of a range of microeconometric models. These include models for discrete, count, duration, censored or truncated dependent variables and examine issues arisen from sample selection and endogenous treatment. You will also gain hands-on experience and computation skills for analysing large scale micro datasets. The computing package used for the unit is STATA.

Offerings

Assessment

The Handbook lists an examination for this unit.

Assessment details may change. Please refer to the assessment information in Moodle closer to the start of the teaching period.

Requisites

Learning outcomes

1. become familiar with typical features and structures of micro datasets
2. identify microeconometric models suitable for given micro datasets and given research objectives
3. specify, estimate, evaluate and analyse econometric models with dependent variables that are binary choices, multinomial discrete choices, durations, censored or truncated, using a given dataset
4. summarise and present key model results and measures of interest in tables and graphs
5. be comfortable and proficient with the use of STATA software to manage and analyse large datasets
6. have had hands-on experience with analysing several real world micro datasets in health, labour, finance and marketing research, through computing exercises and a written assignment.

Workload

Minimum total expected workload to achieve the learning outcomes for this unit is 144 hours per semester typically comprising a mixture of scheduled learning activities and independent study. Independent study may include associated readings, assessment and preparation for scheduled activities. You are expected to complete all pre-class activities prior to your scheduled class, and post-class activities should be completed after your scheduled class. Learning activities may include a combination of teacher directed, peer directed and online engagement activities.

Student experience, not official rules. Nothing here changes what the Handbook says.