ECON 723 : Econometrics 2

Business and Economics

2022 Semester Two (1225) (15 POINTS)

Course Prescription

An overview of time series econometrics, designed to introduce a range of material in stationary and nonstationary time series including: modern model determination methods, unit root and co-integration theory, non-linear time series analysis and continuous time models. Students will be introduced to practical time series forecasting methods.

Course Overview

This course will help you to rigorously understand issues in connecting data, statistics and economic theory and to be able to understand and carry out theoretical and empirical research using time-series data. Lectures and tutorials are delivered in a computer lab.  

The first part of the course provides an introduction to time series methods in econometrics, emphasizing estimation, inference and model specification in the context of stationary time series processes. We then introduce non-stationary time series processes, including unit roots, and related concepts such as co-integration. The second part of the course covers structural vector autoregressions and panel data methods. 

Students will be expected to conduct an empirical investigation of an economic issue or policy question using data that preferably have a time-series dimension (this includes panel data). They must write a professional report that motivates the econometric methods adopted and justifies the methods with a well-supported conclusion.  This project is worth 70% of the final grade. 

Course Requirements

No pre-requisites or restrictions

Capabilities Developed in this Course

Capability 1: Disciplinary Knowledge and Practice
Capability 2: Critical Thinking
Capability 3: Solution Seeking
Capability 4: Communication and Engagement

Learning Outcomes

By the end of this course, students will be able to:
  1. Apply the foundations of econometric theory (Capability 1 and 4.2)
  2. Critically evaluate hypotheses or policies using empirical methods (Capability 2 and 4.2)
  3. Apply strategies to uncover causal relationships in observational data (Capability 3 and 4.2)
  4. Use a computer programming language to manage and analyze data in a team setting (Capability 3 and 4.3)

Assessments

Assessment Type Percentage Classification
Project 70% Individual Coursework
Assignments 30% Group & Individual Coursework
Assessment Type Learning Outcome Addressed
1 2 3 4
Project
Assignments

Workload Expectations

This course is a standard 15 point course and students are expected to spend 10 hours per week involved in each 15 point course that they are enrolled in.

For this course, you can expect 2 hours of lectures, a 2 hour tutorial, 2 hours of reading and thinking about the content and 4 hours of work on assignments or the final project.

Delivery Mode

Campus Experience

Attendance is expected at scheduled activities including tutorials to complete components of the course.

Lectures will not be available as recordings. Other learning activities including tutorials will not be available as recordings.

The course will not include live online events.

The activities for the course are scheduled as a standard weekly timetable.

Learning Resources

Course materials are made available in a learning and collaboration tool called Canvas which also includes reading lists and lecture recordings (where available).

Please remember that the recording of any class on a personal device requires the permission of the instructor.

Student Feedback

At the end of every semester students will be invited to give feedback on the course and teaching through a tool called SET or Qualtrics. The lecturers and course co-ordinators will consider all feedback and respond with summaries and actions.

Your feedback helps teachers to improve the course and its delivery for future students.

Class Representatives in each class can take feedback to the department and faculty staff-student consultative committees.

Other Information

Please remember that the recording of any class on a personal device requires the permission of the instructor.

Academic Integrity

The University of Auckland will not tolerate cheating, or assisting others to cheat, and views cheating in coursework as a serious academic offence. The work that a student submits for grading must be the student's own work, reflecting their learning. Where work from other sources is used, it must be properly acknowledged and referenced. This requirement also applies to sources on the internet. A student's assessed work may be reviewed against online source material using computerised detection mechanisms.

Class Representatives

Class representatives are students tasked with representing student issues to departments, faculties, and the wider university. If you have a complaint about this course, please contact your class rep who will know how to raise it in the right channels. See your departmental noticeboard for contact details for your class reps.

Inclusive Learning

All students are asked to discuss any impairment related requirements privately, face to face and/or in written form with the course coordinator, lecturer or tutor.

Student Disability Services also provides support for students with a wide range of impairments, both visible and invisible, to succeed and excel at the University. For more information and contact details, please visit the Student Disability Services’ website http://disability.auckland.ac.nz

Special Circumstances

If your ability to complete assessed coursework is affected by illness or other personal circumstances outside of your control, contact a member of teaching staff as soon as possible before the assessment is due.

If your personal circumstances significantly affect your performance, or preparation, for an exam or eligible written test, refer to the University’s aegrotat or compassionate consideration page https://www.auckland.ac.nz/en/students/academic-information/exams-and-final-results/during-exams/aegrotat-and-compassionate-consideration.html.

This should be done as soon as possible and no later than seven days after the affected test or exam date.

Learning Continuity

In the event of an unexpected disruption, we undertake to maintain the continuity and standard of teaching and learning in all your courses throughout the year. If there are unexpected disruptions the University has contingency plans to ensure that access to your course continues and course assessment continues to meet the principles of the University’s assessment policy. Some adjustments may need to be made in emergencies. You will be kept fully informed by your course co-ordinator/director, and if disruption occurs you should refer to the university website for information about how to proceed.

Student Charter and Responsibilities

The Student Charter assumes and acknowledges that students are active participants in the learning process and that they have responsibilities to the institution and the international community of scholars. The University expects that students will act at all times in a way that demonstrates respect for the rights of other students and staff so that the learning environment is both safe and productive. For further information visit Student Charter https://www.auckland.ac.nz/en/students/forms-policies-and-guidelines/student-policies-and-guidelines/student-charter.html.

Disclaimer

Elements of this outline may be subject to change. The latest information about the course will be available for enrolled students in Canvas.

In this course students may be asked to submit coursework assessments digitally. The University reserves the right to conduct scheduled tests and examinations for this course online or through the use of computers or other electronic devices. Where tests or examinations are conducted online remote invigilation arrangements may be used. In exceptional circumstances changes to elements of this course may be necessary at short notice. Students enrolled in this course will be informed of any such changes and the reasons for them, as soon as possible, through Canvas.

Published on 22/11/2021 10:10 a.m.