Time Series Econometrics
Postgraduate: Level 7
Monday 15 January 2024
Friday 22 March 2024
05 September 2023
Requisites for this module
MSC L10112 Economics and Econometrics,
MSC L101EB Economics and Econometrics,
MSC L101EK Economics and Econometrics,
MSC L101KE Economics and Econometrics,
MSC L101UH Economics and Econometrics,
MSC L11412 Financial Econometrics,
MSC L10212 Financial Economics and Econometrics,
PHD L10048 Economics
This module is concerned with some topics in modern time series econometrics.
The module coverage begins with some of the fundamental concepts used to analyse stationary time series, before proceeding to the analysis of nonstationary (integrated) processes that have dominated recent research in theoretical and applied time series econometrics. The module concludes with a treatment of continuous time models and models with nonlinearities in mean and variance.
The aims of this module are:
- To focus on the maximum likelihood estimation of linear models.
- To examine univariate and multivariate processes and models.
By the end of this module, students will be expected to be able to:
- Acquire an appreciation of econometric methods applicable to the analysis of models for economic time series, covering stationary and nonstationary situations in both univariate and multivariate contexts.
- Understand the methods of estimation and inference as applied in these models.
- Be able to derive the properties of some econometric methods applicable to time serie.
- Be prepared for the use of these methods in their own empirical research.
This module is not suitable for students from the Essex Business School.
This module will be delivered via:
- One 2-hour lecture per week.
Feedback for this module will occur through:
- Class meetings, where we will go over the answers to problem sets and where you will be able to ask questions about your own method of solution.
- Outline answers that will be posted on the website for the module that will give you written guidance on the appropriate method to approach the problem sets and tests.
- Academic support hours where additional questions can be addressed and further feedback obtained.
Students should make sure that they use these methods and resources to understand how to improve their own performance.
This module does not appear to have a published bibliography for this year.
Assessment items, weightings and deadlines
|Coursework / exam
|Mid-Term Test - In person
|Main exam: In-Person, Open Book, 120 minutes during Summer (Main Period)
|Reassessment Main exam: In-Person, Open Book, 120 minutes during September (Reassessment Period)
Exam format definitions
- Remote, open book: Your exam will take place remotely via an online learning platform. You may refer to any physical or electronic materials during the exam.
- In-person, open book: Your exam will take place on campus under invigilation. You may refer to any physical materials such as paper study notes or a textbook during the exam. Electronic devices may not be used in the exam.
- In-person, open book (restricted): The exam will take place on campus under invigilation. You may refer only to specific physical materials such as a named textbook during the exam. Permitted materials will be specified by your department. Electronic devices may not be used in the exam.
- In-person, closed book: The exam will take place on campus under invigilation. You may not refer to any physical materials or electronic devices during the exam. There may be times when a paper dictionary,
for example, may be permitted in an otherwise closed book exam. Any exceptions will be specified by your department.
Your department will provide further guidance before your exams.
Module supervisor and teaching staff
Dr Shin Kanaya, email: firstname.lastname@example.org.
Lectures & Classes: Dr Shin Kanaya
For further information, send an email message to email@example.com.
Miss Maria Kyriacou
Available via Moodle
Of 14 hours, 14 (100%) hours available to students:
0 hours not recorded due to service coverage or fault;
0 hours not recorded due to opt-out by lecturer(s), module, or event type.
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