Published Dec 01, 2023
This course focuses on the econometric techniques of empirical problems. Topics may include some basic concepts in time series analysis (such as deterministic and stochastic processes, stationary, ACF, Ergodicity conditions), established estimation techniques (such as OLS/GLS, MLE, GMM, Monte-Carlo simulation), popular univariate and multivariate time series models (such as ARMA process, autoregressive conditional heteroskedasticity model, stochastic volatility model, Vector Autoregression, Vector Moving Average), and non stationary models (such as random walk, unit roots, Dickey-Fuller Tests, Co-integration System and error corrections). Some related (empirical/theoretical) published papers for each topic may be discussed in class. An empirical project is required in the course, requiring the use of software (such as Matlab, SAS).
Prereq: ECON 621
This course covers some of the most important concepts, models and methods used in the empirical analysis of macroeconomic problems. In particular the course covers established time series techniques as well as more recent developments such as testing for unit roots, measurement of the persistence of shocks and estimation and hypothesis testing in cointegrated systems. Several dynamic models will be studied using time series analysis methods. Topics covered include basic concepts in time series analysis (modelling volatility and trend), VAR Modes (including structural VAR's), VECM and Cointegration, neural networks and estimation of DSGE models. During the course, students will have the opportunity to explore several case studies using econometric software such as RATS and MATLAB.
Prereq: ECON 602 and ECON 621
Preliminary Time Series Concepts [TSA: Chp. 2, Chp. 3]
(Deterministic and stochastic processes, basic concepts of Stationarity, Autocorrelation Function (ACF), Partial ACF (PACF), Ergodicity, Lag-Operator)
Linear Stationary Time Series Models [TSA: Chp. 3; EA: Chp. 19, 21]
(White Noise (WN) process, Autoregressive (AR) process), Moving Average (MA) process, mixed Autoregressive Moving Average (ARMA) process, Stationarity and invertibility conditions, Statistical properties and estimation strategy, forecasting)
Non-Linear Time Series Modelling with Time-varying Volatility [TSA: Chp. 21; EA: Chp. 19]
(Autoregressive Conditional Heteroskedasticity (ARCH), Generalized ARCH (GARCH), Stochastic Volatility (SV) model, statistical properties, estimation procedures, empirical applications)
Multivariate Time Series [TSA: Chp. 10, Chp. 11]
(Vector Autoregrssions (VAR), Vector Moving Average (VMA), Granger Causality, Maximum Likelihood Estimation (MLE) and Statistical Properties )
Non-Stationary Models for Time Series and Co-integration [TSA: Chp. 15, Chp. 16, Chp. 17, Chp 18, Chp. 19; EA: Chp. 22]
(Random Walk, Unit Roots, Dickey-Fuller Tests, Co-integration System and error correction)
*Recent Development in Non-linear Time Series Models [Journal Papers]
(Structural breaks; High Frequency data; Realized Measures)
*Mixture Models in Time Series [Journal Papers]
(Mixture models theory and applications; Mixture models in Machine Learning)
Some related papers (empirical / theoretical) for each topic might be discussed in the class. If necessary, some introductions of Matlab software may be illustrated for applications. The topics may not be covered in the exact order as shown above.
| Title / Name | Notes / Comments | Required |
|---|---|---|
| Time Series Analysis (TSA), by J. D. Hamilton | No | |
| Econometrics Analysis (EA), by W. H. Greene | No | |
| Statistics and Finance: An Introduction by Ruppert | No | |
| Time Series Analysis: Forecasting and Control by Box, Jenkins and Reinsel | No |
| Component | Value |
|---|---|
| Assignments | 15% |
| Midterm (on Mar. 18th) | 30% |
| In-Class Presentation | 15% |
| Term Project (Due on April 10) | 40% |
Econ 623: Students are expected to do a 15-20 minutes presentation based on the term research projects. Only two formats of the slides are acceptable: pdf or ppt. The presentation should focus on your motivations and contributions of the project (either in empirical data analysis or theoretical model development) with a brief literature review. Regarding the term paper, there is no restriction on the number of pages. However, it should include the following components: Introduction, Model, Data analysis and Conclusion.
Econ 723: Similar requirements as in Econ 623 are also applied to Econ 723. Students are expected to do a 30-40 minutes presentation based on the term research projects. The term paper will be evaluated with a higher standard. A thorough and comprehensive literature review is expected. The term paper should also present some theoretical results with Monte Carlo or empirical data analysis supports.
Text matching software (Turnitin) will be used to screen assignments in this course. This is being done to verify that use of all material and sources in assignments is documented. In the first week of the term, details will be provided about the arrangements for the use of Turnitin and alternatives in this course. See Administrative Policy below for more information and links.
Generative artificial intelligence (GenAI) trained using large language models (LLM) or other methods to produce text, images, music, or code, like Chat GPT, DALL-E, or GitHub CoPilot, may be used for assignments in this class with proper documentation, citation, and acknowledgement. Recommendations for how to cite GenAI in student work at the University of Waterloo may be found through the Library: https://subjectguides.uwaterloo.ca/chatgpt_generative_ai. Please be aware that generative AI is known to falsify references to other work and may fabricate facts and inaccurately express ideas. GenAI generates content based on the input of other human authors and may therefore contain inaccuracies or reflect biases.
In addition, you should be aware that the legal/copyright status of generative AI inputs and outputs is unclear. Exercise caution when using large portions of content from AI sources, especially images. More information is available from the Copyright Advisory Committee: https://uwaterloo.ca/copyright-at-waterloo/teaching/generative-artificial-intelligence
You are accountable for the content and accuracy of all work you submit in this class, including any supported by generative AI.
This course includes the independent development and practice of specific skills, such as [fill this in with your discipline-specific skills]. Therefore, the use of Generative artificial intelligence (GenAI) trained using large language models (LLM) or other methods to produce text, images, music, or code, like Chat GPT, DALL-E, or GitHub CoPilot, is not permitted in this class. Unauthorized use in this course, such as running course materials through GenAI or using GenAI to complete a course assessment is considered a violation of Policy 71 (plagiarism or unauthorized aids or assistance). Work produced with the assistance of AI tools does not represent the author’s original work and is therefore in violation of the fundamental values of academic integrity including honesty, trust, respect, fairness, responsibility and courage (ICAI, n.d.).
You should be prepared to show your work. To demonstrate your learning, you should keep your rough notes, including research notes, brainstorming, and drafting notes. You may be asked to submit these notes along with earlier drafts of their work, either through saved drafts or saved versions of a document. If the use of GenAI is suspected where not permitted, you may be asked to meet with your instructor or TA to provide explanations to support the submitted material as being your original work. Through this process, if you have not sufficiently supported your work, academic misconduct allegations may be brought to the Associate Dean.
In addition, you should be aware that the legal/copyright status of generative AI inputs and outputs is unclear. More information is available from the Copyright Advisory Committee: https://uwaterloo.ca/copyright-at-waterloo/teaching/generative-artificial-intelligence
Students are encouraged to reach out to campus supports if they need help with their coursework including:
Students should be aware that this course contains the intellectual property of their instructor, TA, and/or the University of Waterloo.
Intellectual property includes items such as:
Course materials and the intellectual property contained therein, are used to enhance a student’s educational experience. However, sharing this intellectual property without the intellectual property owner’s permission is a violation of intellectual property rights. For this reason, it is necessary to ask the instructor, TA and/or the University of Waterloo for permission before uploading and sharing the intellectual property of others online (e.g., to an online repository).
Permission from an instructor, TA or the University is also necessary before sharing the intellectual property of others from completed courses with students taking the same/similar courses in subsequent terms/years. In many cases, instructors might be happy to allow distribution of certain materials. However, doing so without expressed permission is considered a violation of intellectual property rights.
Please alert the instructor if you become aware of intellectual property belonging to others (past or present) circulating, either through the student body or online. The intellectual property rights owner deserves to know (and may have already given their consent).
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Important notes
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Please note that a cross-listed course will count in all respective averages no matter under which subject code it has been taken. For example, a PHIL/PSCI cross-list will count in a Philosophy major average, even if the course was taken under the Political Science subject code.
All deferred Final Exam requests for economics courses are administered by the Economics Undergraduate Office. Please consult the Deferred Exam Policy at
https://uwaterloo.ca/economics/undergraduate/resources-and-policies/deferred-final-exam-policy.
Academic integrity: In order to maintain a culture of academic integrity, members of the University of Waterloo community are expected to promote honesty, trust, fairness, respect and responsibility. [Check the Office of Academic Integrity for more information.]
Grievance: A student who believes that a decision affecting some aspect of their university life has been unfair or unreasonable may have grounds for initiating a grievance. Read Policy 70, Student Petitions and Grievances, Section 4. When in doubt, please be certain to contact the department’s administrative assistant who will provide further assistance.
Discipline: A student is expected to know what constitutes academic integrity to avoid committing an academic offence, and to take responsibility for their actions. [Check the Office of Academic Integrity for more information.] A student who is unsure whether an action constitutes an offence, or who needs help in learning how to avoid offences (e.g., plagiarism, cheating) or about “rules” for group work/collaboration should seek guidance from the course instructor, academic advisor, or the undergraduate associate dean. For information on categories of offences and types of penalties, students should refer to Policy 71, Student Discipline. For typical penalties, check Guidelines for the Assessment of Penalties.
Appeals: A decision made or penalty imposed under Policy 70, Student Petitions and Grievances (other than a petition) or Policy 71, Student Discipline may be appealed if there is a ground. A student who believes they have a ground for an appeal should refer to Policy 72, Student Appeals.
Note for students with disabilities: AccessAbility Services, located in Needles Hall, Room 1401, collaborates with all academic departments to arrange appropriate accommodations for students with disabilities without compromising the academic integrity of the curriculum. If you require academic accommodations to lessen the impact of your disability, please register with AccessAbility Services at the beginning of each academic term.
Turnitin.com: Text matching software (Turnitin®) may be used to screen assignments in this course. Turnitin® is used to verify that all materials and sources in assignments are documented. Students' submissions are stored on a U.S. server, therefore students must be given an alternative (e.g., scaffolded assignment or annotated bibliography), if they are concerned about their privacy and/or security. Students will be given due notice, in the first week of the term and/or at the time assignment details are provided, about arrangements and alternatives for the use of Turnitin in this course.
It is the responsibility of the student to notify the instructor if they, in the first week of term or at the time assignment details are provided, wish to submit alternate assignment.