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TOKT1103 — Kinh tế lượng II | Chương trình đào tạo NEU

TOKT1103 — Kinh tế lượng II

Credits
3
Showing intake
K66-2024

Course description

This course provides a solid theoretical and practical foundation for interpreting empirical evidence: economic relationships in the short run and the long run, the simultaneous effects of variables, the effects of factors on choice, fixed effects and random effects, the components of a time series, and the properties of a time series. The course comprises two components: econometric theory and the empirical implementation of each type of model using R software.

Some of the main topics:

Distributed lag models, autoregressive models, and causality

Simultaneous equation models

Models with discrete choices such as logit, probit, multinomial, etc.

Models with panel data

And the basic concepts and models of time series and economic forecasting.

The course places particular emphasis on the procedures and the potential pitfalls involved in conducting and evaluating applied econometric research. The course equips students with the knowledge needed to carry out econometric research on their own using typical economic data.

Course requirements

Regulations on eligibility to take the final examination

The attendance score must be 5 or higher (in accordance with the general regulations of the University)

Method of assessing the attendance score: see Appendix 2, Rubric 1.

Regulations on class attendance

Students are responsible for attending all class sessions in full.

In the event of an absence due to force majeure, students must proactively study the material through the resources provided by the lecturer and other required readings.

Regulations on classroom conduct

The course is conducted on the principle of respect for both learners and instructors;

Take complete notes and participate in answering questions;

Read the required materials in advance;

Complete the homework assignments;

Do not make noise or disturb others, and do not eat during class;

Laptops, tablets, and mobile phones may be used only for the purpose of taking lecture notes and performing calculations for study purposes, and must absolutely not be used for any other purpose.

11. SYLLABUS ADJUSTMENT TIME

Month 2024

Appendix 1. EXAMINATION MATRIX

Weight in the course grade: 50%

Format: Objective multiple-choice examination on computer

Duration: 60 minutes

Number of questions: 40 questions. Each question is worth 0.25 points

Appendix 2. RUBRICS

Rubric 1. Assessment of the attendance score

Weight in the course grade: 10%

Assessed over 8 seminar sessions.

* Note: 10+ : may be considered for adding points to the 40% assessment score when the attendance score is 10.

Criteria for assessing participation in discussion

CLOs of the attendance assessment

Rubric 2. Assessment of the term assignment

Weight: 20% (the first component of the 40% assessment score)

Assessed through the result of the group assignment; a presentation is not required

Rubric 3. Assessment of the R practical work

Weight: 20% (the second component of the 40% assessment score)

Assessed through the result of the practical work on computer

Total number of questions: 20 questions, each worth 0.5 points.

Duration: 30 minutes

Learning outcomes and assessment

The training management system automatically assesses the level of CLO attainment according to the University's common algorithm. The course learning outcomes are evaluated by students and the University after the final examination.

Lecturers

  • Prof. Nguyen Quang Dong
  • Nguyễn Mạnh Thế
  • c. Bui Duong Hai
  • c. Hoang Bich Phuong

Teaching plan

Week 1

Lecture 1. -Introduction to the course -Distributed lag models and autoregressive models 1.1. Distributed lag models and autoregressive models 1.2. Koyck distributed lag model 1.3. Adaptive expectations model 1.4. Partial adjustment model 1.5. The problem of model estimation. 1.5. Causality - Granger causality test 1.6. Almon polynomial
References
[1] Introductory chapter [2] Slides of Lecture 1 [1] Chapter 8 [3] Chapter 18
Activities
Lecturer - Introduces the syllabus, textbook, reference materials, software, and slides; the files shared on the LMS, and the data - Lectures on the theory following the content of the Lecture 1 slides - Introduces some examples on the slides - Raises some issues and situations for learners to discuss. - Provides guidance on reading the study materials - Asks students to review the knowledge, the estimation code, and model analysis - Asks students to review the examples and questions in advance and to do the exercises for Seminar 1 Students In class: - Listen to the lecture; participate in speaking and discussion. At home: - Review the content of Lecture 1 - Read the required materials - Prepare the exercises and practical work as required for Seminar 1 - Download and install the Packages for the course in R.

Week 2

Seminar 1 - Guidance on installing the Packages for time series in R - Guidance on the practical estimation of distributed lag models, autoregressive models, other forms, and the Granger test. - Guidance on the method for doing the group assignment, and forming groups - Discussion of the examples, questions, and exercises of Seminar 1
References
[2] Slides of Lecture 1 and Seminar 1 Slides of Practical session 1
Activities
Lecturer: - Reviews the knowledge of estimating regression models with time series: creating a time variable, plots, descriptive statistics, the estimation problem, and hypothesis testing. - Guides on how to obtain data from various sources - Guides on installing additional Packages in R - Carries out the practical work for two models and the causality test. - Guides and explains the estimation results based on the output. - Guides the exercises in Chapter 8 [1] - Forms the groups for the term assignment. Students: In class: - Follow along and write code, run codes. - Read, analyze the obtained results, and discuss. - Carry out the examples, questions, and exercises and answer the questions - Form groups for the group assignment, and each group proposes a topic to be discussed in subsequent Seminars. At home: - Redo and complete the practical work, edit the code file, and export the results to Word for later review when needed. - Read the slides for Lecture 2 in advance

Learning resources

  • Textbook of Econometrics — Nguyen Quang Dong, Nguyen Thi Minh, National Economics University Publishing House, 2019
  • Introductory Econometrics: A Modern Approach — Jeffrey M. Wooldridge, McGraw-Hill Press, 2009
  • Introductory Econometrics for Finance — Chris Brooks, Cambridge University Press, 2019
Assessment
- Attendance check - code file - Answering questions - Participating in discussion

Week 3

Lecture 2. Multiple-equation models Feedback mechanism Identification, identification rules Hausman test for identification 2.3. Endogeneity test 2.4. Estimation of systems of equations 2.5. Forecasting and scenario building
References
[1] Chapter 9. Sections 9.1 to 9.5 [2] Slides of Lecture 2 [3] Chapter 16
Activities
Lecturer - Lectures on and explains the content of the lesson following the slides - Illustrates the theory through the examples on the slides - Poses questions/presents situations -Answers the learners' questions - Provides guidance on reading the study materials - Assigns exercises and tasks to learners to prepare for Seminar 2 Students In class: - Listen to the lecture and absorb the knowledge - Discuss and answer the issues raised by the lecturer. - Do the examples and exercises as required At home: - Review the content of Lecture 2 - Read the required materials - Prepare the exercises and practical work as required for Seminar 2

Week 4

Seminar 2 - Practice on the three methods of estimating systems of equations using R software - Discussion of the results of the examples, questions, and exercises of Seminar 2 - Discussion of the group assignment topics when a group makes a proposal
References
[1] Chapter 1 [2] Slides of Seminar 2
Activities
Lecturer - Guides on writing code and practicing the estimation of systems of equations in R for the 3 methods OLS, ILS, 2SLS; and the endogeneity test - Reads, analyzes, and discusses the estimation results - Guides/discusses the exercises in the textbook, Chapter 9 [1] - Guides on finding a topic and data for the term assignment - Comments on the topics proposed by the groups Students In class: - Write code and carry out the practical work on computer - Participate in correcting the exercises and discussing the results - Propose a topic for the term assignment - Participate in discussing the term assignment topic of their own group or other groups At home: - Redo and complete the practical work, edit the code file, and export the results to Word for later review when needed. - Read the slides for Lecture 3 in advance
Assessment
- Attendance check - code file - Answering questions - Participating in discussion - Doing the exercises in the textbook as required - Presenting the group's topic - Discussing other groups' topics

Week 5

Lecture 3: Regression with a discrete dependent variable 3.1. Linear probability model 3.2. Logit model 3.3. Probit model 3.4. Hypothesis testing for the logit and probit models 3.5. Tobit model 3.6. Poisson model 3.7. Multinomial model
References
[1] Chapter 10, sections 10.1 to 10.6 [2] Slides of Lecture 3 [3] Chapter 17
Activities
Lecturer - Lectures on and explains the content following the slides of Lesson 3 - Introduces some examples on the slides and poses questions on some content. -Answers the students' questions - Provides guidance on reading the materials - Assigns exercises and practical work to be discussed in Seminar 3 Students: -In class: listen to the lecture, do the examples, answer the questions/discuss the situations - At home: Review the content of Lecture 3; read the required materials; prepare the exercises and practical work as required for Seminar 3

Week 6

Seminar 3 - Practice on Logit, Probit, Tobit, Poisson, and Multinomial regression - Discussion of the group assignment topics when a group makes a proposal - Discussion of the groups' data
References
[1] Chapter 10 [2] Slides of Lecture 3 and Seminar 3 [3] Practical session 4
Activities
Lecturer: - Guides on writing code and practicing regression for the 5 models in R - Guides on writing code to compute the OR, to compute by factors, to compute marginal effects, etc. - Guides on reading and analyzing the results - Answers the questions in the Seminar 3 slides and the exercises in the textbook, Chapter 10 [1] - Discusses the topics proposed by the groups - Discusses the data submitted by the groups Students: - In class: write and run codes on computer to estimate the 5 types of models; participate in discussion and present the results Propose a topic for the term assignment Present the data that has been collected Participate in discussing the topic and data for the term assignment of their own group or other groups - At home: Redo and complete the practical work, edit the code file, and export the results to Word for later review when needed; read the slides for Lecture 4 in advance.
Assessment
- Attendance check - code file - Answering the questions in the Seminar 3 slides - Answering questions about the multiple-regression estimation results - Answering the exercises in the textbook, Chapter 10 - Presenting the group's topic and data - Discussing the topic and data of other groups

Week 7

Lecture 4. Models with panel data 4.1. Introduction to models with panel data 4.2. Fixed effects model 4.3. Time fixed effects model 4.4. Random effects model
References
[2] Slides of Lecture 4 [3] Chapter
Activities
Lecturer: - Lectures on the theory following the content of the Lecture 4 slides - Raises some issues/discussion questions - Introduces the examples on the slides - Provides guidance on reading reference Chapter 14 [3] - Asks students to do the exercises and carry out the examples, questions, and exercises for Seminar 4 Students: In class: listen to the lecture, participate in speaking/discussion At home: review the content of Lecture 4, read the required materials; prepare the exercises and practical work as required for Seminar 4.

Week 8

Seminar 4 - Practice on interval estimation and hypothesis testing using R software - Discussion of the examples, questions, and exercises of Seminar 4 - Discussion of the exercises in the textbook, Chapter 3 - Discussion of the group assignment topics when a group makes a proposal - Discussion of the groups' data
References
[4] Chapter 14 [2] Slides of Lecture 4 and Seminar 4
Activities
Lecturer: - Guides on writing code and practicing the estimation of fixed effects and random effects - Carries out testing to select the model - Computes the coefficients and the differences in the intercept and slope coefficients - Discusses the estimation results - Discusses/guides the exercises in Chapter 14 [3]. - Discusses the data submitted by the groups. - Discusses the models built by the groups Students In class: - Write code, carry out estimation, hypothesis testing, and compute other information for each entity and each time point - Discuss the obtained results; participate in correcting and presenting the exercises - Discuss the data collected and the model estimated by the group. - Participate in discussing the preliminary estimation results of other groups At home: - Redo and complete the practical work, edit the code file, and export the results to Word for later review when needed. - Read the slides for Lecture 5 in advance
Assessment
- Attendance check - Code file of the practical work. - Answering the questions in the slides - Answering questions about inferences on the regression model - Answering the exercises in the textbook, Chapter 14 [3] - Presenting the group's estimation results - Discussing the estimation results of other groups

Week 9

Lecture 5: Analysis of the components of a time series 5.1. Simple extrapolation model 5.2. Test for randomness - runs test 5.3. Simple smoothing methods 5.4. Seasonal adjustment 5.5. The components of a time series 5.6. Holt-Winters exponential smoothing forecasting model 5.7. Hodrick-Prescott (HP) filter
References
[1] Chapter 11, sections 11.1 to 11.7 [2] Slides of Lecture 5
Activities
Lecturer: - Lectures on the theory following the content of the Lecture 5 slides - Introduces the examples on the slides - Poses questions/presents some situations - Provides guidance on reading [1] Chapter 11 and the reference materials. - Asks students to review the examples, questions, and exercises in advance for Seminar 5 Students In class: Listen to the lecture, participate in speaking and discussion. At home: - Review the content of Lecture 5 - Read the required materials - Prepare the exercises and practical work as required for Seminar 5

Week 10

Seminar 5 - Guidance on estimating the trend model and smoothing the series in R. - Guidance on computing seasonal factors and simple smoothing - Guidance on analyzing the components according to the Holt-Winters model - Guidance on estimating the model using the Hodrick-Prescott filter
References
[1] Chapter 11 [2] Slides of Lecture 5 and Seminar 5 [3] Practical session 5
Activities
Lecturer: - Guides on writing code and running the trend-variable model, computing seasonal factors, simple smoothing, the Holt-Winters model, and the HP model. - Guides on carrying out forecasting with the above models - Guides on reading and analyzing the results - Discusses and answers the questions - Comments on the data of the groups that have newly submitted data. Students In class: - Write and run code for the models; analyze the obtained results; answer questions/discuss the results. - Propose a topic for the term assignment - Present the data that has been collected - Participate in discussing the topic and data for the term assignment of their own group or other groups At home: - Redo and complete the practical work, edit the code file, and export the results to Word for later review when needed. - Read the slides for Lecture 6 in advance
Assessment
- Attendance check - Code file of Seminar 5 - Answering the questions in the Seminar 5 slides - Answering questions about the estimation results during the practical work - Answering the exercises in the textbook, Chapter 11 [1] - Presenting the group's estimation results - Discussing the estimation results of other groups

Week 11

Lecture 6: Non-stationary stochastic processes 6.1. Stationary and non-stationary stochastic processes 6.2. Some simple stochastic processes 6.3. Unit root test 6.4. Autocorrelation function 6.5. Non-stationary series and the classical regression model 6.6. Spurious regression, trend-stationary and difference-stationary series Cointegrating regression test
References
[1] Chapter 12, sections 12.1 - 12.7. [2] Slides of Lecture 6 [4] Chapter 6 [4] Chapters 10, 11, 12, 13
Activities
Lecturer: - Lectures on the theory following the content of the Lecture 6 slides - Introduces the examples on the slides - Raises some issues for discussion - Provides guidance on reading the study materials - Assigns the exercises in Chapter 15 [1] - Asks students to review the examples, questions, and exercises in advance for Seminar 6 Students In class: - Listen to the lecture; discuss/answer the content raised by the lecturer At home: - Review the content of Lecture 6 - Read the required materials - Prepare the exercises and practical work as required for Seminar 6

Week 12

Seminar 6 - Guidance on practicing time series testing - Transforming a non-stationary series into a stationary series - Guidance on testing the single-equation error correction model - Guidance on the exercises in Lecture 6, Chapter 11 [1] - Discussion of the group assignment
References
[1] Chapter 5 [2] Slides of Lecture 6 and Seminar 6 [3] Practical sessions 6, 7, 8, 10
Activities
Lecturer - Guides on writing code and running code to simulate stationary and non-stationary series, AR(1), AR(2), and MA. - Computes ACF and PACF in R; performs the DF, Bartlett, Box-Pierce, Ljung-Box, AIC, BIC tests, etc. - Guides on the cointegration test for a single-equation model (ECM). - Guides and discusses the exercises of Lecture 6, Chapter 12 [1]. - Discusses the students' data in the group assignment Students In class: - Write and run codes following the practical work. - Read, analyze, and discuss the obtained results. - Participate in correcting the exercises - Discuss the group assignment At home: - Redo and complete the practical work, edit the code file, and export the results to Word for later review when needed. - Read the slides for Lecture 2 in advance
Assessment
- Attendance check - Answering the questions in the Seminar 6 slides - Answering questions about the estimation results during the practical work - Answering the exercises in the textbook, Chapter 12 [1] - Presenting the group's research results - Discussing the research results of other groups

Week 13

Lecture 7 (Continuation of Lecture 6): Non-stationary stochastic processes 6.1. Stationary and non-stationary stochastic processes 6.2. Some simple stochastic processes 6.3. Unit root test 6.4. Autocorrelation function 6.5. Non-stationary series and the classical regression model 6.6. Spurious regression, trend-stationary and difference-stationary series Cointegrating regression test
References
[1] Chapter 12, sections 12.1 - 12.7. [2] Slides of Lecture 6 [4] Chapter 6 [4] Chapters 10, 11, 12, 13
Activities
Lecturer: - Lectures on the theory following the content of the Lecture 6 slides - Introduces the examples on the slides - Raises some issues for discussion - Provides guidance on reading the study materials - Assigns the exercises in Chapter 15 [1] - Asks students to review the examples, questions, and exercises in advance for Seminar 6 Students In class: - Listen to the lecture; discuss/answer the content raised by the lecturer At home: - Review the content of Lecture 6 - Read the required materials - Prepare the exercises and practical work as required for Seminar 6

Week 14

Seminar 7 (Continuation of Seminar 6) - Guidance on practicing time series testing - Transforming a non-stationary series into a stationary series - Guidance on testing the single-equation error correction model - Guidance on the exercises in Lecture 7 - Discussion of the group assignment
References
[1] Chapter 5 [2] Slides of Lecture 6 and Seminar 6 [3] Practical sessions 6, 7, 8, 10
Activities
Lecturer - Guides on writing code and running code to simulate stationary and non-stationary series, AR(1), AR(2), and MA. - Computes ACF and PACF in R; performs the DF, Bartlett, Box-Pierce, Ljung-Box, AIC, BIC tests, etc. - Guides on the cointegration test for a single-equation model (ECM). - Guides and discusses the exercises of Lecture 6, Chapter 12 [1]. - Discusses the students' data in the group assignment Students In class: - Write and run codes following the practical work. - Read, analyze, and discuss the obtained results. - Participate in correcting the exercises - Discuss the group assignment At home: - Redo and complete the practical work, edit the code file, and export the results to Word for later review when needed. - Read the slides for Lecture 2 in advance
Assessment
- Attendance check - Answering the questions in the Seminar 6+7 slides - Answering questions about the phenomena of the model - Answering the exercises in the textbook, Chapters 5 and 7 - Briefly presenting the group's research results - Discussing the research results of other groups

Week 15

Seminar 8 - Reviewing the course as a whole - Announcing the component assessment results - Examination guidance and answering questions
References
[2] Slides of Seminar 8
Activities
Lecturer: - Synthesizes and reviews the knowledge from the lectures - Reviews the types of questions for the final examination - Announces the 10% attendance score - Announces the term assignment scores and answers questions - Announces the practical test scores and answers questions - Announces the 40% score - Corrects some questions in the reference examination questions Students: - Attend in full to receive information about the component scores - Review the entire course content and the reference questions
Assessment
- Attendance check - After the session, no queries about the component scores will be addressed
Final examination
References
… (Entire content / Which lessons, which chapters…)
Activities
Multiple-choice / essay examination - State only 1 format
Assessment
See Appendix 1: examination matrix