EPOKA UNIVERSITY
FACULTY OF ECONOMICS AND ADMINISTRATIVE SCIENCES
DEPARTMENT OF ECONOMICS
COURSE SYLLABUS
2023-2024 ACADEMIC YEAR
COURSE INFORMATIONCourse Title: ECONOMETRICS II |
| Code | Course Type | Regular Semester | Theory | Practice | Lab | Credits | ECTS |
|---|---|---|---|---|---|---|---|
| ECO 312 | B | 6 | 2 | 0 | 2 | 3 | 6 |
| Academic staff member responsible for the design of the course syllabus (name, surname, academic title/scientific degree, email address and signature) | Prof.Dr. Güngör Turan gturan@epoka.edu.al |
| Main Course Lecturer (name, surname, academic title/scientific degree, email address and signature) and Office Hours: | M.Sc. Egla Mansi emansi@epoka.edu.al , TBA |
| Second Course Lecturer(s) (name, surname, academic title/scientific degree, email address and signature) and Office Hours: | NA |
| Language: | English |
| Compulsory/Elective: | Elective |
| Study program: (the study for which this course is offered) | Bachelor in Business Informatics (3 years) |
| Classroom and Meeting Time: | Monday and Thursday |
| Teaching Assistant(s) and Office Hours: | NA |
| Code of Ethics: |
Code of Ethics of EPOKA University Regulation of EPOKA University "On Student Discipline" |
| Attendance Requirement: | 75% |
| Course Description: | Theory and Economic application f the linear multiple regression model, identification and structural estimation in simultaneous models, analyzing of economic policy and forecasting. |
| Course Objectives: | This econometrics course is designed to provide students with the essential skills and knowledge necessary for conducting rigorous research in economics and finance. By delving into advanced econometric practices and models, students will gain the expertise needed to analyze complex economic phenomena using empirical data. Through the utilization of the STATA and R econometric packages, students will engage in hands-on learning experiences, mastering techniques for data manipulation, estimation, and interpretation of results. Moreover, laboratory sessions will offer students practical opportunities to apply theoretical concepts to real-world datasets, fostering a deeper understanding of econometric methodologies. By the course's conclusion, students will emerge equipped with the ability to conduct independent research, critically evaluate empirical findings, and contribute meaningfully to the advancement of economic and financial knowledge. |
|
BASIC CONCEPTS OF THE COURSE
|
| 1 | Dependent Variables and Binary Outcomes |
| 2 | Measurement Error and Proxy Variables |
| 3 | Panel Data Analysis |
| 4 | Instrumental Variable |
| 5 | Difference-in-Differences (DID) Methods |
| 6 | Resampling Methods |
| 7 | Simultaneous Equations |
| 8 | Multinomial Variable Models |
| 9 | Regression Discontinuity Design (RDD) |
|
COURSE OUTLINE
|
| Week | Topics |
| 1 | Introduction to the: Course, Syllabus, Textbook and Evaluation Method. |
| 2 | Binary Dependent Variable, chapter 14 at Cameron and Trivedi, pg 463: This chapter considers the simplest case of binary outcomes, where there are two possible outcomes. Examples include whether or not an individual is employed and whether or not a consumer makes a purchase |
| 3 | Proxy variables and measurement error, chapter 9 Wooldrige, pg.294: In this chapter, we return to the much more serious problem of correlation between the error, u, and one or more of the explanatory variables. |
| 4 | Instrumental Variable, chapter 15 Wooldrige and chapter 4 Angrist and Pischke, pg.495: IV methods were pioneered to solve the problem of bias from measurement error in regression models. One of the most important results in the statistical theory of linear models is that a regression coefficient is biased towards zero when the regressor of interest is measured with random errors. Instrumental variables methods can be used to eliminate this sort of bias. |
| 5 | Panel Data and chapter 21 cameron and trivedi, pg. 697: panel data, also called longitudinal data, contain periodically repeated observations of the same subjects, they have a large potential for resolving issues that cross-section models cannot satisfactorily handle. |
| 6 | Difference-in-Difference Methods, chapter 9 in Cunningham: The difference-in-differences design is an early quasi-experimental identification strategy for estimating causal effects that predates the randomized experiment by roughly eighty-five years. In this chapter, I will explain this popular and important research design both in its simplest form, where a group of units is treated at the same time, and the more common form, where groups of units are treated at different points in time. |
| 7 | Simultaneous equations, chapter 16 Wooldridge, chapter 2 C&T, pg.534: The objective is to bring into the discussion several key ideas and concepts that have more general relevance. Although the analysis is restricted to linear models, many insights are routinely applied to nonlinear models. |
| 8 | Limited dependent variable models, chapter 17 Wooldrige, pg.559: we studied the linear probability model, which is simply an appli<.>ation of, the multiple regression model to a binary dependent variable. A binary dependent vamable is an example of a limited dependent variable (LDV). Logit/Probit |
| 9 | Midterm |
| 10 | Multinomial variable models, chapter 17 Wooldrige and chapter 15 C&T, pg.559: The preceding chapter considered models for discrete outcome variables that can take one of two possible values. Here we consider several possible outcomes, usually mutually exclusive. |
| 11 | Regression Discontinuity Design, chapter 6 Cunningham: The reason RDD is so appealing to many is because of its ability to convincingly eliminate selection bias. This appeal is partly due to the fact that its underlying identifying assumptions are viewed by many as easier to accept and evaluate. |
| 12 | Resampling Methods, chapter 10 in Hansen: Dealing with Bootstrap Algorithm/Regression and Cross Validation |
| 13 | Project Presentations |
| 14 | Project Presentations |
| Prerequisite(s): | Econometrics I |
| Textbook(s): | Wooldridge J.M.(2015). Introduction to Econometrics. Cengage Learning Cameron, A. C., & Trivedi, P. K. (2005). Microeconometrics: methods and applications. Cambridge university press. Angrist, J. D., & Pischke, J. S. (2009). Mostly harmless econometrics: An empiricist's companion. Princeton university press. Cunningham, S. (2021). Causal inference: The mixtape. Yale university press. Gaillac, C., & L’Hour, J. (2019). Machine learning for econometrics, lecture notes ensae paris. |
| Additional Literature: | Lectures, Practical Sessions, Exercises |
| Laboratory Work: | Yes |
| Computer Usage: | Yes (Stata and R Software) |
| Others: | No |
|
COURSE LEARNING OUTCOMES
|
| 1 | Develop proficiency in advanced econometric techniques and methodologies, spanning topics such as binary dependent variables, measurement error, instrumental variables, panel data analysis, differencein-differences methods, simultaneous equations, limited dependent variable models, multinomial variable models, regression discontinuity design, and resampling methods. |
| 2 | Cultivate the ability to critically analyze econometric models and methodologies, identify potential sources of bias or error, and implement appropriate strategies to address them. |
| 3 | Gain hands-on experience with econometric software and tools, such as STATA and R, in conducting empirical analysis, interpreting results, and drawing meaningful conclusions from economic and financial data. |
| 4 | Develop the skills necessary to design and execute empirical research studies in economics and related fields, including formulating research questions, selecting appropriate econometric methods, collecting and analyzing data, and communicating findings effectively. |
| 5 | Deepen understanding of causal inference methods, including quasi-experimental designs and identification strategies, to estimate causal effects in observational data settings and mitigate biases inherent in econometric analysis. |
| 6 | Recognize the interdisciplinary nature of econometrics and its applications across various domains, including economics, finance, public policy, and social sciences, and appreciate the broader implications of econometric analysis for decision-making and policy formulation. |
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COURSE CONTRIBUTION TO... PROGRAM COMPETENCIES
(Blank : no contribution, 1: least contribution ... 5: highest contribution) |
| No | Program Competencies | Cont. |
| Bachelor in Business Informatics (3 years) Program | ||
| 1 | Identify activities, tasks, and skills in management, marketing, accounting, finance, and economics. | 5 |
| 2 | Apply key theories to practical problems within the global business context. | 5 |
| 3 | Demonstrate ethical, social, and legal responsibilities in organizations. | 5 |
| 4 | Develop an open minded-attitude through continuous learning and team-work. | 5 |
| 5 | Integrate different skills and approaches to be used in decision making and data management. | 5 |
| 6 | Combine computer skills with managerial skills, in the analysis of large amounts of data. | 5 |
| 7 | Provide solutions to complex information technology problems. | 5 |
| 8 | Recognize, analyze, and suggest various types of information-communication systems/services that are encountered in everyday life and in the business world. | 5 |
|
COURSE EVALUATION METHOD
|
| Method | Quantity | Percentage |
| Homework |
2
|
5
|
| Presentation |
2
|
10
|
| Project |
2
|
5
|
| Term Paper |
1
|
30
|
| Final Exam |
1
|
30
|
| Total Percent: | 100% |
|
ECTS (ALLOCATED BASED ON STUDENT WORKLOAD)
|
| Activities | Quantity | Duration(Hours) | Total Workload(Hours) |
| Course Duration (Including the exam week: 16x Total course hours) | 16 | 3 | 48 |
| Hours for off-the-classroom study (Pre-study, practice) | 16 | 3 | 48 |
| Mid-terms | 1 | 16 | 16 |
| Assignments | 1 | 10 | 10 |
| Final examination | 1 | 18 | 18 |
| Other | 1 | 10 | 10 |
|
Total Work Load:
|
150 | ||
|
Total Work Load/25(h):
|
6 | ||
|
ECTS Credit of the Course:
|
6 | ||
|
CONCLUDING REMARKS BY THE COURSE LECTURER
|
|
Each week a randomly picked student has to prepare a presentation on the reading list they will find on Google Classroom. Each person will need to present once. In addition to the short presentations, each paper will be assigned to a student to write and present a “referee report”. The aim of the referee report is to be critical: Give a short overview of the paper; think about an alternative explanation/story, and criticize the empirical identification strategy of the paper (and try to suggest an alternative one). |