Level 6 • Economics

Econometrics I

ECO 311 — Semester V
Course CodeECO 311
TypeB
ECTS5
SemesterV
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Course Description

Econometrics I: Techniques of Econometrics, estimating the basic linear model and hypothesis testing, empirical illustrations to contemporary economic issues

Course Objectives

The goal of this course is to provide students with knowledge of the elements of statistical inference, namely multivariate statistics and multivariate data analysis methods. Students will understand and be able to perform standard descriptive and inferential data analysis, investigate and test relationship between variables as well as specify, use and interpret multivariate models, including regression-type models. The course will also emphasize empirical analysis and focus on the use of data in practice along with the use of available statistical software. An empirical project is an integral part of the course. If possible, economics, financial, and business applications will be chosen during the course to reflect the interests and backgrounds of students.

Key Concepts
  1. Ordinary least squares
  2. Regression
  3. Panel data
  4. Instrumental Variable
  5. Model Specification
  6. Multicollinearity
  7. Heteroskedasticity
  8. Autocorrelation
  9. Time Series
  10. Endogeneity
Course Outline
WeekTopics
1Intro; Evaluation Method; Term Project & A brief Lecture on Types of Data
2Descriptive Summary, Chapter 1 page: 1-30. Data Sources and Graphical Representation of Data 2. Summary Statistics for one Variable 3. Summary Statistics for two (or more) variables
3Regression Analysis, Chapter 1 page: 1-30 1. What is regression analysis? 2. The Classical Model: Ordinary Least Squares (OLS) 3. Learning and Using Regression Analysis/Running Your Own Project 4. Practical issues: Reading Computer Output 5. The Classical Model: Assumptions and Properties 6. Hypothesis Testing
4Ordinary Least Squares, Chapter 2 page: 35-63 1. What is regression analysis? 2. The Classical Model: Ordinary Least Squares (OLS) 3. Learning and Using Regression Analysis/Running Your Own Project 4. Practical issues: Reading Computer Output 5. The Classical Model: Assumptions and Properties 6. Hypothesis Testing
5Assymptotic theory/properties and testing in regression, Chapter 3, page:65-89 1. What is regression analysis? 2. The Classical Model: Ordinary Least Squares (OLS) 3. Learning and Using Regression Analysis/Running Your Own Project 4. Practical issues: Reading Computer Output 5. The Classical Model: Assumptions and Properties 6. Hypothesis Testing
6Model specification, Chapter 4, page:92-108 1. Choosing the Variables in a Regression 2. Including and Interpreting Categorical Variables 3. Choosing the Functional Form
7Model specification and closing functional form, Chapter 4, page:92-108 1. Choosing the Variables in a Regression 2. Including and Interpreting Categorical Variables 3. Choosing the Functional Form
8How to write a research paper and review for midterm, Chapter 11, page:340-358 1. Choosing a Research Project 2. Data Management
9Midterm
10Heteroskedasticity, Chapter 8-10, page: 221-337 1. Outliers 2. Multicollinearity 3. Heteroskedasticity and Autocorrelation 4. Lagged Dependent Variables and Time Series
11Autocorrelation and Lagged dependent variable (Time Series), Chapter 12, page:364-385 1. Outliers 2. Multicollinearity 3. Heteroskedasticity and Autocorrelation 4. Lagged Dependent Variables and Time Series
12Transformations, Endogeneity and Instrumental Variables, Chapter 16, page 465-484: Miscellaneous (some topics could be replaced/extended base on the class response) 1. Discontinuity design, diff-in-diff 2. Sensitivity issues, using robustness approach in the analysis 3. Introduction to non-parametric methods 4. Collecting data – introduction to survey data
13Project Presentations
14Project Presentations + Review
Learning Outcomes
  1. The student should be able to understand the nature of Econometrics and apply the basic econometric techniques in different studies.
  2. The student should be able to estimate and interpret econometric models.
  3. The student should be able to check the robustness and specification of the econometric models.
  4. The student should be able to apply fundamental econometric principles to real life and scientific problems as well as test economic theories.
  5. Understand how to specify and estimate econometric models, including simple linear regression, multiple regression, and time series models, in the context of economic data.
  6. Gain proficiency in hypothesis testing, including testing the significance of coefficients, assessing model fit, and conducting various statistical tests such as t-tests, F-tests, and chi-squared tests.
  7. Learn how to use econometric models for predictive purposes, including forecasting economic variables and evaluating model performance.
  8. Understand the challenges and methods associated with making causal inferences from observational data, including issues related to endogeneity, omitted variables, and instrumental variables.
  9. Gain hands-on experience with econometric software packages (e.g., R, Python, Stata) to perform data analysis and estimate econometric models.
  10. Cultivate critical thinking skills by evaluating the strengths and limitations of econometric models and their applicability to real-world economic problems.
Assessment
MethodQty% Each
Homework45
Midterm Exam(s)125
Project125
Final Exam130
Textbooks

Studenmund (2011): Using Econometrics: A Practical Guide, 6th edition, Pearson. The website for the book (www.pearsonhighered.com/studenmund) includes the datasets mentioned in the book formatted for use in Stata (and other programs). It also includes additional interactive regression learning exercises.

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