Nadir J.
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Online Course
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Econometrics I
Learn Econometrics the Practical Way – Hands-On Python Skills for Real-World Data Analysis and Modeling
835 students already have enrolled
Skill level:
Basic
Duration:
2 hours
-
Lessons (2 hours)
- Lessons (2 hours)
CPE credits:
2
CPE stands for Continuing Professional Education and represents the mandatory credits a wide range of
professionals must earn to maintain their licenses and stay current with regulations and best
practices. One CPE credit typically equals 50 minutes of learning. For more details, visit NASBA's
official website: www.nasbaregistry.org
CPE stands for Continuing Professional Education and represents the mandatory credits a wide range of
professionals must earn to maintain their licenses and stay current with regulations and best
practices. One CPE credit typically equals 50 minutes of learning. For more details, visit NASBA's
official website: www.nasbaregistry.org
Accredited:
certificate
What You Learn
- Master Python for econometric analysis using pandas.
- Master data wrangling, including importing, merging, and transformations.
- Conduct descriptive analysis with a focus on outlier detection.
- Perform regression analysis and test for violations of model assumptions.
- Apply panel data and binary choice models with correct model specification.
Topics & tools
regression analysiseconometricsdata analysisfinance theorypythontheory
Your instructor
Track record
Bringing real-world expertise from leading global companies
Academic background
University of Tuebingen
Course OVERVIEW
Description
CPE Credits: 2
Field of Study: Economics
Delivery Method: QAS Self Study
Delivery Method: QAS Self Study
The introduction provides a concise overview of the course's aims and scope. The course is self-contained, including a brief introduction to Python with a focus on Pandas. Then we move into Data Wrangling, focusing on data imports, merging and transformations. Then, we explore our data using Descriptive Analysis. Section 5 introduces Regression Analysis. We will learn that many assumptions are made in the process of regression analysis. Section 6 explores violations of assumptions, including Heteroskedasticity and endogeneity. Tests and methods to address the issues are discussed in detail. Section 7 introduces panel data models, including fixed and random effects. Then, we move on to binary choice models, i.e., explaining yes-or-no events or decisions. We conclude with a section on model specification and parameter stability.
After completing this course, you will be able to use Python for Econometric Analysis with confidence. You will develop a solid understanding of applied Econometrics, including data wrangling, outlier detection, and regression analysis.
This course will be more than sufficient for most applied work. I promise that it is highly applied and provides hands-on experience with numerous practical applications. Enjoy the Joy of Econometrics!
Prerequisites
- Python (version 3.8 or later), Streamlit library, OpenAI API key, and a code editor or IDE (e.g., VS Code or Jupyter Notebook)
- Basic familiarity with Python programming is required.
- Familiarity with basic statistics and linear algebra is helpful but not mandatory.
Advanced preparation
Curriculum
57 lessons
13 exercises
1 exam
- 2. Python - Refresher14 Min
This section provides a beginner-friendly introduction to Python with a focus on data handling and analysis using Pandas. If you are familiar with Python, you may skip this section.
- 3. Data Wrangling14 Min
Handling data is a crucial step in Econometrics. We focus on data structures, merging, and identifiers.
- 4. Descriptive Analysis9 Min
It is essential to detect outliers that bias your empirical models. Additionally, we need to understand how to handle missing values.
- 5. Regression Analysis12 Min
Regression analysis is the predominant tool in Econometrics. We provide a detailed introduction to OLS estimation and interpretation of results.
- 6. Post Estimation Analysis14 Min
In regression analysis, several assumptions are made that need to be tested. This section provides solutions for addressing many standard model assumption violations.
The fun starts after regressions FreeLet's assume FreeMulticollinearity FreeHow to detect multicollinearity? FreeHow to fix multicollinearity? FreeHeteroskedasticity FreeOmitted variables or non-linearity FreeEndogeneity FreePost estimation analyses in Python FreeExercise FreeCoding exercise Free - 7. Panel Data10 Min
This section covers fixed and random effects, as well as the Hausman test, which is used to distinguish between these two specifications.
- 8. Binary Choice Models10 Min
To capture yes-no decisions or events, we require a different set of tools. This section covers Maximum-Likelihood estimation, model predictions, and validation.
- 9. Model Specification10 Min
How do we know that our empirical model is the best? Well, we need to test. This section covers various techniques used in selecting the optimal model.
- 10. Course exam120 Min
Free preview
1.1 Getting started
1 min
1.2 Your instructor
1 min
1.3 Learning objectives
1 min
1.4 Course overview
2 min
1.5 What is Econometrics?
1 min
1.6 Machine learning vs Econometrics
1 min
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