Econometrics: Methods and Applications Course Syllabus

Overview (80-120 words) describing structure and time commitment.

Module 1: Simple Regression

Estimated time: 7 hours

• Motivation and representation of simple regression models
• Estimation techniques for simple linear regression
• Evaluation of model fit and assumptions
• Practical applications in economics and business contexts

Module 2: Multiple Regression

Estimated time: 7 hours

• Extension from simple to multiple regression models
• Interpretation of coefficients with multiple predictors
• Model evaluation and goodness-of-fit measures
• Application to real-world data scenarios

Module 3: Model Specification

Estimated time: 7 hours

• Understanding functional form and variable selection
• Model transformation techniques
• Diagnosing misspecification and bias
• Hands-on exercises for correct model building

Module 4: Endogeneity

Estimated time: 7 hours

• Identifying endogeneity and its sources
• Instrumental variables and two-stage least squares
• Testing for endogeneity in regression models
• Correcting bias due to omitted variables or simultaneity

Module 5: Binary Choice Models

Estimated time: 7 hours

• Introduction to models for binary outcomes
• Logit and probit regression estimation
• Interpretation of marginal effects and probabilities
• Real-life applications in policy and business decisions

Module 6: Time Series Analysis

Estimated time: 7 hours

• Motivation and characteristics of time series data
• Model specification for dynamic processes
• Estimation and forecasting techniques
• Addressing autocorrelation and stationarity

Module 7: Case Project

Estimated time: 7 hours

• Integrate econometric methods across modules
• Address practical questions using real datasets
• Submit peer-reviewed project report

Module 8: Building Blocks (Optional)

Estimated time: 7 hours

• Review of matrix algebra fundamentals
• Probability theory and statistical inference
• Core concepts for econometric modeling

Prerequisites

• Familiarity with basic statistics and probability
• Understanding of linear algebra and matrices
• Basic knowledge of statistical software (helpful but not required)

What You'll Be Able to Do After

• Apply simple and multiple linear regression techniques to real data
• Identify and correct model specification issues
• Address endogeneity using instrumental variables
• Analyze binary outcomes with logit and probit models
• Perform time series analysis and forecasting