Summary
In this course, students learn how to conduct exploratory data analysis to summarize and visualize complex datasets, frame and articulate economic and policy-related problems clearly, and critically assess the impact of policies using econometric techniques. By the end of the course, students will be adept at applying these skills to make informed decisions and contribute effectively to policy evaluation and economic strategy development.
Detailed Course Outline
Introduction
The course begins by introducing graduate students in engineering to the fundamental concepts of econometrics, emphasizing practical applications in their field. It aims to develop competency in the following key areas:
- Exploratory Data Analysis (EDA): Techniques for summarizing main characteristics of data, often with visual methods. Understanding data distributions, variability, and presence of outliers. Preparing data for econometric modeling through cleaning and transformation processes.
- Econometric Analysis: Application of statistical and econometric methods to evaluate the effectiveness of policies. Introduction to regression models, hypothesis testing, and causal inference. Using econometric software (STATA) as well as programming languages (R and Python) to perform analyses and interpret the outputs.
- Presentation of Findings: Effective communication of econometric findings to both technical and non-technical audiences. Development of visualizations to represent data and econometric results clearly. Crafting comprehensive reports and presentations that summarize research methodology, analysis, and recommendations.
Course Learning Objectives
- Develop Analytical Skills: Enhance ability to critically analyze economic and policy issues using quantitative methods.
- Problem Solving: Apply econometric tools to investigate and solve policy-related problems.
- Decision Making: Utilize data-driven insights to inform strategic decisions in policy and business contexts.
Methodology
The course employs a dynamic blend of teaching methodologies to ensure a comprehensive learning experience that bridges theoretical knowledge with practical application. This approach includes:
Lectures: Foundational concepts and advanced econometric techniques are taught through interactive lectures. These sessions cover topics such as data exploration, regression analysis, and the interpretation of econometric results, providing the theoretical backbone for applied learning.
Hands-On Labs: Students participate in computer labs where they use econometric software to analyze datasets. These labs focus on practical skills, such as data cleaning, model selection, and diagnostic testing, which are essential for conducting robust econometric analysis.
Project Work: Throughout the course, students engage with real-world datasets to practice their econometric skills. A key project involves a synthetic dataset on traffic where a specific policy has been implemented during the observation period.
Project Scenario: The dataset includes traffic metrics such as accident rates, pollution levels, and congestion statistics before and after the policy implementation. Students are tasked to first detect the nature of the policy enacted—such as the introduction of speed limits, congestion charges, or emissions regulations—through exploratory data analysis.
Econometric Analysis: Once the policy is identified, students apply econometric models to evaluate the effects of this policy on traffic metrics. This involves hypothesis testing to ascertain the impact on accident frequencies, air quality improvements, and traffic flow enhancements.
Interpretation and Reporting: Students interpret their analytical results to determine the efficacy of the policy.
Assessments: The course assessment is a final presentation where students showcase what they learned during the course through a detailed analysis of the traffic dataset.
Target Audience
This course is ideal for graduate students in engineering who are interested in applying quantitative analysis to policy and economic research. It is also suitable for professionals in economics, public policy, and related fields seeking to upgrade their econometric skills.
By integrating theoretical knowledge with practical skills, this course prepares students to tackle complex challenges in their professional careers, armed with powerful tools to influence decision-making processes effectively.