Econometrics I: Econometric Methods

Description

This course provides an introduction to econometric methods and their applications. The main workhorse of applied econometrics is the linear regression model and the course will develop its theory and look at a wide range of applications. The course emphasizes intuitive and conceptual understanding as well as hands on econometric analysis using modern computer software on data sets from economics and business. Students learn how to conduct empirical studies, as well as how to analyze and interpret results from other empirical works. We cover a broad range of topics, including a brief review of basic statistics, ordinary least squares estimation and its properties, choice of functional form, departures from standard OLS assumptions, and time series analysis. This is a hand-on course with a focus on applications in economics as well as business. A standard statistical software will be used during computer sessions, no special programming skills are required. Learning Outcomes Upon successful completion, students will have the knowledge and skills to: Define and derive the Ordinary Least Squares (OLS) estimator in the linear regression model, including its mathematical representation and assumptions. Examine the statistical properties of the OLS estimator, such as unbiasedness, efficiency, and consistency. Apply the Central Limit Theorem (CLT) to approximate the statistical distribution of the OLS estimator, explaining the implications for hypothesis testing and confidence intervals. Critically evaluate the strengths and limitations of the OLS estimator, including its sensitivity to outliers, multicollinearity, and omitted variable bias. Demonstrate proficiency in using specialized econometric softwares to summarize and analyse actual economic data, including data visualization and model estimation. Contextualize and critically evaluate the results of empirical analysis, interpreting the findings in light of economic theory and policy implications. Other Information