Concordia University - ECON 421: Econometrics I (Solutions)

- Concordia University's ECON 421: Econometrics I equips you with the tools to analyze economic data and build models. The course starts with foundational concepts like linear regression and explores various data types - cross-sectional/ time series. It then dives into more advanced topics like OLS estimation, functional forms, incorporating qualitative data, and handling issues like heteroskedasticity and endogeneity. Finally, you'll learn about simultaneous equations models, a powerful tool for analyzing interdependent economic relationships.

✔ (Summary) Main Topics Covered:

• Econometrics & Economic Data: Delving into the field of econometrics, this course sheds light on economic models and econometric models, as well as various types of economic data, such as cross-sectional, time series, pooled cross sections, and panel data.
• Simple Regression Model: The foundation of simple linear regression is explored, which is a statistical method for modeling the relationship between two continuous variables.
• Multiple Regression Analysis: The course delves into multiple regression, a statistical technique for analyzing the relationship between a single dependent variable and multiple independent variables.
• OLS Asymptotics: The asymptotic theory for ordinary least squares (OLS) estimation is covered, including the properties of OLS estimators and their asymptotic distributions.
• Further Issues: Functional Forms and Model Fit: Additional aspects of econometric modeling are explored, such as choosing functional forms and assessing model fit.
• Multiple Regression with Qualitative Analysis: The course discusses how to incorporate qualitative information into multiple regression models, including the use of dummy variables.
• Heteroskedasticity: The issue of heteroskedasticity in regression models is addressed, which arises when the variance of the error term is not constant across observations.
• Specification & Data Issues: The course tackles potential problems with model specification and data quality, as well as strategies for addressing these issues.
• Instrumental Variable & Two-Stage Least Squares (2SLS): The use of instrumental variables and two-stage least squares estimation to address endogeneity issues in regression models is covered.
• Simultaneous Equations Models: The course concludes with the estimation and identification of simultaneous equations models, which involve multiple equations with multiple endogenous variables.