Econometric Model Specification and Diagnostic Testing

Choosing the right model structure is essential for producing valid empirical insights in data analysis and econometrics. This text-based course guides you through core econometric principles, helping you understand how to formulate, evaluate, and refine statistical models with confidence. You will begin with essential terminology and assumptions behind linear regression, then systematically explore how to detect and correct common specification issues such as omitted variables, functional form errors, and endogeneity. What you'll learn: - Understand fundamental econometric definitions, regression assumptions, and model structures - Detect and address functional form misspecification using diagnostic tests - Identify omitted variable bias, multicollinearity, and measurement errors in empirical data - Apply diagnostic procedures for heteroskedasticity, autocorrelation, and non-normality - Evaluate modern robust inference techniques and standard error adjustments - Practice systematic model selection and validation workflows through written analytical exercises The material moves from foundational theory and definitions to structured diagnostic workflows, reinforcing concepts through text explanations and step-by-step analytical examples. This course is designed for beginners and aspiring data analysts who want to build a solid foundation in econometric methodology without prior advanced training. Start reading today to sharpen your empirical research skills.