Chapter 4: Model Misspecification

In this section

• Common Forms of Misspecification and Their Consequences
  Explore how incorrectly specifying regression models leads to biased estimates, inefficient results, and poor decisions, with real‑world finance examples for CFA Level II candidates.
• Heteroskedasticity: Detection and Remedies
  Explore the causes, implications, and solutions for heteroskedasticity in regression, including robust standard errors, Weighted Least Squares, and detection tests such as Breusch–Pagan and White’s Test.
• Serial Correlation (Autocorrelation): Issues and Corrections
  Explore the causes, detection methods, and remedies for serial correlation in regression models and time-series data, ensuring accurate inference and robust forecasting in financial analysis.
• Multicollinearity: Identification and Impact
  Learn how to identify, measure, and address multicollinearity in multiple regression models, ensuring stable and reliable coefficient estimates.
• Practical Steps to Avoid Misspecification
  Learn how to properly specify regression models by combining theory, data exploration, and diagnostic tools to prevent common pitfalls in quantitative finance. This section offers a step-by-step guide—from building a solid framework to iterative testing and documentation—ensuring accuracy and credibility in your modeling approaches.
• Item Set Illustrations of Misspecification Scenarios
  Explore comprehensive exam-style scenarios where multiple forms of misspecification—like heteroskedasticity, serial correlation, and multicollinearity—are present. Learn diagnostic testing, step-by-step fixes, and exam-focused best practices for tackling tricky regression models.