Abstract:

Model specification plays a crucial role in determining the validity, reliability, and predictive accuracy of regression models. An incorrectly specified model can lead to biased estimates, inefficiency, and misleading conclusions. This research focuses on identifying and evaluating model specification techniques that enhance the accuracy and interpretability of regression models. The study examines various aspects of model formulation, including the selection of relevant explanatory variables, functional form identification, detection of multicollinearity, and treatment of heteroscedasticity and autocorrelation. Advanced diagnostic tests such as the Ramsey RESET test, Variance Inflation Factor (VIF), and Breusch–Pagan test are employed to assess specification adequacy. The research further explores the integration of automated feature selection and data transformation methods using data science tools to minimize specification errors. Empirical analysis based on real-world datasets demonstrates that properly specified models significantly improve predictive performance and reduce estimation bias. The findings highlight the importance of systematic specification procedures in developing robust regression models for data-driven decision-making across various domains.

Description:

.