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Welcome to a comprehensive exploration of econometric modeling, where theory meets practical application. This book offers a step-by-step methodology for students, quantitative analysts, and economists to build and validate econometric models that meet the Best Linear Unbiased Estimator (BLUE) standards. This resource bridges the gap between Ordinary Least Squares (OLS) theory and practical empirical analysis, structured in eleven chapters:
Part I: Establishing the Foundation (BLUE Framework): We defend OLS regression as the optimal linear estimation tool, introducing a framework for data preparation that includes outlier removal and normalization, along with a methodical approach to variable selection to address issues such as Omitted Variable Bias (OVB) and Multicollinearity.
Part II: Modeling, Categorical Coding, and Coefficient Generation: We develop and calibrate models, exploring advanced categorical coding techniques—Dummy, Effect, One-Hot, and Ordinal Coding—to generate clear, statistically significant dollar coefficients for both continuous and discrete variables while ensuring model linearity and explanatory power.
Part III: Validation, Application, and Compliance: The final chapters focus on validating models against core OLS assumptions—Homoscedasticity and Normality—and demonstrate how to ensure that sales samples accurately represent broader populations. We conclude by creating bottom-up, objective valuation grids, ensuring that the derived coefficients are fully validated and compliant with BLUE.
This book is an essential resource for anyone aiming to master the development of statistically defensible, unbiased econometric models.
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