ECONOMETRICS
Complete Flashcard Set
80+ Cards covering all key topics
Definitions • Formulas • Concepts • Diagrams
Topics Included:
OLS Regression • Measures of Fit • LSA Assumptions
Hypothesis Testing • OVB • Panel Data • IV/2SLS
Binary Outcomes • Time Series • Forecasting
Print double-sided, cut along dotted lines
, SECTION 1: OLS Regression Basics
What is the Population Regression Function (PRF)? The PRF shows the expected value of Y given X:
E(Y|X) = β₀ + β₁X
β₀ = intercept (value of Y when X=0)
β₁ = slope (change in Y for 1-unit change in X)
What is the full regression model with the error term? Yᵢ = β₀ + β₁Xᵢ + uᵢ
Yᵢ = dependent variable (outcome)
Xᵢ = independent variable (regressor)
uᵢ = error term (unobserved factors)
⚠ uᵢ captures everything affecting Y not included in X
What are the OLS estimator formulas for β̂₀ and β̂₁? β̂₁ = Σ(Xᵢ-X̄)(Yᵢ-Ȳ) / Σ(Xᵢ-X̄)²
β̂₀ = Ȳ - β̂₁X̄
OLS minimizes the sum of squared residuals: min Σûᵢ²
What is the difference between the ERROR TERM (u) Error (uᵢ) Residual (ûᵢ)
and the RESIDUAL (û)? Population concept Sample concept
Unobservable Observable
Yᵢ - E(Y|X) Yᵢ - Ŷᵢ
⚠ We use residuals to estimate properties of errors
Complete Flashcard Set
80+ Cards covering all key topics
Definitions • Formulas • Concepts • Diagrams
Topics Included:
OLS Regression • Measures of Fit • LSA Assumptions
Hypothesis Testing • OVB • Panel Data • IV/2SLS
Binary Outcomes • Time Series • Forecasting
Print double-sided, cut along dotted lines
, SECTION 1: OLS Regression Basics
What is the Population Regression Function (PRF)? The PRF shows the expected value of Y given X:
E(Y|X) = β₀ + β₁X
β₀ = intercept (value of Y when X=0)
β₁ = slope (change in Y for 1-unit change in X)
What is the full regression model with the error term? Yᵢ = β₀ + β₁Xᵢ + uᵢ
Yᵢ = dependent variable (outcome)
Xᵢ = independent variable (regressor)
uᵢ = error term (unobserved factors)
⚠ uᵢ captures everything affecting Y not included in X
What are the OLS estimator formulas for β̂₀ and β̂₁? β̂₁ = Σ(Xᵢ-X̄)(Yᵢ-Ȳ) / Σ(Xᵢ-X̄)²
β̂₀ = Ȳ - β̂₁X̄
OLS minimizes the sum of squared residuals: min Σûᵢ²
What is the difference between the ERROR TERM (u) Error (uᵢ) Residual (ûᵢ)
and the RESIDUAL (û)? Population concept Sample concept
Unobservable Observable
Yᵢ - E(Y|X) Yᵢ - Ŷᵢ
⚠ We use residuals to estimate properties of errors
