Endogeneity and Other Concerns

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4 Terms

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Types of endogeneity

There are five types of endogeneity: omitted variable bias, measurement error, misspecification, selection bias, and simultaneity. These types can lead to biased and inconsistent estimates in regression analysis.

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Gauss-Markov assumptions

Assumption MLR.1 (Linear in parameters), MLR.2 (Random sampling), MLR.3 (No perfect collinearity), MLR.4 (Zero conditional mean), MLR.5 (Homoscedasticity)

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Classical linear model (CLM) assumptions

Gauss-Markov + Assumption MLR.6: the error component u is independent of x1, ..., xk and follows a normal distribution with mean 0 and variance σ²u

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Measurement error

A type of endogeneity that occurs when there is a discrepancy between the true value of a variable and the value that is measured, leading to biased estimates in regression analysis.