A key assumption for the identification of the ceteris paribus effect in a multiple linear regression model, Yt = XtB + Et Homoskedasticity: Zero unconditional mean: Zero conditional mean: Simple random samplin: V (et) = 0² E(et |Xt) = 0 E(et | Xt) = 0 Cov(Et, Es) = 0
A key assumption for the identification of the ceteris paribus effect in a multiple linear regression model, Yt = XtB + Et Homoskedasticity: Zero unconditional mean: Zero conditional mean: Simple random samplin: V (et) = 0² E(et |Xt) = 0 E(et | Xt) = 0 Cov(Et, Es) = 0
Chapter1: Making Economics Decisions
Section: Chapter Questions
Problem 1QTC
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Transcribed Image Text:A key assumption for the identification of the ceteris paribus effect in a multiple linear regression model,
Yt = XtB + Et
Homoskedasticity:
Zero unconditional mean:
Zero conditional mean:
Simple random samplin:
V (et) = 0²
E(et |Xt) = 0
E(et | Xt) = 0
Cov(Et, Es) = 0
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Step 1
Multiple linear regression model is a model of several explanatory variables for predicting the outcomes of other response variables. Now there are assumptions on MLRM which we have to maintain in the model.
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