Your textbook modifies the four assumptions for the multiple regression model by adding a new assumption. This represents an extension of the cross-sectional data case, where errors are uncorrelated across entities. The new assumption requires the errors to be uncorrelated across time, conditional on the regressors as well $\left( \operatorname { cov } \left( u _ { i t } , u _ { i s } \mid X _ { i t } , X _ { i s } \right) = 0 \right.$ for $t \neq s$. (a)Discuss why there might be correlation over time in the errors when you use U.S.state
panel data.Does this mean that you should not use OLS as an estimator?

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The Answer of Your textbook modifies the four assumptions for the multiple regression model by adding a new assumption. This represents an extension of the cross-sectional data case, where errors are uncorrelated across entities. The new assumption requires the errors to be uncorrelated across time, conditional on the regressors as well $\left( \operatorname { cov } \left( u _ { i t } , u _ { i s } \mid X _ { i t } , X _ { i s } \right) = 0 \right.$ for $t \neq s$. (a)Discuss why there might be correlation over time in the errors when you use U.S.state
panel data.Does this mean that you should not use OLS as an estimator?

Your Textbook Modifies the Four Assumptions for the Multiple Regression (cov⁡(uit,uis∣Xit,Xis)=0\left( \operatorname { cov } \left( u _ { i t } , u _ { i s } \mid X _ { i t } , X _ { i s } \right) = 0 \right.(cov(uit​,uis​∣Xit​,Xis​)=0

Your Textbook Modifies the Four Assumptions for the Multiple Regression $\left( \operatorname { cov } \left( u _ { i t } , u _ { i s } \mid X _ { i t } , X _ { i s } \right) = 0 \right.$