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# tutorial4 - ReviewQuestions( :wage=0 1educ 2expr u

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Week 4 Tutorial Exercises Review Questions (these may or may not be discussed in tutorial classes) What do we mean when we say “regress wage on educ and expr ”? Use OLS to estimate the multiple linear regression model: wage = β 0 + β 1 educ + β 2 expr + u . Why and under what circumstance do we need to “control for” expr in the regression model in order to quantify the effect of educ on wage ? We need to control for expr in the model when the partial or ceteris paribus effect of educ on wage is the parameter of interest and expr and educ are correlated. What is the bias of an estimator? bias =E( ) – β j . What is the “omitted variable bias”? It is the bias resulted from omitting relevant variables that are correlated with the explanatory variables in a regression model. What is the consequence of adding an irrelevant variable to a regression model? Including an irrelevant variable will generally increase the variance of the OLS estimators. What is the requirement of the ZCM assumption, in your own words? You must do this by yourself. Why assuming E ( u ) = 0 is not restrictive when an intercept is included in the regression model? When the mean value of the disturbance is not zero, you can always define the new disturbance as the original disturbance minus the mean value, and define the new intercept as the original intercept plus the mean value. In terms of notation, why do we need two subscripts for independent variables?

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tutorial4 - ReviewQuestions( :wage=0 1educ 2expr u

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