Question

In: Economics

QUESTION 1 Omitted variable bias is a problem because it prevents the model from being able...

QUESTION 1

  1. Omitted variable bias is a problem because

    it prevents the model from being able to be estimated by ordinary least squares.

    it causes the model to no longer be linear in the parameters.

    it prevents correctly estimating marginal effects.

    it causes perfect multicollinearity.

QUESTION 2

  1. In multiple linear regression analysis, the number of independent variables should be

    as large as possible.

    more than 5.

    guided by economic theory.

    enough to guarantee that statistical significance is achieved.

QUESTION 3

  1. Omitted variable bias occurs when

    always occurs when performing simple linear regression analysis.

    independent variables that should be included in the analysis are not included and those independent variables are related to the variables in the regression model.

    independent variables that should not be included in the analysis are included in the analysis.

    always occurs when performing multiple linear regression analysis.

QUESTION 4

  1. Multiple linear regression analysis determines the

    linear relationship between the dependent variable and many independent variables.

    true value of the population slope coefficient.

    linear relationship between the dependent variable and exactly one independent variable.

    true value of the population intercept.

QUESTION 5

  1. The “holding all other independent variables constant” condition is important

    because it comes at the end of every definition in economics.

    because economists want to know how a change in the dependent variable affects the independent variable.

    to ensure that the error term is correlated with the independent variables.

    to ensure that we are correctly estimating marginal effects.

Solutions

Expert Solution

1. Omitted Variable Bias is a condition where an important variable in the regression is omitted. As a result of this omission, the model rrepresents misspecification.

In order to run OLS, a set of assumptions must be fulfulled for the estimates to be considered BLUE -best, linear, and unbiased estimators.

One integral assumption of OLS is that the error term in the model must be uncorrelated with the independent variables.

When we omit an important variable from the model, the effect of the omitted variable goes into th error term of the equation. And to have the Omitted Variable Bias one of the condition requires that it be correlated with one or more of the explanatory varaibles. This leads to the voilation of the CLRM assumption and OLS estimates thus obtained would be baised and inconsistent.

Thus, option A, it prevents the model from being able to be estimated by ordinary least squares is correct.


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