Assume you believe the demand for a good can be determined by the model:demand i =β 0 +β 1 po i +β 2 pc i +β 3 ps i +ϵ i where demandi measures demand for the good in units, poi measures price in $ for the good, pci measures price in $ for a complement good, psi measures price in $ for a secondary complement good. A researcher gathers some results and feels they should drop variables to solve multicollinearity in order to solve issues in their model. Based only on the residuals you believe the most likely issue is:ASpecification Error from wrong functional formBNo specification error but pure heteroskedasticityCNo specification error but multicollinearityDSpecification Error from omitted variables
Question
Assume you believe the demand for a good can be determined by the model:demand i =β 0 +β 1 po i +β 2 pc i +β 3 ps i +ϵ i where demandi measures demand for the good in units, poi measures price in for a complement good, psi measures price in $ for a secondary complement good. A researcher gathers some results and feels they should drop variables to solve multicollinearity in order to solve issues in their model. Based only on the residuals you believe the most likely issue is:ASpecification Error from wrong functional formBNo specification error but pure heteroskedasticityCNo specification error but multicollinearityDSpecification Error from omitted variables
Solution
Based on the information provided, it seems that the most likely issue is C: No specification error but multicollinearity. This is because the researcher is considering dropping variables to solve multicollinearity, which suggests that the independent variables in the model are highly correlated. This can lead to unstable and unreliable estimates of the regression coefficients. Therefore, the issue is not with the functional form of the model or omitted variables, but with multicollinearity.
Similar Questions
Assume you believe the demand for a good can be determined by the model:demand i =β 0 +β 1 po i +β 2 pc i +β 3 ps i +ϵ i where demandi measures demand for the good in units, poi measures price in $ for the good, pci measures price in $ for a complement good, psi measures price in $ for a secondary complement good. Now assume the true model should have included income where income is income for individual i. Assume the good is normal (increase in income will result in increased demand) and income has no correlation with pc. The bias on β 2 will be:AnegativeBzeroCimpossible to determineDpositiveSUBMIT ANSWER
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