Which of the following is true about multicollinearity?
1 answer:
Answer:
d. It is best measured using the statistic variance inflation factor (VIF).
Explanation:
Multicollinearity is an important issue in multiple regression model, having many independent/ explanatory variables. Multicollinearity is the situation in which two or more independent variables are highly correlated. It is problematic because it increases the standard error of independent variable coefficient & undermines its statistical significance
Variance Inflation Factor [VIF] is a check & corrective measure of multicollinearity.
- VIF as a multicollinearity check : It quantifies the correlation between one explanatory variable with other explanatory variables.VIF = 1 implies there is no multicollinearity (correlation between independent variables); VIF upto 5 implies there is moderate multicollinearity (correlation between independent variables). VIF > 5 implies high multicollinearity (correlation between independent variables)
- VIF as a multicollinearity correction : Calculating
= σ^2 /
; where TSS = total sum of square of variable j , σ^2 = j variance, R^2 j = R^2 from regressing all other independent variable on variable j
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Answer:
a. increase price in the short run but not in the long run.
Explanation:
A perfectly competitive market is one in which firms in an economy produce similar goods, and use resources that are limited in quantity.
An increase in demand will result in a corresponding increase in price, and results in firms making high profits. In the diagram below it results in a shift of demand from D1 to D2.
In the long run as firms have low barrier to entry more firms enter the market and supply shifts from S1 to S2. There is reduction in prices and profits start to fall. This is illustrated in the second diagram.
