Multiple Regression – 4

Consider a multiple regression model with three independent variables, X1, X2, and X3, and their respective coefficients are estimated as follows:

$Y=3X1+2X2-4X3+5$

Now, suppose you want to assess the presence of multicollinearity using the variance inflation factor (VIF). The calculated VIF for X2 is 3.8, and the calculated VIF for X3 is 4.5.

What is the likely interpretation of these VIF values?

In a multiple regression model with interaction terms, you have two independent variables, X1 and X2, and an interaction term X1*X2. If the coefficient for the interaction term is -0.4, how would you interpret this coefficient?

In a time series multiple regression model, you include a lagged dependent variable as an independent variable. If the coefficient for the lagged dependent variable is 0.6, how would you interpret this coefficient?

In a multiple regression analysis, the White test statistic for heteroscedasticity is 15.4. If the critical value at a 1% significance level is 10.8, what is the decision regarding the null hypothesis of homoscedasticity?

In a multiple regression model, you have two subsamples: Subsample A and Subsample B. The Chow test statistic is calculated as 10.2. If the critical value at a 5% significance level is 8.43, what is the decision regarding the null hypothesis of no structural change?

In a multiple regression model with interaction terms, how do you interpret the coefficient of an interaction term between X1 and X2?

When dealing with time series data, what is the purpose of including lagged dependent variables in a multiple regression model?

What is the primary objective of instrumental variable (IV) regression in the presence of endogeneity?

In the context of multiple regression, what does the White test assess?

What is the purpose of the Chow test in the context of multiple regression?