FRM Part I · FRM Exam Part I · Regression with Multiple Explanatory Variables
A researcher models bond spread changes using quarterly data and wants to capture seasonal effects with an intercept for each of the four quarters. The model includes a constant. How many quarterly dummy variables should be included to avoid perfect multicollinearity?
Three dummy variables should be included. With an intercept in the model, four quarterly dummies would sum to the constant column, causing perfect multicollinearity. Omitting one quarter makes it the base category, and the other coefficients are interpreted relative to it.
- A3Correct
- B4
- C2
- D1
Explanation
With a constant included, using all four dummies makes their sum equal the constant column, creating the dummy variable trap. Including three dummies leaves one quarter as the base category.
Did you get it right without looking?
One question tells you little. A timed set on Regression with Multiple Explanatory Variables shows your real accuracy, how long you take and where you lose marks.
More Regression with Multiple Explanatory Variables questions
- A model of portfolio returns omits a relevant variable X2 that is positively correlated with the included variable X1, and X2 has a positive…
- In a multiple regression, a researcher finds that two explanatory variables have a sample correlation of 0.98, the overall F-test is highly …
- Which statement about the OLS estimators in a multiple regression is correct when the classical assumptions hold except that the error varia…
- An analyst omits a relevant explanatory variable that is positively correlated with an included variable, and whose true coefficient is posi…
- Model A has k = 2 regressors and R-squared of 0.500. Model B adds 3 more regressors (k = 5) and has R-squared of 0.530. Both use n = 31 obse…
- An analyst regresses a firm's monthly excess return (Y) on the market excess return (X1) and omits a relevant variable X2 that is positively…