CFA2.QM: Rhonda Hamilton Case Scenario, Multicollinearity

[Teaching Assistant]

29/07/2021 at 9:10 am

Học viên: Cô và các bạn ơi, em có làm 1 set quan:

Rhonda Hamilton Case Scenario

Rhonda Hamilton manages the Select Electric Fund. Hamilton is reviewing a research report written by her colleague Brian Ender about the US electric utility industry. Ender’s report includes the results of a regression of the monthly return for an electric utility equity index for the previous 203 months (the dependent variable) against the monthly returns for the S&P 500 Index and the difference between the monthly returns on long-term US government bonds and one-month US Treasury bills (SPREAD) (the two independent variables).

Hamilton has reviewed Ender’s regression results. She agrees that the S&P 500 and SPREAD are reasonable independent variables, but she is not convinced of the validity of Ender’s model. Using Ender’s data, Hamilton tested for and confirmed the presence of conditional heteroskedasticity. She then ran a regression similar to that run by Ender and corrected for conditional heteroskedasticity using robust standard errors (i.e., Hansen’s method). Hamilton’s regression model and relevant statistics are presented in Exhibit 1.

EXHIBIT 1

HAMILTON’S REGRESSION MODEL FOR ELECTRIC UTILITY INDUSTRY

Variable          Coefficient            t-Statistic         p-Value

Constant         −0.000069            −0.013            0.99

S&P 500         0.3625                  6.190             <0.01

SPREAD         1.0264                 4.280             <0.01

R2 0.40

Durbin–Watson statistic 0.84

Correlation between SPREAD and S&P 500 0.30

Hamilton wants to test the null hypothesis that the coefficient on SPREAD is equal to 1 against the alternative hypothesis that it is not equal to 1. She is also interested in how closely the S&P 500 predicts the electric utility index returns. Hamilton wants to use the regression results to address both of these issues. Finally, she wants to determine whether the model has serial correlation. Selected values of the t-distribution are shown in Exhibit 2.

________________________________________

1. Given the information in Exhibit 1, Hamilton’s conclusion that multicollinearity is not a problem, is most likely based on the observation that the:
2. model F-value is high and the p-values for the S&P 500 and SPREAD are low.
3. correlation between the S&P 500 and SPREAD is low.
4. model R2 is relatively low.

Solution

B is correct. Multicollinearity occurs when two or more independent variables (or combinations of independent variables) are highly (but not perfectly) correlated. Correlation between independent variables may be a reasonable indication of multicollinearity in cases in which the regression contains only two independent variables. In Hamilton’s regression model, the correlation between the SPREAD and the S&P 500 variables is only 0.30.

đáp án họ ko test DW mà dựa vào luôn correlation = 0.3 và họ kết luận mức correlation  là low, cũng được ạ

Giảng viên: DW là test serial correlation, còn câu hỏi ở đây là multicoli, 2 cái khác nhau em nhé

Đọc lại cách detect multi nhé, trong đó có rho giữa 2 biến X so với 0.7

21/2/2019