Question #23
Reading: Reading 1 Multiple Regression
PDF File: Reading 1 Multiple Regression.pdf
Page: 10
Status: Unattempted
Question
Assume that in a particular multiple regression model, it is determined that the error terms are uncorrelated with each other. Which of the following statements is most accurate?
Answer Choices:
A. This model is in accordance with the basic assumptions of multiple regression analysis because the errors are not serially correlated
B. Unconditional heteroskedasticity present in this model should not pose a problem, but can be corrected by using robust standard errors
C. Serial correlation may be present in this multiple regression model, and can be confirmed only through a Durbin-Watson test. Vijay Shapule, CFA, is investigating the application of the Fama-French three-factor model (Model 1) for the Indian stock market for the period 2001–2011 (120 months). Using the dependent variable as annualized return (%), the results of the analysis are shown in Indian Equities—Fama-French Model. Indian Equities—Fama-French Model Factor Coefficient P-Value Intercept 1.22 <0.001 SMB 0.23 <0.001 HML 0.34 0.003 Rm-Rf 0.88 <0.001 R-squared 0.36 SSE 38.00 AIC –129.99 BIC –118.84 Shapule then modifies the model to include a liquidity factor. Results for this four-factor model (Model 2) are shown in Revised Fama-French Model With Liquidity Factor Revised Fama-French Model With Liquidity Factor Factor Coefficient P-Value Intercept 1.56 <0.001
Explanation
One of the basic assumptions of multiple regression analysis is that the error terms are
not correlated with each other. In other words, the error terms are not serially correlated.
Multicollinearity and heteroskedasticity are problems in multiple regression that are not
related to the correlation of the error terms.
(Module 1.3, LOS 1.i)
Vijay Shapule, CFA, is investigating the application of the Fama-French three-factor model
(Model 1) for the Indian stock market for the period 2001–2011 (120 months). Using the
dependent variable as annualized return (%), the results of the analysis are shown in Indian
Equities—Fama-French Model.
Indian Equities—Fama-French Model
Factor
Coefficient P-Value
Intercept
1.22
<0.001
SMB
0.23
<0.001
HML
0.34
0.003
Rm-Rf
0.88
<0.001
R-squared
0.36
SSE
38.00
AIC
–129.99
BIC
–118.84
Shapule then modifies the model to include a liquidity factor. Results for this four-factor
model (Model 2) are shown in Revised Fama-French Model With Liquidity Factor
Revised Fama-French Model With Liquidity Factor
Factor
Coefficient P-Value
Intercept
1.56
<0.001
SMB
0.22
<0.001
HML
0.35
0.012
Rm-Rf
0.87
<0.001
LIQ
–0.12
0.02
R-squared
0.39
SSE
34.00
AIC
–141.34
BIC
–127.40