Question #24

of 139 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? 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 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