# Durbin-Watson

Does anyone know if this will be (of if it was) tested? I’ve looked at the LOS and it doesn’t mention it anywhere… but it does seem pretty important… Thanks, ReTab

ReTabulate, I think you’re right. The only related item is LOS 12.g: discuss the types of heteroskedasticity and the effects of heteroskedasticity and serial correlation on statistical inference; It was tested last year, but the LOS then was LOS 12.f: explain the assumptions of a multiple regression model, describe heteroskedasticity and serial correlation, and discuss their effects on statistical inference, explain how to test and correct for heteroskedasticity and serial correlation, and calculate and interpret a Durbin-Watson statistic It looks to me like this year’s multiple regression LOS have been tamed, maybe due to the addition of the time series section. A bit of charity from CFAI.

Given that it’s a test for violations of OLS assumptions, I’d expect it to be tested. Not so much that you’d calculate it, but you might well have to interpret it (i.e. given a DW statistic and criutical values, state whether there’s positve, negative, or no autocorrelation). It could come under LOS 12d, g, or i

Maybe, but none of the command words in any of the LOS lead me to believe it will. Anyway, I intend to be ready, just in case. http://www.cfainstitute.org/cfaprog/courseofstudy/commandwords.html http://cfainstitute.org/cfaprog/resources/leveliioutline.html Reading 12: Multiple Regression and Issues in Regression Analysis The candidate should be able to: a. formulate a multiple regression equation to describe the relation between a dependent variable and several independent variables, determine the statistical significance of each independent variable, and interpret the estimated coefficients and their p-values; b. formulate a null and an alternative hypothesis about the population value of a regression coefficient, calculate the value of the test statistic, determine whether to reject the null hypothesis at a given level of significance, using a one-tailed or two-tailed test, and interpret the result of the test; c. calculate and interpret 1) a confidence interval for the population value of a regression coefficient and 2) a predicted value for the dependent variable, given an estimated regression model and assumed values for the independent variables; d. explain the assumptions of a multiple regression model; e. calculate and interpret the F-statistic, and discuss how it is used in regression analysis, define, distinguish between, and interpret the R2and adjusted R2in multiple regression, and infer how well a regression model explains the dependent variable by analyzing the output of the regression equation and an ANOVA table; f. formulate a multiple regression equation using dummy variables to represent qualitative factors, and interpret the coefficients and regression results; g. discuss the types of heteroskedasticity and the effects of heteroskedasticity and serial correlation on statistical inference; h. describe multicollinearity and discuss its causes and effects in regression analysis; i. discuss the effects of model misspecification on the results of a regression analysis, and explain how to avoid the common forms of misspecification; j. discuss models with qualitative dependent variables; k. interpret the economic meaning of the results of multiple regression analysis, and critique a regression model and its results.

cool… thanks for the info and help guys!