Correcting autocorrelation timeseries

An analyst modeled the time series of annual earnings per share in the specialty department store industry as an AR(3) process. Upon examination of the residuals from this model, she found that there is a significant autocorrelation for the residuals of this model. This indicates that she needs to: A) switch models to a moving average model. B) revise the model to include at least another lag of the dependent variable. C) alter the model to an ARCH model. ANSWER: (B) revise the model to include at least another lag of the dependent variable. Can someone please shed some light as to where in the books I can find more information on this? I’ve reread Schweser 3 times now and can’t find a reference to this anywhere. Only thing I can find is the use a DW Test for NON-Autoregressive models.

I had read the text book before my schweser arrived and I know it is definitely there in the text book. I still have to revise using Schweser - but I found this at the bottom of page 233 in the book … may be what you are looking for Correcting seasonality. The interpretation of seasonality in the above example is that occupancy in any quarter is related to occupancy in the previous quarter arid the same quarter in the previous year. For example, fourth quarter 2008 occupancy is related to third quarter 2008 occupancy as well as fourth quarter 2007 occupancy. To adjust for seasonality in an AR model, an additional lag of the dependent variable (corresponding to the same season in the previous year) is added to the original model as another independent variable. For example, if quarterly data are used, the seasonal lag is 4; ii monthly data are used the seasonal lag is 12; and so on.

reading 13, section 12 in the CFAI material is a good review for you – a good summary of the process in selecting a model