I rushed through the quant material when I took the exam last year. I am no quant expert, but this question has been bugging me for some time.
Why does the CFA decide to go with Durbin Watson to test for serial correlation? I thought DW only tests for first order serial correlation and the inconclusive area of the test makes it a less effective test. Is there a reason behind this?
It does only test for first order serial correlation. It’s a pretty common introductory test for serial correlation. It’s probably to introduce students to the idea of this problem, and if you have some decent wits about you, you’ll ask the question you seem to be getting at…what if there’s something other than first-order autocorrelation?
There are more tests, of course. Durbin and Watson test statistics are developed for higher order autocorrelation: they’re generalized DW statistics (this is the same as ANOVA being a generalization of the independent samples t-test, for example). These generalized DW statistics should be used sequentially, though, as they assume no lower-order autocorrelation; if you’re suspecting third-order, you need to test first and then second order, assuming first order showed insufficient evidence of autocorrelation and then assuming second-order DW showed insufficient evidence of autocorrelation. Read the on PSU masters in applied stats page if you want some more background, poke around on there; it doesn’t cover higher order DW but it talks about other kinds of autocorrelation testing or things to think about. This SAS documentation has some limited background on the generalized DW statistics.
To touch on the “inconclusive” comment: this test is good and bad for having this region. Really, every statistical test is unable to render a “conclusive” answer. This whole "p
Also wanted to say that “inconclusive” is a good way to think of “fail to reject H0” in the sense that you’re not saying “no” but rather saying “I’m not comfortable saying yes without more information”…