# DoubleDip

Hey, you sent out some great stuff on time series y’day - do you have a complete summary? I don’t have enough time to cover it tonight - am finding it tough to digest Schweser/CFAI explanations at this stage. I found your guide to be much simpler to follow. any help would be appreciated. thanks

wowww good timing i just started to review quant for the final time

Some things I would remember: definitely t test (b - bo)/s, standard deviation is the square root of variance, how to compute covariance, F test, what is numerator and denominator for F test (k, n-k-1), careful with the lookup tables (F test is one tailed, t test might be two tailed), know RSS, SSE, what to divide by to get means, (number of predictor variables in regression = k so that’s the df for RSS, and n-(k+1) for unexplained variance), F = MSR/MSE … what else. correlation = sqrt of R… know adjusted R^2 (R^2 always increases when you add new explanatory variables even if they have no power to explain, but adjusted R^2 includes a penalty for a new variable, R^adj may or may not increase when you add a new variable). I would be able to construct an ANOVA table too. I would do that first. And how to compute confidence interval for the predicted variable y. Also know that if the t value you calculate is greater than the critical value you look up, that is strong evidence of significance. You can quickly calculate the t value from your coefficients by taking the coefficient and dividing by the error. Then, you can quickly determine whether significant just by comparing to t critical. If I look up a critical t value of 2 and I have calculated t statistics of 1.9, 2.5, and 4, the first one is statistically INSIGNIFICANT and the last two are significant. And, the best estimate for the expected value of a variable is the mean.

wait im confused when you say you can use the DW to test for serial correlation of the error terms, and then later you say you are not supposed to use DW to test for serial correlation between the predicted model and actual data. My quant is very weak, though I don’t recall that being within the scope of the LII exam. Could you please explain?

Doubliedip, I am not 100% clear about first differencing…it is still a little confusing for me. For example, which one below do you consider first differencing? Suppose the exam ask you to do first differencing, which one do you choose? My understading is that the first one is for testing unit root (it is also first differencing, but only for detecting unit root), the second one is what you need to build a correct model after you detected something to have a unit root. Which is correct for 1st differencing: x(t) - x(t-1) = b(0) + (b1-1)x(t-1)+e(t) x(t)-x(t-1) = b(0) + b1(x(t-1)-x(t-2)) + e(t)

As stated above, just remember that you can’t use DW for autoregressive models. You must use a t-test in that situation: r/(1/T^.5)).

ahh. Okay. DW for time series…

remember folks that for multiple regressions, a positive serial correlation gives you type I errors. I’m not sure if we have to change the model if there is negative serial correlation. Anyone know? That was a great post DoubleDip

Excellent timing doubledip…time series was kicking my a\$\$ and we know we’ll see at least something on it tomorrow.

PhillyBanker Wrote: ------------------------------------------------------- > ahh. Okay. DW for time series… Careful here, I think you can still use DW for trend models, but you can use it for AR time series models.