you look at monthly data and come up with the equation xt = b0 + b1 + xt-1 + b2 + xt-2. you find that the residuals have a significant ARCH process. The best solution to this is to: A) re-estimate the model using only an AR(1) specification. B) re-estimate the model using a seasonal lag. C) re-estimate the model with generalized least squares.

C

what is ARCH? i dont remember coming across this term in Quant. The did discuss this in one footnote in FI study sessions tho.

Auto regression

auto-regressive conditional heteroskedasticity

Movements of the business cycle are likely to have the greatest effect on a: A) cyclical industry in the pioneer stage. B) growth industry. C) mature industry.

A) cyclical industry in the pioneer stage

hmm, between A and B… yeah i think i’d go A also out of those. C was right on mine- If the residuals have an ARCH process, then the correct remedy is generalized least squares which will allow you to better interpret the results.

C?

C is correct. I selected A and got it wrong.

here’s another good little review q- n = 60 observations on variables X and Y in which the r = 0.42. If the level of significance is 5%, we: A) cannot test the significance of the correlation with this information. B) conclude that there is statistically significant correlation between X and Y. C) conclude that there is no significant correlation between X and Y.

dude post the answer from Schweser

B for bannis’ second

actually that makes sense swaption. crap. who said these were from schweser? we’re just making them up off of the top of our head.

t-stat= 3.5245617 > t-crit. Reject Null Ans is B?

bannis 2nd: B) conclude that there is statistically significant correlation between X and Y. r*sqrt(n-2)/sqrt(1-r^2) = 3.52 > 1.96 (58 > 30 so can use z-approximation).

Here’s a softy to quench the summer. Which of the following factors is least likely to affect the fortunes of an industry in the pioneer stage? A) Government. B) Demography. C) Social change.

n = 60 observations on variables X and Y in which the r = 0.42. If the level of significance is 5%, we: A) cannot test the significance of the correlation with this information. B) conclude that there is statistically significant correlation between X and Y. C) conclude that there is no significant correlation between X and Y. to do this you need to remember the formula for testing statistical significance of R. and I don’t remember it. thank you. can someone pls paste it?

C) Social change. ???

yep. i forget the t = r x sq root (n-2)/ sq root (1 - r^2) formula more than i’d like to admit. i just did a 20 q qbank after not looking at quant in a while and totally booted this easy one. bad girl.