Study Session 3: Quantitative Methods for Valuation
In Reading 9: Correlation and regression, Practice Problems, problem no. 11, the solution says the absolute value -2.3017 is greater than the critical value 1.96. How?
I wanna know if that’s right, and if so how exactly is it being interpreted because if it were 2.3017 i.e positive, one can say its greater than 1.96 but this one’s negative and even if we consider -1.96, -2.3017 is still smaller than the critical value.
Hi Guys, I just passed my level 1. A little above the border line. My weakest subjects were Quants followed by Portfolio management (both below 50%) and Fixed Income 50%. I am just wondering how much of the topic of these 3 subjects fromm level 1 is repeated in level 2. If it is a lot of ground to cover, i should perhaps take re-read or even take classes for them. Do not want to be struggling in the end. Thanks for support.
Okay guys so I get the Durbin Watson Test is used for identifying autocorrelation, and we reject the null if it’s either below the low DW stat or above the high DW stat. But I have something in my notes/memory about accepting/rejecting based on whether DW is greater than, less than or = 2.
Can someone clarify these two for me please?
Topic Test Quant: Hamilton’s conclusion that multicollinearity is not a problem, is most likely based on the observation that:
I just came across this question in the quant section:
Hamilton’s conclusion that multicollinearity is not a problem, is most likely based on the observation that:
- model F-value is high and the p-values for the S&P 500 and SPREAD are low.
- correlation between the S&P 500 and SPREAD is low.
- model R2 is relatively low.
Correct answer would have been (2) because correlation b/w S&P and Spread is low (given in the question).
Are we to know the level of significance values or are we gonna be given in the exam
I came across an example where “the p-value of 0.33 is high, thus xyz will fail to reject null hypothesis.
In the absence of a significance level to compare with, what would be a reasonable demarcation of p value to conclude as above?
Why would a t stat of 8.617 and a p value of 0.000000 mean the coefficient is statistically significant?
I am little confused with the multicollinearity and misspecification thing…
When two independent variables are correlated its multicollinearity and to correct that schweser text says omit one or more correlated variables…
how can I detect unit root/non-stationarity?
let’s say that there is one dependent variable and two independent.
if we can reject the two independent variable, does this mean that unit root exists for the dependent variable?
additionally, what is the impact on the result?
thanks in advance
Hope studying is going well for everyone…
I wanted to clarify some things from quant. I will put my conclusions below, and if anything is wrong id love for some of you quant gurus to chime in (because i absolutely suck at quant)
1. Heteroskedasticity- coefficients will still be consistent, but biased. standard errors will be biased and inconsistent
2. Serial correlation- coefficients will still be consistent, but biased. standard errors will be biased and inconsistent