Study Session 3: Quantitative Methods for Valuation
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
How would there be heteroskedasticity when the t-test is greater thant the critical value?
UNIT ROOT TEST FOR NONSTATIONARITY AND THE TEST FOR HETEROSKEDASTICITY
Unit root test statistic
Unit root test critical value at the 5% level of significance
Heteroskedasticity test statistic
Heteroskedasticity test critical value at the 5% level of significance
Why is the answer c when t-test>p-value