Study Session 3: Quantitative Methods for Valuation
Throughout the session, I have seen that series are changed into log ones. Why is this better? If your data cannot become negative, why should it be transformed?
Thanks for the help!
When we are performing a two-tailed t-statistic test , at level of significance ( alpha) = 5 and p value = 2.7 ,
Do we compare p value with alpha or with alpha/2 i.e. will 2.7 be compared with 5 or 2.5?
Can anyone help me understand this fact : if the serie is Covariance stationary then : the absolute value of “b1” from AR(1) is less than 1.
What is the reason for the standard error of coefficient estimates to be biased in conditional heteroskedasticity
What does the standard error of a coefficient mean, and what is the logic of it being impacted due to addition of more independent variable?
What does the assumption
“Variance for the error term is same across all observations” mean?
Are autocorrelations for time series model the correlation between the error term for time t and t-1, t-2, t-3, and so on or are they correlations between t and t-1, t-1, and t-2, t-2 and t-3. Please Let me know thanks!
When to use an F test and when to use a T test for a given regression?
Can an AR model be covariance stationary and be ARCH(1) at the same time? or does ARCH(1) make it covariance non stationary?
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