Very Informative and short lectures; Thanks for the whole Regression part. Please upload lectures for at least one more subject of level 2 i.e. FRA. It would be very helpful. thanks once again for such a wonderful lectures :)
Other topics are available via a paid subscription of $5 per month. Check out the channel for demo of other topics and for details about the subscription
Sir as you mentioned in the video, are there going to be any summary videos updated on this channel for all subjects? It would be of great help in brushing up all the concepts
Sir if we are able to predict the variance in ARCH does this not violate the assumption or does that not become statistically insignificant. So in this case what is the benifit of predicting future variance
Even after correcting random walk with first differencing, model will not be accurate right? It is covariance stationary but it is not specified, because b0 and b1 are zero.
With first differencing we change the model itself including B0 and b1 so they might not always be 0. This makes the results from model usable for interpretation. Model might not be most accurate, but its results are usable for analytical purposes
Hi sir, thank you for these videos , great explanation. Just wanted to confirm, does time series chapter (i.e part 1 and part 2) covers all the topics of time series
ARCH is a specific situation where our error terms from one time period to next are related, so to detect this we have to run a regression of two error terms to find out dependency on each other. Variance is simply square of error term in regression because, error term itself is difference of actual and predicted, which is same as calculating difference of any value from expected mean. Hope this clarifies
I can't thank you enough...no doubts left..so much clarity
Was so confused after reading the content. The entire regression series really helped in clearing out the doubts.
Thank you so much!
Thanks so much for this. Appreciate it.
Thanks a lot for such a valuable insights. You made the topic very easy which cant be grasped through plain reading.
You came like a massiah to me before the exam!
This was helpful...you have made a difficult topic look easy... Thanks for all the efforts..
You are such a gem ❣️❣️
Helpful as always!!!
Very Informative and short lectures; Thanks for the whole Regression part.
Please upload lectures for at least one more subject of level 2 i.e. FRA.
It would be very helpful.
thanks once again for such a wonderful lectures :)
Thank you, sir. It was really helpful
Great Work ! Loved your regression series. Looking for other video series to, way to go!
Other topics are available via a paid subscription of $5 per month. Check out the channel for demo of other topics and for details about the subscription
At 8:05 B1 = 0. You might wanna edit that part. I am lucky to receive your teaching btw.
Sir as you mentioned in the video, are there going to be any summary videos updated on this channel for all subjects? It would be of great help in brushing up all the concepts
Sir if we are able to predict the variance in ARCH does this not violate the assumption or does that not become statistically insignificant. So in this case what is the benifit of predicting future variance
31:00 ARCH onwards
how to stop the subscription?
Just wanted to confirm, unit root is b1 = 1 right?
Yes
Even after correcting random walk with first differencing, model will not be accurate right? It is covariance stationary but it is not specified, because b0 and b1 are zero.
With first differencing we change the model itself including B0 and b1 so they might not always be 0. This makes the results from model usable for interpretation. Model might not be most accurate, but its results are usable for analytical purposes
Great, please make Fixed income videos too
More videos are in production and there will be a platform update shared soon so stay tuned. Thanks for the feedback
Hi sir, thank you for these videos , great explanation. Just wanted to confirm, does time series chapter (i.e part 1 and part 2) covers all the topics of time series
Hi Sahil, thanks for appreciation. Both parts do cover the entire reading of Time series
Sir, can you please explain how variance of error term is time series as it a sum of squared errors ? ( reference section : ARCH )
ARCH is a specific situation where our error terms from one time period to next are related, so to detect this we have to run a regression of two error terms to find out dependency on each other. Variance is simply square of error term in regression because, error term itself is difference of actual and predicted, which is same as calculating difference of any value from expected mean. Hope this clarifies
Could you please make a video on machine learning and big data projects
In the works already
I still have no idea what probit logit and discriminant are.
Thank you so much. It was very helpful.