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Steven Proud
เข้าร่วมเมื่อ 11 พ.ค. 2014
Does getting married make you richer? An example of omitted variables bias
This video uses a sample of data from the UK Labour Force Survey, to illustrate omitted variables bias
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Variance of the OLS estimator part 5 - testing hypotheses
มุมมอง 325 หลายเดือนก่อน
Variance of the OLS estimator part 5 - testing hypotheses
Variance of the OLS estimator part 4
มุมมอง 205 หลายเดือนก่อน
Here, we discuss how estimates of the variance of the OLS estimator are constructed
Variance of the OLS estimator part 2
มุมมอง 325 หลายเดือนก่อน
We can use some strong assumptions to construct the variance of the OLS estimator.
Variance of the OLS estimator (Part 1)
มุมมอง 425 หลายเดือนก่อน
The data used here is from the OECD (2019)
Example of estimating OLS with a zero intercept
มุมมอง 255 หลายเดือนก่อน
Example of estimating OLS with a zero intercept
Is the OLS estimator unbiased
มุมมอง 265 หลายเดือนก่อน
In this video, we consider the simple OLS estimator, where the intercept is imposed to be equal to zero, and evaluate whether it is unbiased or not. We consider two very strong assumptions: 1. The model is correctly specified 2. The error term is random compared with the values of X. It is perfectly possible that either (or both) of these assumptions are not true.
Constructing the OLS estimator with a restriction imposed
มุมมอง 325 หลายเดือนก่อน
We can construct the OLS estimator with various restrictions imposed. In this short video, we construct the estimator if we want to impose that the intercept is equal to 4.
Example of testing a hypothesis using the t-distribution
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In this video, we work through a test of a hypothesis when we do not know the true variance of the underlying population.
An unbiased estimator of the population variance
มุมมอง 186 หลายเดือนก่อน
This video builds upon the previous video th-cam.com/video/cHnLMD42Nuw/w-d-xo.htmlsi=eG-0n6KslnmdUo-c to construct an unbiased estimator of the population variance
Biased estimator of population variance
มุมมอง 556 หลายเดือนก่อน
In this video, we analyse a possible estimator for the (population) variance of a random variable, and then show that the proposed estimator provides biased estimates of the population variance
Distribution of the sample mean, when randomly sampling from the uniform distribution
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Distribution of the sample mean, when randomly sampling from the uniform distribution
Example of a Biased estimator
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In this video, we evaluate a biased estimator of the population average, or expectation. The estimator is similar to the sample mean, but rather than dividing the sum of the observations by n, we first multiply by (n 1), and then divide through by the square of n.
Randomly sampling from a Uniform Distribution between -3 and 5
มุมมอง 526 หลายเดือนก่อน
We often need to think about the distribution of samples from a population. In this very simple example, we randomly sample two observations from a random variable X~U(-3, 5). That is, X follows a uniform distribution between -3 and 5. Statisticians often use the sample mean as an estimator of the population average, or expectation. This exercise provides the first step towards thinking about t...
Evaluating a (continuous) joint distribution
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Evaluating a (continuous) joint distribution
Differences between true errors and fitted residuals (OLS)
มุมมอง 57ปีที่แล้ว
Differences between true errors and fitted residuals (OLS)
Deriving an unbiased estimator for the population variance
มุมมอง 557ปีที่แล้ว
Deriving an unbiased estimator for the population variance
"What are the barriers to active learning in Economics?"
มุมมอง 2372 ปีที่แล้ว
"What are the barriers to active learning in Economics?"
How to interpret OLS regression results
มุมมอง 10K3 ปีที่แล้ว
How to interpret OLS regression results
Introduction to type 1 and type 2 errors
มุมมอง 1323 ปีที่แล้ว
Introduction to type 1 and type 2 errors
Thank you so much sir. 4 years after posting this video you are still here helping students improve our understanding. God bless you.
Thank you
Excellent video
Kind of crazy that I never really learnt this. Thanks
👏👏👏👏
This is really soo helpful....very short and to the point ...thank you😊😊
Great explanation
how to improve R square /multiple R value sir?
... Meanwhile me : Wtf is a plim 😂
A simple answer is that a plim (or Probability LIMit) of an estimator is what the estimator converges to as the sample size gets very large (tends towards infinity). A plim is slightly different from a normal limit, because rather than the estimator converging , we are looking at convergence with probability. Intuitively, if the probability limit of an estimator is equal to (say) 2, that means that the probability that the estimates you observe are equal to 2 converges to 1 as the sample tends towards infinity.
does this not work on mac, have a similar add-in without regression
Omg this was to the point and detailed thank you so much!!!😊
I have a 6 foot Thunderbolt 3 cable, that I am using since TB3 is out to connect an eGPU, which was $59. CalDigit now has a TB4 version of this cable for $64. My $60 cable is working for 7 years now with no issues. No need for a $130 'miracle'. The chipset btw in these cable is likely also. no miracle, but a line driver, like a repeater, to accommodate losses.
what are you on about?
Nicely explained.
Great job! Straight to the point...
You're amazing <3
Goat
please explain what the results that you got mean
Big help for my math CW for computer science BsC 😭🙏🏾
That's very helpful! Thank you for the explanation.
Thanks Steven!
It was of great help to me🙏🙏🙏🙏
One question, for plim(f(A)) = f(plim(A)), do we need f to be a continuous function?
can someone help me , why p value Significance F always appear #NUM!
This may be due to insufficient observations. If the F-statistic cannot be evaluated, then you will end up with these sorts of errors. However, the context of the error matters.
Thank you. Very helpful
Great
why ^B is equal to B1 + sum cov/var? From where it comes from? thanks :)
First of all you must clear the wage which is your independent variable and the check it’s relationship of independent variable
thank you so much, simple and easy
question - to show that your estimator is not just consistent, but also unbiased, don't you need the stronger zero conditional mean assumption?
Yes - that's absolutely correct. To show that the OLS estimator is consistent , all it needs to be true is that cov(u, X)=0. However, for the slope estimator to be unbiased, you need that E(u|{X})=constant (for all coefficients to be unbiased, you need this to be equal to zero). The zero covariance condition automatically follows from the zero conditional mean, , the reverse is not true.
Legendary, thanks!
Underrated video that save my life
Awesome!
Why we cannot use linest?
I've clicked on so many useless videos that never ONCE mentioned the add-in function. You are a lifesaver.
You have explained it very clearly. Thank you
THE BEST!!!
Super clean explanation, thank you for making this.
Good explanation! Thank you.
wow, this video was so simple, but so very helpful!
thank you so much sir for this tutorial
Amazing! Thanks,
Thank you
This was very helpful. Thank you so much:):):):)
thank you😁. exactly what i needed
thanks
Thanks a lot!
Sir,Can i get your mail-id.. so that if i any confusion about regression i can ask Sir
this truly helps. stands out of countless videos
Can't thank you enough 🙏.I tried downloading Eviews and exhausted,but your Excel trick helped me very much in my assignment
Thanks a lot Steven
Thank you so so so much!