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MetricsProf
Приєднався 9 сер 2018
Linear regression for economists: The linear model.
This video is part of an online module for my course Basic Econometric at University of Gothenburg, Sweden.
Переглядів: 644
Відео
Statistical properties of OLS: variance, consistency, normality
Переглядів 699Рік тому
This video is part of an online module for my course Basic Econometric at University of Gothenburg, Sweden.
Linear regression for economists: The conditional mean function (CMF)
Переглядів 1 тис.Рік тому
This video is part of an online module for my course Basic Econometric at University of Gothenburg, Sweden.
Causal inference in econometrics
Переглядів 1,3 тис.Рік тому
This video is part of an online module for my course Basic Econometric at University of Gothenburg, Sweden.
Linear regression for economists: The F-test and p-values.
Переглядів 721Рік тому
This video is part of an online module for my course Basic Econometric at University of Gothenburg, Sweden.
Linear regression for economists: The t-test.
Переглядів 704Рік тому
This video is part of an online module for my course Basic Econometric at University of Gothenburg, Sweden.
Introduction to statistical testing for economists
Переглядів 659Рік тому
This video is part of an online module for my course Basic Econometric at University of Gothenburg, Sweden.
Statistical properties of OLS: Simulating sampling error.
Переглядів 773Рік тому
This video is part of an online module for my course Basic Econometric at University of Gothenburg, Sweden.
Population variance and sample variance: a numerical example
Переглядів 1182 роки тому
This video is part of an online module for my course Basic Econometric at University of Gothenburg, Sweden.
Derivation of Wald form of IV estimator
Переглядів 2,7 тис.3 роки тому
This video is part of an online module for my course Basic Econometric at University of Gothenburg, Sweden.
IV regression lecture 3: Two-stage least squares.
Переглядів 2,9 тис.4 роки тому
This video is part of an online module for my course Basic Econometric at University of Gothenburg, Sweden.
IV regression lecture 2: Continuous instrument.
Переглядів 2,6 тис.4 роки тому
This video is part of an online module for my course Basic Econometric at University of Gothenburg, Sweden.
OLS estimator Lecture 1: Modern derivation of OLS estimator from econometrics identification result.
Переглядів 1,8 тис.4 роки тому
This video is part of an online module for my course Basic Econometric at University of Gothenburg, Sweden.
Lab 1: Finding help on Stata problems / Importing data from a csv file
Переглядів 2,3 тис.4 роки тому
This video is part of an online module for my course Basic Econometric at University of Gothenburg, Sweden.
IV regression lecture 1: Intuition of IV regression / IV with a binary instrument
Переглядів 4 тис.4 роки тому
This video is part of an online module for my course Basic Econometric at University of Gothenburg, Sweden.
Endogeneity lecture 5: Demand estimation and endogeneity of price.
Переглядів 3,6 тис.4 роки тому
Endogeneity lecture 5: Demand estimation and endogeneity of price.
Endogeneity lecture 3: Binary regressor and intuition for omitted variable bias.
Переглядів 4,6 тис.4 роки тому
Endogeneity lecture 3: Binary regressor and intuition for omitted variable bias.
Endogeneity lecture 4: Measurement error and attenuation bias.
Переглядів 9 тис.4 роки тому
Endogeneity lecture 4: Measurement error and attenuation bias.
Endogeneity lecture 2: Omitted variable bias.
Переглядів 15 тис.4 роки тому
Endogeneity lecture 2: Omitted variable bias.
Endogeneity lecture 1: What is an endogeneity problem?
Переглядів 39 тис.4 роки тому
Endogeneity lecture 1: What is an endogeneity problem?
Panel data lecture 2: First-difference transformation in Stata
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Panel data lecture 2: First-difference transformation in Stata
Panel data lecture 1: Fixed effect and first-difference transformation
Переглядів 2,5 тис.4 роки тому
Panel data lecture 1: Fixed effect and first-difference transformation
Time series lecture 2: Stationarity and weak time dependence
Переглядів 1,7 тис.4 роки тому
Time series lecture 2: Stationarity and weak time dependence
Time series lecture 1: Time series data vs cross-sectional data
Переглядів 4,7 тис.4 роки тому
Time series lecture 1: Time series data vs cross-sectional data
Prediction lecture 5: Shrinkage and model averaging
Переглядів 1,1 тис.4 роки тому
Prediction lecture 5: Shrinkage and model averaging
Prediction lecture 4: Training error and test error / Over- and underfitting
Переглядів 1,9 тис.4 роки тому
Prediction lecture 4: Training error and test error / Over- and underfitting
Prediction lecture 3: Prediction error and bias-variance trade-off
Переглядів 2 тис.4 роки тому
Prediction lecture 3: Prediction error and bias-variance trade-off
Prediction lecture 2: Approximating a conditional expectation
Переглядів 1,1 тис.4 роки тому
Prediction lecture 2: Approximating a conditional expectation
Prediction lecture 1: Prediction vs causal inference
Переглядів 3 тис.4 роки тому
Prediction lecture 1: Prediction vs causal inference
Canvas hack: add/link to existing page in two seconds
Переглядів 60 тис.4 роки тому
Canvas hack: add/link to existing page in two seconds
Prof please come back to your UA-cam channel. Best Video. never understimate channels with short views. This video is mine gold <3 Thank you Professor Metrics
Thanks, I don't really get the FOC stuff at the end though
Can you share any references on how to estimate the demand curve using this method. I haven't been able to wrap my head around it.
great explanation
Thanks for the very helpful video! Given that the source of randomness is different for cross-sectional and time series regressions, may I ask how one has to think about panel data?
Extremely clear explanation, thank you!
Goat 🐐
It is way easier to use conditioning on Z and then taking expectation wrt Z.
"lets just ignore the dad"
so so helpful. gratitude & thanks.
Great explanation. Do you think the the w2,w3 are confounders? And the mechanism you explained about comparing wages for different majors is identifying counterfactuals? So basically the essence is that we identify confounders and then account for the outcome for each confounder which is a counterfactual.
thank you!
Thank you this tutorial is very helpful as a Business student.
Hi how can I choose the instrument variable .
I watched all the 5 lectures on Endogeneity. Excellent examples and explanations. Thank you.
At 25:00 it is supposed to be divided by var(Z1), not cov(Z1).
The covariance of z1 on itself is its variance, so not technically incorrect.
Thanks so much
Your content is so touching
thanks u explained this way better than my professor
Thank you for sharing this video! Great explanation on causal inference!
hi, can i please know is there any circumstances where this type of bias does not matter?
snap dr de ballen van maat
Thank you
Why is Var(X1*) = Var(X1) + Var(W) ? Did you show this in the video before proclaiming it at 19:55?
It comes from the formula for the variance of the sum of two random variables Var(X+Y) = Var(X) + 2Cov(X,Y) + Var(Y) X1*=X+W Var(X1*)= Var(X1+W), applying the formula above, Var(X1*)= Var(X1) + 2Cov(X1,W) + Var(W) given that Cov(X1,W)=0, because assumption-2 (6:54), i.e., no systematic measurement error, then Var(X1*) = Var(X1) + Var(W)
same question, did you figure it out?
@@youshengtang3997 simply Var(x*)=Var(x+w)=var(x)+var(w)+2Cov(x,w), which equals to var(x)+var(w) since Cov(x,w) equals to zero by assumption. However, I cant prove that Cov(x,w)=var(w) any good?
@youshengtang3997 simply Var(x*)=Var(x+w)=var(x)+var(w)+2Cov(x,w), which equals to var(x)+var(w) since Cov(x,w) equals to zero by assumption. However, I cant prove that Cov(x,w)=var(w) any good?
Thank you very much, This helps a lot
the legend is back. Thank you very much for the videos. Keep them coming. Thank you!
May I ask why can you write the covariance over variance term as the difference between two sample means?
excellent display of the intuitions behind IV
Excellent explanations. Thank you so much!
Great stuff, more panel data videos please. Something on dynamic panel would be great!
You’re the best, great video to get at least 50% from the exam
Too fast, if you can be slow about it, not everyone is a fast learner
Your videos help me so much thank you
very good video and thank you...
this clears up a lot!
nice
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The most genuine hacker I ever come across with is *hypertech23* on I G he did mine successfully,✅🇺🇸🇺🇸
The most genuine hacker I ever come across with is *hypertech23* on I G he did mine successfully,✅🇺🇸🇺🇸
what if we have more years? how do we manage that situation? thank you for the video
This video is very clearly organized. Suitable for beginners!
Ohh, you saved my life. Thank you for such helpful videos.
WTF a-hole this is the worst video on TB. Congrats.
I'm out bruh, can't find what I'm looking for. Finna fail this midterm quiz. Good luck to anyone who's looking for answers lmao
bro if you REALLY wanna "hack" Canvas, try phishing your teacher's password. Look up on UA-cam how to create a phishing site and if your teacher isn't that computer savvy, you could probably trick her into giving you their password. Good luck.
lmao here's a good video ua-cam.com/video/u9dBGWVwMMA/v-deo.html
@@ELCARNICEROJAGUAR Can you do it for me?
Thanks for all the explanations. Keep your work dear Professor
Thank You very much. Can you also pse explain how do we solve Dummy variable trap with similar explanation
bruh
You look handsome in this one!
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I Was recommended to greatspyzie@gmail. Com by a classmate and I consulted them with just a tiny hope and they did a lot and Assisted me in upgrading my scores