- Видео 75
- Просмотров 30 956
An Cao
Добавлен 10 июл 2010
Видео
Tobit-2 model - motivation, formulation, estimation
Просмотров 1683 года назад
Tobit-2 model - motivation, formulation, estimation
Tobit I introduction & marginal effects
Просмотров 7253 года назад
Tobit I introduction & marginal effects
Maximum Likelihood Estimation - Linear regression
Просмотров 3,1 тыс.3 года назад
Maximum Likelihood Estimation - Linear regression
Sampling properties - Variance estimate
Просмотров 873 года назад
Sampling properties - Variance estimate
part 3 - Gauss Markov & OLS estimator
Просмотров 1113 года назад
part 3 - Gauss Markov & OLS estimator
3. Long-run producer's surplus (Markets week 5)
Просмотров 2133 года назад
3. Long-run producer's surplus (Markets week 5)
1.2. Long run analysis - part 2 (Markets week 5)
Просмотров 1523 года назад
1.2. Long run analysis - part 2 (Markets week 5)
1.1. Long run analysis - part 1 (Markets week 5)
Просмотров 1863 года назад
1.1. Long run analysis - part 1 (Markets week 5)
3.2. Comparative statics (Markets week 4)
Просмотров 2133 года назад
3.2. Comparative statics (Markets week 4)
3.1. Demand and supply shifts (Markets week 4)
Просмотров 1483 года назад
3.1. Demand and supply shifts (Markets week 4)
2. Short-run equilibrium (Markets week 4)
Просмотров 2053 года назад
2. Short-run equilibrium (Markets week 4)
1. Very short-run and short-run supply (Markets week 4)
Просмотров 1713 года назад
1. Very short-run and short-run supply (Markets week 4)
1.3. Bayesian games with continuum of action (Markets week 3)
Просмотров 2233 года назад
1.3. Bayesian games with continuum of action (Markets week 3)
1.2. Bayesian-Nash equilibrium (Markets week 3)
Просмотров 4843 года назад
1.2. Bayesian-Nash equilibrium (Markets week 3)
You explained it so clearly, thanks!
Thank you so much, wish you the best as well.
Thank you!
Nice !
super helpful, thank you!
It seems P(yi>0|hi=1) measures something like intention, which would be hard to support with behavioral data. I can measure the probability of the action, and the magnitude, but intention would be difficult. Good and important topic.
Thank you so much ❤
very clear and easy to follow, got the point, thanks so much!!
E cảm ơn a
Thank you so much madam, may God bless you.
This is by far and away the best explanation of the vast breadth of limited dependent variable models I have ever found. I have been working on a paper where the outcome is a limited dependent variable for almost a year. While my choice of running both Heckman and 2PM models was correct all along, this is the most thorough explanation of when to use what model I have ever seen. Even getting into the likelihood functions. I will recommend everyone I know struggling with this. Well done!
THANK YOU 🙂 YOURVIDEOS HELPS ME TO UNDERSTAND THE BASICS WHICH I'M NOT ABLE TO UNDERSTAND THANKS
Very good 👍
Thank You!!!
gebtoshal ? :)
23:39 typo: the last term on the right-hand side should be +p2c2, not p2c1
Thank you so much for detail explanation, wish u all the best
Thanks Ma'am, I really appreciate to your teaching 🥰🥰 From Odisha, India
❤️❤️ beautiful
Great
Hello Ms. An Cao, I enjoyed your presentation above. As a beginner on this topic, I find it quite insightful. Could you please share the slides you keep referring to in this lecture? Again thank you for your good work
Hi there, here is the link to the slides: uni-bonn.sciebo.de/s/Pkeq6MT6jcdvLsv
@@quean0101 Thank you. I really appreciate
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Very educative and enlightening. I had been looking for something like this....Please upload more.
Good teacher but no views. I feel bad.
30:46 should be 70 percent (the average score so far), so that it makes sense that when then next assignment you get 80 percent, your average score will be improved!
7:20 I goofed. So it's actually "eq" in the textbook. That was just my typo. Sorry!
Mistake: from 26:31 til 30:15, the maximized MP should be 1,2000,000 and not 120k. I missed one zero at the end. Similar mistake for the maximized AP, and the MP when l = 30 8 (from 31:54 till end). They are both 900k, not 90k. However, the nature of the relationship among productivities remain unaffected.
Well done, An! Miss you here!!! Keep up...
great work !!! An