Richard Gallenstein
Richard Gallenstein
  • Видео 78
  • Просмотров 209 238
Minimum Detectable Effect Calculation
Minimum Detectable Effect Calculation
Просмотров: 9 940

Видео

Lecture 13 - R Demo
Просмотров 6333 года назад
This video provides a demo for Lecture 13: Panel Data Models in R.
Lecture 12 - R Demo
Просмотров 4713 года назад
This video provides a demo for Lecture 12: Instrumental Variable Models in R.
Lecture 11 - R Demo
Просмотров 2,1 тыс.3 года назад
This video provides a demo for Lecture 11: Propensity Score Matching in R.
Lecture 10 - R Demo
Просмотров 3003 года назад
This video provides a demo for Lecture 10: Randomization in R.
R Tutorial
Просмотров 1,1 тыс.3 года назад
This video provides a very brief intro to how to use the R interface.
Lecture 8 - R Demo
Просмотров 2983 года назад
This video provides a demo for Lecture 8: Binary Dependent Variable Models in R.
Lecture 14 - R Demo
Просмотров 9143 года назад
This video provides a demo for Lecture 14: Difference in Differences Model in R.
Lecture 6 - R Demo
Просмотров 2103 года назад
This video provides a demo for Lecture 6: Multicolinearity and Goodness of Fit in R.
Lecture 7 - R Demo
Просмотров 2113 года назад
This video provides a demo for Lecture 7: Heteroskedasticity in R.
Lecture 15 - R Demo
Просмотров 7423 года назад
This video provides a demo for Lecture 15: Regression Discontinuity in R.
Lecture 4 - R Demo
Просмотров 3633 года назад
This video provides a demo for Lecture 4: Multivariate Regression in R.
Lecture 3 - R Demo
Просмотров 3443 года назад
This video provides a demo for Lecture 3: Simple Linear Regression in R.
Lecture 5 - R Demo
Просмотров 2513 года назад
This video provides a demo for Lecture 5: Omitted Variable Bias in R.
Lecture 1 - R Demo
Просмотров 9003 года назад
This video provides a demo for Lecture 1: Descriptive Statistics in R.
Lecture 2 - R Demo
Просмотров 4123 года назад
Lecture 2 - R Demo
Lecture 15 Regression Discontinuity
Просмотров 15 тыс.3 года назад
Lecture 15 Regression Discontinuity
Lecture 14 Difference in Differences
Просмотров 23 тыс.3 года назад
Lecture 14 Difference in Differences
Lecture 13 Panel Data
Просмотров 10 тыс.3 года назад
Lecture 13 Panel Data
Lecture 12 Natural Experiment and IV
Просмотров 4,8 тыс.3 года назад
Lecture 12 Natural Experiment and IV
Lecture 11 Propensity Score Matching
Просмотров 32 тыс.3 года назад
Lecture 11 Propensity Score Matching
Lecture 10 - Randomization
Просмотров 2,5 тыс.3 года назад
Lecture 10 - Randomization
Lecture 8 Binary Dependent Variable Models
Просмотров 4 тыс.3 года назад
Lecture 8 Binary Dependent Variable Models
Practical Implementation
Просмотров 3715 лет назад
Practical Implementation
Intregral Approach to RCTs
Просмотров 4445 лет назад
Intregral Approach to RCTs
Mechanism Experiments1
Просмотров 4765 лет назад
Mechanism Experiments1
Market Power Monopoly
Просмотров 1265 лет назад
Market Power Monopoly
Market Power Monopsony
Просмотров 2535 лет назад
Market Power Monopsony
Equilibrium and Efficiancy 4 Solving for an Equilibrium
Просмотров 2065 лет назад
Equilibrium and Efficiancy 4 Solving for an Equilibrium
Equilibrium and Efficiancy 3 Welfare Theorems and Prices
Просмотров 3435 лет назад
Equilibrium and Efficiancy 3 Welfare Theorems and Prices

Комментарии

  • @Ziashefaie
    @Ziashefaie 5 дней назад

    A wonderful lecture. Thank you for this.

  • @first_derivative
    @first_derivative Месяц назад

    Thnak you so much, very helpful! 🧡

  • @samuelnzekwe7696
    @samuelnzekwe7696 Месяц назад

    I am watching in 2024, and everytime you mentioned that this is just another method in a series of other methods you had already tackled in this tutorial, I pausee the video to be sure that youtube's algorithm was doing what I hoped it should do(bring up the full course catalog on my right pannel-it did!). Thanks a lot for the videos, they were easy to follow through and your use of examples to clarify concepts even made the whole experience more intuitive. Never have to cram😂

  • @Iluahmad
    @Iluahmad 2 месяца назад

    Good Day Sir, this is very insightful, please do you have a video on how to implement this on either Stata or R??

    • @richardgallenstein3878
      @richardgallenstein3878 2 месяца назад

      Check out my Applied Econometrics playlist on my RUclips channel. You will find a whole econometrics course that includes this content using both R and STATA.

    • @Iluahmad
      @Iluahmad 2 месяца назад

      @@richardgallenstein3878 Thanks very Much, I’ll check right away, God Bless 👍👍

  • @schafer4935
    @schafer4935 2 месяца назад

    Thank you for the great quality video. I have a question: Considering your equation Income = ß0 + ß1*Dodoma + ß2*Year + ß3*Year*Dodoma + E. Which part do I have to test via F-Test to valid that the parallel trend assumption holds? Thank you again and very best regards.

  • @Ziyue-t8g
    @Ziyue-t8g 3 месяца назад

    really clear expression!!! Thank you so much:)

  • @jagjeetskhanuja8921
    @jagjeetskhanuja8921 3 месяца назад

    Brilliant class. This is one rare video, which made Panel Data Model understanding so clear. As a Econometrics student, i am tempted to view other previous videos to understand the subject better. Thanks

  • @soukinaomar6164
    @soukinaomar6164 3 месяца назад

    Hello Dr. Richard I have Questions, what's the best way to make analysis for panel Data is that R or Stata ?

  • @douzhang6453
    @douzhang6453 3 месяца назад

    Super clear. Thanks! The empirical example just showcases what makes a good and poor assumption. Also like the logics you demonstrated throughout your explanation.

  • @debanjandas7006
    @debanjandas7006 4 месяца назад

    I’m working on a project which we have proven to be Natural Experiment. Can you suggest some paper so we can strengthen the methodology?

  • @faijannabil9028
    @faijannabil9028 5 месяцев назад

    May I request you to cover endogenous switching regression model.

  • @shakeelahmed2932
    @shakeelahmed2932 5 месяцев назад

    Sir, why have you not taken all variables in validating assumption 2? Why you leave the variable - female?

  • @yanniskaragiannis3026
    @yanniskaragiannis3026 5 месяцев назад

    Nice. Clear. Simple. But, dude, do you seriously think 'criteria' is in the singular?!? 😂

  • @SouthernIg
    @SouthernIg 6 месяцев назад

    A great video with very clear explanation.

  • @farooqpetersaidi7784
    @farooqpetersaidi7784 6 месяцев назад

    Thanks❤

  • @MoretloBotipe
    @MoretloBotipe 6 месяцев назад

    Hi, your work is very helpful thank you.may you please make a video on quantile regression

  • @nizgottherizz
    @nizgottherizz 7 месяцев назад

    Such a clear explanation. Thank you.

  • @redaple3088
    @redaple3088 7 месяцев назад

    Where I can find that dataset wages_random? I want to replicate that code.

  • @SincerelyRoza
    @SincerelyRoza 8 месяцев назад

    Sir, God bless you.

  • @chukwuedooburota48
    @chukwuedooburota48 8 месяцев назад

    Thanks prof. Gallenstein for a very clear presentation

  • @ervinamunthe4797
    @ervinamunthe4797 8 месяцев назад

    very well explanation, thankyou Richard.

  • @123hgggmllvcc
    @123hgggmllvcc 8 месяцев назад

    Wow, really good lecture on DID, thank you very much!

  • @Dr_Shiny
    @Dr_Shiny 9 месяцев назад

    Respect and Love

  • @siakasoura4889
    @siakasoura4889 9 месяцев назад

    thank you very much for this beautiful video on randomization. you are really educational.

  • @adeboladaramola3838
    @adeboladaramola3838 9 месяцев назад

    From what I have learned DiD is not limited to panel data.

  • @supercool37
    @supercool37 9 месяцев назад

    I hope this is the the right video for calculating minimum detectable effect size if I see an observational study published in a paper and I am reviewing it for discussing in journal club? My main concern is not to jump to an erroneous conclusion of equivalence based on an underpowered observational study which did not even mention any power analysis. This misunderstanding has a potential for negatively impacting patient care. Is there an article and is there a calculator?

  • @michaelasare4987
    @michaelasare4987 9 месяцев назад

    Is the B1 the probability or the change in probability due to a one unit change in X.

  • @dikshaarya7074
    @dikshaarya7074 10 месяцев назад

    How to get the data sets?

  • @bodwiser100
    @bodwiser100 10 месяцев назад

    What if the reason that the treatment group was given the treatment (i.e, not random assignment) is correlated with the treatment group's trend? In other words, what if assignment of treatment was done non-randomly precisely because a certain group in the population was identified to have a different trend than other groups? Is there any economic/statistical check for that?

  • @JingwenLiu-s2f
    @JingwenLiu-s2f 11 месяцев назад

    Thanks a lot for your lectures! They are very clear and easy to understand! It helped me a lot.

  • @benardkiplimo3508
    @benardkiplimo3508 11 месяцев назад

    Thank you for the great lecture Prof! It couldn't have come at a better time

  • @adityarazpokhrel7626
    @adityarazpokhrel7626 11 месяцев назад

    Wonderful. This much clearer picture of RDD, I haven't received from anyone else. Keep posting. Love from NEPAL. 😊

  • @adityarazpokhrel7626
    @adityarazpokhrel7626 11 месяцев назад

    Thank you very much. Should we try out some of the Placebo tests to validate the results ?

  • @TÔMTIÊNYÊN
    @TÔMTIÊNYÊN 11 месяцев назад

    How can we get the data set in this example of Stata Professor ?

  • @mg24ification
    @mg24ification 11 месяцев назад

    Thank you so much, very helpful! Regarding the subgroup analysis: if the interacton coefficient is not significant, would that mean that the subgroup are different in the sample at hand, but that there is no statistical significance for it? Thanks in advance for the clarification :)

  • @courage___
    @courage___ 11 месяцев назад

    So precise, so clear, easily understandable. This is the best DID video. Thank you!

  • @zethayn
    @zethayn Год назад

    Excellent explanation, thank you!

  • @valentinvigouroux3419
    @valentinvigouroux3419 Год назад

    very clear thank you very much. Helped a great deal

  • @berke-ozgen
    @berke-ozgen Год назад

    Best explanation series in the RUclips I have watched so far. Thanks Professor for each video on this serie. Worth to note, the videos are really underrated.

  • @dagnachewgetnet9202
    @dagnachewgetnet9202 Год назад

    Thank you professor for your nice and detailed presentations! If we are using Probit model and there will be a hetroskedasticity , can we report the marginal effect coefficients or totally leave the model use only the results of LPM? Thank you!

  • @noor-hj4fn
    @noor-hj4fn Год назад

    Very concise, thank you

  • @linhthiphuongnguyen6792
    @linhthiphuongnguyen6792 Год назад

    Professor, thank you so much for a very clear explanation of DID. I was having a hard time to understand this method but through your video, it helps me to understand it clearly.

  • @Ali.Solt1
    @Ali.Solt1 Год назад

    Great thanks

  • @WENQINGSHE
    @WENQINGSHE Год назад

    Thank you so much professor, it helps me a lot!

  • @josephndagijimana6610
    @josephndagijimana6610 Год назад

    This is great !

  • @Zane_Zaminsky
    @Zane_Zaminsky Год назад

    Nice video. First part of video: one column. Use runiformint(0,1) Use it again in part two, within vaccess.

  • @뷰신-k3g
    @뷰신-k3g Год назад

    Can you also make demo video for coarsened exact matching with stata code?

  • @Milton_Friedmanite
    @Milton_Friedmanite Год назад

    Been looking for a playlist like this, love it!

  • @jpierre2040
    @jpierre2040 Год назад

    Wow! What an elaborate way of explaining the DiD concept. This is the best lecture so far. Thanks so much sir, i have learnt alot. kindly help me understand, incase there are three groups (treated, control and pure control) in an RCT experiment, how do you estimate the DiD?

  • @Skandalos
    @Skandalos Год назад

    16:50 When you write L(x*,y*,lambda*) it isnt technically a function but a function value. It becomes a function when written as L(z, alpha, beta)