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@@vd9570 yup! I am at university of Arizona

@@justinjarvis5681 Awesome!

@@vd9570 woot good job 👍

Great question! I assume you're talking about 7:30 in the video. The key is that PRICES are changing. The change in P does in fact increase consumption. And we model this by moving down along the AD curve. Because of the way it is constructed, a change is price ALWAYS is modeled by a movement along the AD curve. Anything else that changes, except price, shifts the curve itself.

nice. Good luck :)

Wonderful! Glad to help

Great question. When I say "increase reserves", I do NOT mean that the bank pulls money out of circulation (from loans, deposits, etc.) to store it in reserves. That WOULD DECREASE the money supply. Instead, what I mean here is that the Fed is paying the banks for the bonds they just bought from the banks. They pay by inserting "printed" money into their reserve accounts. Thus, the banks feel richer, and then in turn make more loans, and increase the monetary supply.

Hello there lol 😂

You're very welcome!

Awesome and good luck!!

Woot woot! Congrats. That is awesome :)

Good luck! You can do it!!

thank you!! these are super helpful@@justinjarvis5681

I GOT A 96% ON MY MIDTERM!! Thank you so much!! @@justinjarvis5681

Awesome! Thanks

Same here

Thank you!!

I think the math looks ok at 9:46. What were your other two questions??

Hi! This doesn't make sense so me, so I won't be able to do it! Sorry about that.

@@justinjarvis5681 not a problem, thanks for the awesome content and your efforts.

Awesome! I'm glad to hear these are helpful. Good luck on your summer course!

@Justin Jarvis I thought you'd like to know I finished strong with an A-. I wouldn't have done this well if it weren't for your videos and your knack for teaching. Thanks again sir, hope you have an amazing Summer ☀️

@@Tommynyp33 woot that's great. I hope you also learned to love some econ as well 😎

You know, I don't think I have it anymore. Sorry 😔

You sure could compare between treatment 1 and treatment 2 (no control) in theory. The design will still have internal validity. However, the problem with this is that it will likely lack external validity. This means that you will be picking up the differences between treatment1 and treatment 2....you won't be picking up the effect of treatment 1 all by itself. Usually, in these sorts of situations, where it is unethical to not offer treatment, what is done is a staggered implementation approach. Apply the treatment in 2-3 waves, perhaps 1 year apart (depending on what the treatment is), and then use the later waves as control groups for the earlier waves. Make sure that subjects are assigned randomly to the waves, however.

@Justin Jarvis thanks a lot for your valuable answer. Regarding the external validity, I designed a pre- & post test for each treatment , starting from an equal point as both groups comparable at baseline and then allocating subjects to 2 different treatments and then bivaruate statistics taking in my consideration evaluation for pre- test and post-test for both groups and for each and comparing the difference (pre value - post value) between both groups to figurout the improvement difference. Is a pre-post- test for each group not enough to evaluate the effect of each treatment ? As the durability of study is time (4 months apart), limited

@@fadhilfp6 Hello again. I might be misunderstanding your question. Since you were discussing the allocation of individuals to certain groups, I had assumed you were talking about an RCT-like design. Are you talking instead about a DinD design? The Differences in Differences technique is usually used when we CANNOT assign individuals to groups; that is, for some reason the data we have is generated by individuals who voluntarily signed up for the program. If we are able to assign, the RCT design will be cleaner, and as I said before, the later waves can be used as the control for the earlier waves. However, if you are thinking about a DinD design, with individuals assigned to two different treatment groups, this can be done (since you have pre and post data for each group). However, the alpha (coefficient of interest) you will calculate will not be the effectiveness of a treatment, rather it will be the (marginal) casual difference of treatment 1 as compared to treatment 2. This might yield useful data, depending on the context, but it will not yield information as to the effectiveness of treatment 1 (or treatment 2 for that matter) over the counterfactual. This is because there is no counterfactual generated.

@Justin Jarvis I am so thankful for your answer, Sir. I appreciated it.