Autoregressive Order one process introduction and example

[@tazzron]

It's come to the point where I've stopped looking at lecture slides and exclusively come here to learn.

[@boubougeorge1777]

That is so right 😂

[@umiddey8714]

It's 2020 and I do the same.

[@thhbere]

2021, same

[@donghoshin3460]

2022, same

[@Flush333]

2023, same

[@yaweli2968]

It’s a crime you are teaching for free and my professor is getting paid when he can’t teach. Thanks for what you do. Much appreciated.

[@samkim6933]

You are a genius~!! made me intuitively understand the material when no other books or lecturers couldn't...

[@attesaarinen4311]

Ben you da MVP

[@_kim7271]

유가쇼크 후 가격이 원상복귀하는 과정이라니... 설명 미쳤다... 이게 ㄹㅇ 계량경제학이지...

[@Eizengoldt]

Hi cutie

[@lizrael]

Thank you so much! You have helped me understand more than any of my profs ever had.

[@wanjadouglas3058]

Very nice example Lambert

[@mehmethikmet3439]

Ben you are a fucking legend mate.

[@changlinlei7600]

Thank you sir for the clear explanation

[@Garet43]

This is an excellent explanation of the AR1 effect! Thank you!!

[@Highlyk]

Great vids Ben. Incredibly useful.

[@Alex-uo1ef]

Great video! Helped me a lot for understanding the AR Model in the context of signal processing !

[@amengioio]

Thanks for making this informative video. Should the formula of the oil price example just be, "Oilp[t] = .5*Oilp[t-1] + eps[t]" instead of "delta(Oilp[t]) = .5*delta(Oilp[t-1]) + eps[t]"? Because if I expand the formula, it seems to be a AR(2) process.

[@tusharbharati5151]

+Ryan Zhang I agree. +Ben Lambert: Please take a look

[@tommarty234]

+Ryan Zhang keep in mind that delta(Oilp[t]) is the random variable being considered, not Oilp[t] - so the AR(1) model's recursive order to Oilp[t], or any other variable, is not relevant in this context. Also I think he has defined delta(Oilp[t]) as the delta from some constant, as opposed to delta between the last two values, otherwise his plot would oscillate. I realise this question is from over a year ago, Im just replying in case anyone else has the same question.

[@pedromrfernandes]

i guess with the formula in deltas the example is correct. however, with the formula in levels, the price would have to climb by an extra $5 in t+1, reaching +15$ from the period before the shock, then reaching a peak right after the change vanish. can anyone confirm?

[@namukayasandra5633]

Thank you.I have been having trouble understanding y applied econometrics module

[@ΓιωργοςΓαλάρης-ψ3β]

Thanks a lot for these videos! Very helpfull!!

[@ecerulm]

This video seems to be part of series, I would be nice to include a link to the series/playlist (if there is one) in the description or better yet as overlay annotation in the video itself.

[@iamdumbmatt]

very intuitive explanation sir

[@patrickgold3616]

why is the oil price suddenly called x and not y? In previous videos, the dependent variable was always y. X here makes it seem like we are discussing an independent variable?

[@Kavafy]

It's both

[@aristalyakusuma1]

Thank you for the video. It really helps me!

[@zarulkhaliff5107]

Ben, can I use cross-section data for forecasting using AR(1)? Thanks

[@urabi3tube]

I have a question why do use AR or MA or ARMA model in other words why we abandon multiple regression, to be more clear I want to know why we decide to take the variable's lag as an explanatory variable?????and when also can I decide which model to build by looking to variables? please can u help me we this issue? thanks :)

[@ignaciosacristanlopez-brav684]

Wxcellent video like always

[@gulzameenbaloch9339]

Thank you so much

[@yinyuebu]

Love this!

[@SpartacanUsuals]

Hi, many thanks for your message! Best of luck with your studies. Thanks, Ben

[@shairozsohail1059]

Great job!

[@andrewseeran8811]

Is this a continuous "version" of a markov process?

[@anishsharma3071]

Hello Ben, These videos are proving themselves very helpful. Thanks for the same. I had few questions 1. i have read this definition somewhere - "a moving average process is a linear regression of the current values of a time series against both the current and previous unobserved white noise error terms, which are random shocks". what do we mean byy saying an error term as unobserved?. 2. how we can exactly quantify unobserved white noise error term in our MA/ AR model.(have we regressed the time series and then have compared actual and modeled) ? Please clarify, i will be thankful.

[@dimasmukhlas3952]

Thanks man! very much help!

[@ciceklale1825]

thank you for this video

[@KorawichKavee]

What do you mean by iid?

[@mech_builder7998]

en.wikipedia.org/wiki/Independent_and_identically_distributed_random_variables

[@quincyreinevergara8822]

:o

[@iremcifci5596]

your voice is disturbing for me i cannot focus

[@RoylanMartinez]

there are more videos out there, no one is asking you to watch this video

[@prateekratna]

this sucks

[@silentstorm718]

Be respectful