Unit Root, Stochastic Trend, Random Walk, Dicky-Fuller test in Time Series

In this video you will learn about Unit roots and how you would detect them in Time Series data. Random stochastic trend is the reason why many time series data exhibit unit root. This is found when the time series data is random walk
Stationarity & Non Stationary series
Deterministic & Stochastic trend
Random Walk
Unit root test
Dicky-Fuller test for unit root
For courses on Credit risk modelling, Market Risk Analytics, Marketing Analytics, Supply chain Analytics and Data Science/ML projects contact [e-mail]
For Study Packs : analyticuniversity.com/
Complete Data Science Course : bit.ly/34Sucmb
Access All Coursera Plus courses @ $400 : bit.ly/2ZL51Dd
Discounted courses on Udemy (for $11): bit.ly/2LYU6hp
Free access to Skillshare: bit.ly/2thklJu
Coursera :
Data Science : bit.ly/37nABr6
Data Science Python : bit.ly/2ZK5oMm
Recommended Data Science Books on Amazon :
Python for Data Science: geni.us/PythonDataScience
R for Data Science : geni.us/DataScienceR
Machine Learning using Tensorflow: geni.us/MLinTensorflow
Data Science from Scratch: geni.us/DataSciencefromScratch
Python programming: geni.us/LearnPython
Artificial Inteligence: geni.us/LearnAI
Data Vizualization : geni.us/DataViz
20% discounts on below live courses : use coupon UA-cam20
Data Science Live Training :
AI and Tensorflow: bit.ly/2tOnOzA
Python : bit.ly/2QkH1QQ

[@panshulrastogi1187]

Extremely helpful, one of the clearest explanations I've come across. Thank you!

[@abhinavsattiraju9718]

Best explanation on youtube! Tried very hard to find one, thank you!

[@ShamsherAlam-xs3bo]

Sir thank you very much, this video is very valuable for me and make it easy for me to understand this concept

[@shikhasingh8404]

You made this topic very easy Thnx sir.

[@patapingping]

TKS for clear presentation . appreciate bro !!

[@volkanky]

I have a question please help me ; I have a export data but ı reach the trend stationary process, so can I use this data for VAR analysis? how can I transform the trend stationary process to sationary process

[@AnalyticsUniversity]

Complete Data Science Course : bit.ly/34Sucmb
Data Science Books on Amazon :
Python Data Science : amzn.to/2Qg6g8m
Business ANalytics : amzn.to/2F7RhGT
STatistics : amzn.to/2ZGcSjb
Statistical Leanring : amzn.to/2ZHV6fn
Python : amzn.to/2u0uKJR
Audio books : amzn.to/2SSynMD
Coursera :
Data Science : bit.ly/37nABr6
Data Science Python : bit.ly/2ZK5oMm
Discounted courses on Udemy (for $11): bit.ly/2LYU6hp
Udacity Nanodegree:
Data Science : bit.ly/39IzAfc
Machine Learning : bit.ly/2sOinRb
Free access to Skillshare: bit.ly/2thklJu
20$ discounts on below LIVE courses : use coupon UA-cam20
Data Science Live Training :
AI and Tensorflow: bit.ly/2tOnOzA
Python : bit.ly/2QkH1QQ
Data Analytics : bit.ly/2PR4eez
Data Science : bit.ly/2QhxmdR
SAS : bit.ly/2Mpx83m
Big Data Training:
Hadoop : bit.ly/2sgHWdb
Splunk : bit.ly/2Ms0A8

[@jhontreyesalbarracin9479]

Thank you, great explanation. It follows a clear structure and well-linked the concepts together. It worked really good for me as a review to reinforce from my readings.

[@DewanggaPrabowo]

how we choose the method for unit root test? adf, pp, and so on

[@PierLim]

Thank you for this. Just a quick question - At 12:14, why does Yt-1 + Yt-2 + .... converge into Y0? Isn't it adding up?

[@cityuuniversity1365]

Your not adding up but replacing Yt-i with (c + Yt-i-1 + a_t-i) so in the end you will have Yt-t which is Y_0

[@getachewguadie9541]

thank you very much

[@stranglesensei]

gracias profe

[@khaledmustafa7341]

Hello, I have a question, when I manually find the Dickey Fuller statistic value, the statistic value is very slightly different from the value generated from the Eviews program, although I use the same data, what is the reason?,, I mean the normal Dickey Fuller test, not the developer

[@TheEarthenPotIlasengupta]

delta t = beta , right? and exp (delta t ) = 0 , right ? please clarify . Else there is a confusion @7.20

[@ashishtiwari1912]

Thank you very much. The video was very helpful

[@TJAcademyofficial]

These video will help you to learn the concept. Subscribe TJ Academy
ua-cam.com/channels/Q7Cbm57341QKdgZ_fTDGvw.htmlvideos
English (with EViews): ua-cam.com/video/iuDMgV5dqv4/v-deo.html
Urdu/Hindi: ua-cam.com/video/d3Uy1p-DaOM/v-deo.html (1/2)
ua-cam.com/video/CaHcxLG0mH4/v-deo.html (2/2)

[@HariniDesai]

love this

[@hamismiraji1894]

this is very good

[@nikunjgattani999]

Very helpful

[@user-lh1es3zk3i]

there is a mistake in DF test explanation (slide starts at 18:49). not (1-phi) but (phi-1). otherwise you'd get wrong hypothesis testing results

[@berke-ozgen]

Thank you Sir! Really good explanation but we did not mention variance stationarity, it is also one of the reasons of nonstationarity. If you made a video about it, please let me watch.

[@Roland-hm2vt]

what did he explain at 2:30 if not variance stationarity?

[@serviosilver9440]

Very good explanation. Thank you

[@Rong30B]

adds are disturbing the flow & more importantly concentration

[@adityamahajan1238]

What is phi hat and se (phi hat) at the end of the video?

[@DewanggaPrabowo]

phi hat is the estimator of phi or we can say the prediction/estimate, and I don't know about s.e.

[@sheilaalsy]

@@DewanggaPrabowo standard error probably?

[@DewanggaPrabowo]

@@sheilaalsy maybe

[@zoozolplexOne]

Cool!!!

[@pulltheskymusicgroup4475]

🇹🇿

[@mintaowei2686]

way too many ads bro

[@quant-prep2843]

gero ??? it is zero maan!

[@lucasaugustodeoliveirasilv7995]

understood anything that u said. terrible english